The Red (Team) Analysis Weekly – 4 June 2020

This is the 4 June 2020 issue of our weekly scan for political and geopolitical risks (open access).

Using horizon scanning, each week, we collect weak – and less weak – signals. These point to new, emerging, escalating or stabilising problems. As a result, they indicate how trends or dynamics evolve.

The 4 June 2020 scan→

Horizon scanning, weak signals and biases

We call signals weak, because it is still difficult to discern them among a vast array of events. However, our biases often alter our capacity to measure the strength of the signal. As a result, the perception of strength will vary according to the awareness of the actor. At worst, biases may be so strong that they completely block the very identification of the signal.

In the field of strategic foresight and warning, risk management and future studies, it is the job of good analysts to scan the horizon. As a result, they can perceive signals. Analysts then evaluate the strength of these signals according to specific risks and dynamics. Finally, they deliver their findings to users. These users can be other analysts, officers or decision-makers.

You can read a more detailed explanation in one of our cornerstone articles: Horizon Scanning and Monitoring for Warning: Definition and Practice.

The sections of the scan

Each section of the scan focuses on signals related to a specific theme:

  • world (international politics and geopolitics);
  • economy;
  • science including AI, QIS, technology and weapons, ;
  • analysis, strategy and futures;
  • the Covid-19 pandemic;
  • energy and environment.

However, in a complex world, categories are merely a convenient way to present information, when facts and events interact across boundaries.

The information collected (crowdsourced) does not mean endorsement.

Featured image: Milky Way above SPECULOOS / The Search for habitable Planets – EClipsing ULtra-cOOl Stars (SPECULOOS) is searching for Earth-like planets around tiny, dim stars in front of a panorama of the Milky Way. Credit: ESO/P. Horálek.

Dynamics of contagion and the COVID-19 Second Wave

This article, using scientific knowledge, looks at the COVID-19 dynamics of contagion to identify ideal measures that should be taken to stop contagion. These ideal measures, then, compared with real policies will allow assessing the potential for a second wave.

Our aim, for this series, is to find ways to improve how we foresee if, where and when a second wave or recurrent ones, could strike, and how lethal they could be. We assume the virus does not mutate and disappear. Here, we seek a way to evaluate the measures and policies countries and non-state actors take against the COVID-19 to estimate if they mitigate or not the risks of contagion and thus of a second wave.

In other words, what we are trying to find out is how adequate the measures implemented are to control the contagion. This control is crucial if we do not want to see again infections and then severe cases rise exponentially and uncontrollably. This would mean a second wave with a return to lockdown.

To achieve our aim, we need to understand how the COVID-19 spreads, hence the various dynamics of contagion at work. Thus, we retrace the way contagion takes place, at individual level, in the case of the COVID-19 pandemic. To do so, we use and synthesise knowledge scientists accumulated since the start of the pandemic to date. As a result, we obtain an ideal benchmark against which measures and policies can be evaluated. From a policy-orientated perspective, we thus also obtain indicators for better monitoring of the situation on the ground and for steering policies.

We thus assess how efficient our net is. Ideally, we would also need to be able to determine how many cases can slip through our net. The more numerous the remaining undetected cases, the higher the likelihood to see a new dire wave, the closer that can take place in time and the more intense and dangerous the wave.

First, we look at the dynamic of infection through transmission and at incubation. This gives us crucial elements notably related to individual protective measures and to quarantines for individuals which appear not to have the COVID-19. Second, we identify possible cases of contagion, focusing mainly on contagion taking place outside the hospital track. In other words we look at the contagion that is more difficult to identify and control because it is not easily observable and collides with everyday life. We thus address pre-symptomatic contagion, asymptomatic contagion, contagion for mild COVID-19 disease and post recovery contagion. Finally, synthesising the knowledge gathered, we summarise the ideal measures that could be taken in a table to ease assessment (direct access to summary table). We give a more detailed example of what should be the ideal duration of quarantine for travels and of the risks entailed.

Infection, transmission and Incubation

To become infected, someone needs to receive a minimal dose of virus. Once this dose reaches “our respiratory tract, one or two cells will be infected and will be re-programmed to produce many new viruses within” a certain amount of time (Dr Michael Skinner, “Expert reaction to questions about COVID-19 and viral load“, ScienceMediaCentre, 26 March 2020). The new viruses infect in turn other cells, which, produce new viruses etc. As far as the COVID-19 is concerned, we do not know yet this minimal infectious dose.

Then, the amount of virus an infected individual produces is the viral load (Prof Jonathan Ball, Ibid.). Note that we do not know if, for the COVID-19, there is a link between high viral load and severity of illness (Marta Gaglia and Seema Lakdawala, “What we do and do not know about COVID-19’s infectious dose and viral load“, The Conversation, 14 April 2020).

Now two things happen, which do not always take place synchronously, but that are often considered together: infecting other people and developing symptoms and becoming ill. Here we focus mainly on the contagion aspect of the COVID-19, paying as much attention as possible to what happens outside hospitals.

Viral shedding, spreading the disease and contagion

Now, the infected person will also expel some of the virus that has replicated within her body in the environment through various means. This is known as viral shedding.

Once another person absorbs parts of this viral shedding and as soon as the minimal infectious dose is reached, the second person becomes infected and the process continues. Contagion has taken place.

Erin Bromage, Associate Professor of Biology, describes how this process can take place in a post that is very easy to read (“The Risks – Know Them – Avoid Them“, 6 May 2020). He points out that contamination may occur at once, or through absorption of many smaller doses of virus. Nonetheless, in that case, we do not know the exact process through which each dose of virus remains in the organism and for how long, if a small dose could become inactive or be expelled for example.

We know that the virus is transmitted through respiratory droplets as well as through contact with infected materials. However, recently American studies have shown that the virus could also be airborne, which other scientists still debate (e.g. Tanya Lewis, “How Coronavirus Spreads through the Air: What We Know So Far“, The Scientific American, 12 May 2020). Lewis explains that the difference between airborne contagion and contagion through respiratory droplets is thin, and depends actually on the size of the droplets (Ibid,). Airborne contagion “refers to transmission of a pathogen via aerosols—tiny respiratory droplets that can remain suspended in the air (known as droplet nuclei)—as opposed to larger droplets that fall to the ground within a few feet” (Ibid.).

As a result ventilation becomes an importent factor that must be considered as favouring contagion or, on the contrary, making infection more difficult (Ibid., Bromage, Ibid.). It may either help clear virus present in the air and on surfaces, or, on the contrary, move infectious viral elements elsewhere, where people may become infected… when they thought they respected social distancing. Bromage, for example, explains that infection may take place within an empty room that has been previously infected. He also highlights the dangers of air conditioning that may propagate virus throughout space.

Thus, Bromage stresses that the fundamental equation is “Successful Infection = Exposure to Virus x Time”, and that this equation is strongly impacted by ventilation, i.e. volume and flow of air (Ibid.).

Incubation

Usually, once infected, at one stage, symptoms may appear. As a result, people who are ill and have symptoms may withdraw from society, which diminish the risk to transmit the disease. This is even more so if the symptoms are strong enough to incapacitate the infected individual. Meanwhile, the patient also needs care.

The time between contamination and appearance of symptoms is called the incubation period. To date, a study reviewing 181 cases, estimates that “fewer than 2.5% of infected persons will show symptoms within 2.2 days (CI, 1.8 to 2.9 days)”, and 50% of people will have developed symptoms between 4.5 and 5.8 days after contamination (Stephen A. Lauer, MS, PhD et al., “The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application“, Annals of Internal Medicine, 5 May 2020). 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection (Ibid.). However, “these estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.”

Previously, Zhong et al., had estimated the longest incubation period to 24 day (Clinical characteristics of 2019 novel coronavirus infection in China, 6 February 2020, medRxiv). Meanwhile Chinese officials had reported a case with a longer incubation period of 27 days (Angela Betsaida B. Laguipo, “Coronavirus incubation period could be 27 days, longer than previously thought“, News Medical, 24 February 2020).

This appears to correspond to a little noticed fact: in April, China increased the length of its quarantine in Heilongjang from 14 days to 28 days (Reuters, “China’s Harbin orders 28-day quarantine after rise in imported cases“, 12 April 2020). The system of quarantine and their duration is however complex and diverse in China, and all arrival cities or regions do not use a 28 days length (see European Chamber, Travel Policies to and from Cities in China, 15 May 2020).

Yet, an illness does not always develop in such an easily observable way. We have other cases, which favour contagion, as happens with the COVID-19.

The COVID-19 and contagion

Pre-symptomatic contagion

If a person is infected and is contagious before to become symptomatic, then the virus may spread more. Indeed, as people have neither felt unwell nor, once the new disease has been identified, detected as infected, then they carry on with their lives. Meanwhile, they contaminate other people and materials.

This is the case with the SARS-CoV-2. He et al. found that 44% of secondary cases, despite strong diverse measures to suppress the pandemic, were infected by pre-symptomatic patients (“Temporal dynamics in viral shedding and transmissibility of COVID-19‘, 15 April 2020). They “inferred that infectiousness started from 2.3 days (95% CI, 0.8–3.0 days) before symptom onset and peaked at 0.7 days (95% CI, −0.2–2.0 days) before symptom onset”. As a result, they recommend that “the definition of contacts covers 2 to 3 days prior to symptom onset of the index case”.

Another more recent study, from India, considering 1251 individuals from the literature, assessed that 68,4% of infections resulted from pre-symptomatic individuals (Meher K Prakash, “Quantitative COVID-19 infectiousness estimate correlating with viral shedding and culturability suggests 68% pre-symptomatic transmissions“, medRxiv 2020.05.07.20094789).

However here, because patients are contagious before symptom onset, then, the problem is that scientists and people fighting against the pandemic need to work backwards. They work from the time of symptom onset, the first easily observable evidence they have of illness. But, once illness has started, then we are already up to three days late on the virus, if we consider He et al. findings, with the longest confidence interval, to be on the safe side.

Thus, during these three days, the virus has had time to propagate among the population. This explains the importance of testing and searching for contact cases, as a key way to fight against a pandemic. Testing and contact tracing is also an attempt to move from working backward to working forward, meanwhile anticipating and not anymore reacting to the virus.

Pre-symptomatic contagion combined with early incubation

Furthermore, let us combine pre-symptomatic contagion with knowledge on infection and incubation. We may estimate that if “fewer than 2.5%” show symptoms within 2.2 days”, knowing that infectiousness starts 2.3 days before symptoms onset, then “fewer than 2.5%” of infected people will be infectious quasi immediately, probably within hours. As a result, they will also have time to infect others extremely rapidly. Research looking for this exact phenomenon will need to confirm or falsify such findings.

Nonetheless, waiting for further research, safety and precaution demand that such cases and corresponding estimates be integrated within a framework for action. The quasi-instantaneity of the phenomenon means that, for up to 2,5% of infected people, contagion is almost certain to happen whatever the tests and contact tracing carried out.

Indeed, to stop these people infecting others, we would need to know they are infected at the very moment they are and to be able to immediately isolate them. This would mean creating a device that can test individuals permanently, without secondary effects nor pain and without errors. Furthermore, this device would have to be able to alert the infected people. Receiving the signal, these infected people could behave in such a way they won’t risk infecting others. However, considering possible or rather probable unwillingness of a fraction of the population to comply with isolation needs, trends towards incivility and more rarely even malevolence, it is likely that the device would also have to warn authorities. Assuming such a device were to exist, ethicals debate are likely.

In any case, once infection is detected, isolation would have to be implemented immediately – the easiest and least constraining isolation being truly efficient masks, of course.

Waiting for such a device, the only way to stop this specific type of contagion, and until these 2.5% can be better characterised, is to lessen or even stop the quantity of virus each and every individual can shed in the environment, on the one hand, and to heighten to the maximum the protection of another person against absorbing the virus. This means efficient face masks and rigorous hygiene to stop contamination through surfaces and materials (for a recent review of studies on face masks’ efficiency see, Chu et al., “Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis“, The Lancet, 1 June 2020).

Asymptomatic contagion

We saw that symptoms, which mean that people feel unwell, are a natural way to slow and reduce contagion. Indeed, people stop their usual activity because the do not feel well. However, other possibilities exits.

If people are ill and contagious, without ever developing symptoms – they are asymptomatic – then the virus may spread more. Indeed, these people will be completely unaware that they are ill, and how could they know? They will thus carry on with their usual activities, meanwhile infecting others.

Furthermore, many detection systems (at least up until the COVID-19) were implemented to identify symptoms. Thus, even once a new epidemic is detected, asymptomatic people will not be stopped by the various measures taken to stop contamination (Monica Gandhi, M.D., M.P.H.et al. “Asymptomatic Transmission, the Achilles’ Heel of Current Strategies to Control Covid-19“, The New England Journal of Medicine, 24 April 2020). Thus contagion may spread even when one thinks protected by various systems.

COVID-19 patients can be asymptomatic and contagious

This is what happened with the COVID-19.

We now know from different studies carried out in different countries that asymptomatic patients are contagious (Monica Gandhi, M.D., M.P.H.et al., ibid; Zhou R, et al., “Viral dynamics in asymptomatic patients with COVID-19“, International Journal of Infectious Diseases, 7 May 2020).

We had early indications of this with the case of the early German cluster (24 January 2020, warning correspondance 30 January 2020 in NEJM), even though at the time the WHO refused to recognise the possibility of asymptomatic contagion (see Rothe et al. 2020 “Transmission of 2019-nCoV Infection from an Asymptomatic Contact in Germany“, NEJM; Helene Lavoix, The New Coronavirus COVID-19 Mystery – Fact-Checking, The Red (Team) Analysis Society, 5 February 2020).

The WHO, mentioned asymptomatic cases in its situation report-46 on 6 March 2020. In its 27 May 2020 Interim Guidance “Clinical management of COVID-19” it recognises the contagious potential of asymptomatic patients (see pp. 11, 40).

How many patients could be asymptomatic?

We are still unsure of the number of COVID-19 patients who could be asymptomatic. Findings vary widely.

Early estimates, mixing asymptomatic and paucisymptomatic patients, assess that between 30% to 60% of COVID-19 infected patients will be in these cases (Institut Pasteur, updated 27 mai 2020).

In a study on 78 COVID-19 patients “from 26 cluster cases of exposure to the Hunan seafood market or close contact with other patients with COVID-19”, Yang et al. found that 42.3% patients were asymptomatic (Comparison of Clinical Characteristics of Patients with Asymptomatic vs Symptomatic Coronavirus Disease 2019 in Wuhan, China. JAMA Netw Open 27 May 2020).

In another study on a cruise ship departing from Ushuaia, Argentina in mid-March 2020, and infected with the COVID-19, the authors found that 84% of the COVID-19-positive patients were asymptomatic (Ing A.J., et al., “COVID-19: in the footsteps of Ernest Shackleton“, Thorax, 27 May 2020).

The percentages are so high that it is crucial to consider these cases. What may be good news in terms of health and severity of disease – the number of asymptomatic patients – may, on the contrary be bad news in terms of controlling contagion.

Dynamics of asymptomatic contagion

Yang et al. (Ibid.) found that the median duration of viral shedding for asymptomatic patients was 8 days, with a possible range from 3-12 days, compared with 19 days, with a possible range from 16-24 days for symptomatic ones.

Another 7 May 2020 Chinese study on a few cases (31 patients initially asymptomatic, out of which 9 remained asymptomatic), showed that the duration of asymptomatic patients’ viral shedding was between 5 and 14 days, and similar to the duration of the viral shedding of symptomatic patients – between 5 and 16 days (Zhou R, et al., ibid.). The good news was that the viral load of asymptomatic patients in this study was not as high as for symptomatic patients (Ibid.). Zhou et al. thus suggest “the possibility of transmission during their asymptomatic period” while calling for further research.

The study also highlighted that the viral load peaked earlier in asymptomatic patients (as selected in the study – Zhou et al., Ibid.).

However, because we do not know when infection took place for each patient (we only know the date when they tested positive to COVID-19 and hospitalised), it is difficult to infer anything certain in terms either of exact peak time for viral load or even maximum duration of viral shedding (Zhou et al., Ibid.). We also have no idea about the incubation period, as the latter is calculated according to symptoms.

Even though the contagion potential of asymptomatic people may be lower, for our purpose, we need nonetheless to take it into account. As for the duration of viral shedding to consider, because the studies available still concern a small number of patients, out of caution and considering the risks, it seems better to consider the longest possible duration, i.e. 14 days.

As for pre-symptomatic infections, the only way to stop contagion spread by asymptomatic patients is first to identify them through testing and second to isolate them. The duration of the isolation must be, ideally, the whole length of the period during which they could possibly transmit the virus, i.e. the duration of the viral shedding. Here we have, however, a problem, as appeared in Zhou et al. study. Once we identify someone who is infected and does not have any symptoms, we do not have any way to know when this person has been infected, nor if s/he is pre-symptomatic or asymptomatic.

If we imagine s/he was infected the day of detection (in the case of the shortest possible incubation), s/he may start developing symptoms two to three days later. Thus it was a pre-symptomatic case. The isolation period must be the classical isolation period of a symptomatic patient with COVID-19, starting from symptom onset (and NOT from the day of detection), as detailed below.

If we s/he does not develop symptoms, then it is an asymptomatic case, and the patient must be kept in isolation during the longest possible viral shedding duration, i.e. 14 days. Logically, if the duration identified by research is correct, then the patient should stop being infectious before the end of the 14 days. Tests ideally will need to be done again during this period, and, again ideally, the person will not be released from quarantine both before 14 days and before testing negative (including a system to account for false negative).

Mildly symptomatic contagion

Then, we have people who are contagious and only have very mild symptoms. Notably at the start of the epidemic, when it is not yet known, these people will not stay at home because of these mild symptoms, which will also allow the virus to spread.

Later, once the epidemic and the risks in terms of contagion are known, economic duress, job and career competition, as well as absence of support in everyday life are also likely to favour a behaviour where mildly symptomatic cases may be forced or strongly enticed to overcome mild symptoms and proceed as usual. Incivility and malevolence may also possibly become factors of conscious and willed spread of the disease.

How many symptomatic COVID-19 patients develop mild symptoms

According to the WHO, 40% of symptomatic COVID-19 patients develop a mild form of disease. We do not know if they include asymptomatic people in this estimate.

As previously, we need to know the duration of viral shedding as well as, ideally, the kinetics of the viral load.

Dynamics of mildly symptomatic contagion

According to He et al. (Temporal dynamics in viral shedding and transmissibility of COVID-19‘, 15 April 2020), the viral load of patients was highest closest to symptom onset and decreased until 21 days after symptoms’ onset, without difference according to illness severity.

This is longer than the estimated duration of viral shedding found by Zhou R, et al., which was between 5 and 16 days.

Meanwhile, in another small study on 16 Chinese patients with mild symptoms, scientists found that “the mean duration of symptoms was estimated to be 8 days (interquartile range, 6.25–11.5). Most important, half (8 of 16) of the patients remained virus positive (a surrogate marker of shedding) even after the resolution of symptoms (median, 2.5 d; range, 1–8 d) (Chang et al., “Time Kinetics of Viral Clearance and Resolution of Symptoms in Novel Coronavirus Infection“, Am J Resp Crit Care Med , 1 May 2020). Thus, at worst, patients with mild symptoms could remain contagious for up to 11,5 days plus 8 days, i.e. 19,5 days.

Peak infectiousness is reached before day 5 after the onset of symptoms and then decline during the first week for patients with mild disease (Wölfel, R. et al., “Virological assessment of hospitalized patients with COVID-2019“, Nature, 1 April 2020). If there is lung infection then the peak is reached around 10 to 11 days.

Furthermore, Wölfel, R. et al underline a very important point: people can both develop antibodies and remain infectious:

“Seroconversion occurred after 7 days in 50% of patients (and by day 14 in all patients), but was not followed by a rapid decline in viral load.” 

Wölfel, R. et al., “Virological assessment of hospitalized patients with COVID-2019“, Nature, 1 April 2020

Thus the idea to use serological tests haphazardly and to let people believe that having developed antibodies – testing positive with serological tests – could make them safe for others is false, thus extremely dangerous and will lead to further contagion.

For its part the WHO highlights that “limited published and pre-published information provides estimates on viral shedding of up to 9 days for mild patients and up to 20 days in hospitalized patients” (Interim Guidance 27 May 2020, p.11). It thus does not concord with what He et al. and Chang et al. found.

For the sake of safety, and waiting for further research, the longest period of danger, i.e. 21 days, must be considered, with possibly lighter yet safe measures for the last 5 days (21 days minus 16 days).

This means that infected people with mild symptoms can potentially remain contagious for up to 21 days following symptom onset plus the up to 3 days of pre-symptomatic contagion. If we take Chang et al. study, the dangerous period is 19,5 days plus 3 days. If these people carry on with their lives, then in 22,5 to 24 days, they have the time to infect quite a lot of other people, according to their lifestyle.

As for the other cases, it is imperative that these patients be isolated. Here, the major hurdle to overcome may not be not knowing about the disease as in asymptomatic and pre-symptomatic contagion, but other factors external to the illness itself, from economic to cultural ones. Of course, these factors will also be active for other cases, but here they are possibly the most important to consider and overcome.

Moderate, severe and critical cases and contagion post-resolution of symptoms

Contagion through moderate disease

When people develop moderate symptoms, i.e. pneumonia (40% cases) (WHO Interim report 27 May 2020, p. 13), even though they are not hospitalised, their condition forces them to stay at home. The potential of contagion is limited to family and health personal caring for the patient.

As long as the illness is unknown, then contagion may spread easily. Once the disease and its infectivity are known, as after a first wave, then the contagion risks should become minimal.

For our purpose it may nonetheless be necessary to check how these patients are handled, considering notably cultural and economic factors. The maximum length of viral shedding of 21 days after symptom onset will need to be applied (He et al., Ibid.).

The WHO suggests that isolation and measures stop 10 days after symptom onset “plus at least 3 days without symptoms (without fever and respiratory symptoms).” (Ibid, p. 11).

Severe and critical disease

Finally, when people develop a severe form of disease, then they are hospitalised. As a result, they are removed from the normal course of life. At the start of an epidemic if a special way to separate them from other patients is not implemented, which may not be as the disease is not identified, or if ever the health system breaks down, then they can contaminate other patients and the medical staff. This risk should disappear or be extremely reduced once the disease is known.

Then, once severely ill patients are released post-recovery, if they are still contagious they will again contaminate other people around them. As they may be convalescent, the contamination may be less intense though.

With the SARS-CoV-2, it seems that viral shedding lasts 20·0 days (IQR 17·0–24·0) from illness onset for severely ill recovering patients, and lasts until death (Huang C. et al., “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China“, The Lancet, Vol 395 March 28, 2020: 1058).

However, patients may continue to shed virus long after being discharged from hospital. The WHO underlines that “the longest observed duration of viral RNA detection in survivors was 37 days”, using Huang et al (Ibid.) and Zhou F. et al. (“Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study”, Lancet, 2020).

Meanwhile, the infectious power of the materials with which contagious patients have been in contact also plays a part, including natural elements, such as plants, water, rock, sand. And here our knowledge is even more uncertain. hence we compensate uncertainty by creating barriers between human beings and surfaces where the virus could be. This allows also to overcome uncertainty… better safe than sorry.

Anti-contagion measures and detecting future waves

Here, looking at the dynamics of contagion and taking one by one the various cases through which infection can take place, we have highlighted what could or should ideally be done to stop contagion, according to research and knowledge identified up to 2 June 2020.

Assessing measures and policy against COVID-19 contagion

The further away the measures set up to stop contagion are from the ideal, the more unnoticed contagion can take place.

We summarise these ideal measures in the table below:

Knowledge gatheredIdeal measureMain challenges
TransmissionTransmission through respiratory droplets
Face masks and hygiene, social distancing.
Cultural and normative factors, education, economic factors (cost and availability of efficient masks)
Transmission via aerosolsFace masks and hygiene, social distancing.Cleaning and adaptation of all air conditioning and ventilations

Transmission via surfacesNot included in article



Incubation
Quarantine/isolation for 0 to 28 days
Refusal to be quarantined for so long – cost (but lower than a country lockdown)
Pre-symptomatic contagion
Contagious up to 3 days before symptom onsetCase tracing and testing
The criteria for identification of contagion must be infection, not symptoms
Pre-symptomatic contagion and early incubationInfection and infectivity take place quasi simulateouslyContagious quasi-instantaneously (within hours?)Face masks and hygiene
Quasi-instantaneity of viral shedding (? further specific research needed)
Impossible to detect and isolate on time
Asymptomatic casesInfected up to 27 days before viral shedding starts
Isolation/quarantine for up to 14 days after tested positive
Identification of infection – socio-economic and cultural factors stopping isolation and favouring hiding contactsMaking sure the period is correct, dearth of studies.
Mildly symptomatic contagionInfected up to 27 days before symptom onsetContagious up to 3 days before symptom onsetIsolation/quarantine for up to 21 days after symptom onset (irrespective of resolution of symptoms)Possible lighter measures for the last 5 days (to consider uncertainty and difference among studies)Identification of symptoms onset, socio-economic and cultural factors stopping isolation and favouring hiding symptomsIdentification of Infection
Moderate disease contagionInfected up to 27 days before symptom onsetContagious up to 3 days before symptom onsetIsolation/quarantine for up to 21 days after symptom onset (irrespective of resolution of symptoms)Most at risk are family and health personal caring for the patient –Further study needed
Severe disease contagionInfected up to 27 days before symptom onsetContagious up to 3 days before symptom onsetHospital care – contagion within hospital – considered as well handled once disease knownFor post-recovery patients, up to 24 days after symptom onset? Until test negative plus 3 days?Does not fit with length of hospitalisation – further research needed
Ciritical disease contagionInfected up to 27 days before symptom onsetContagious up to 3 days before symptom onsetHospital care – contagion within hospital – considered as well handled once disease knownFor post-recovery patients, up to 24 days after symptom onset? Until test negative plus 3 days?Does not fit with length of hospitalisation – further research needed
Death

Special measures until burial
Cultural and economic factors
All cases 

Must test negative at least once (or more) before being released.Family members and all people in regular contact with people who are ill should be tested regularly during their possible incubation period and carry out strict hygiene measures plus face mask plus protective equipment?Cultural and normative factors, education, economic factors, (cost and availability of efficient masks)

Evaluation against the ideal measures must be done at country, region or non-state actor level because of the array of measures decided globally. We also need to consider how well these measures are implemented, which may vary according to cases. We would also need to add contagion through materials which we have not detailed here and not forget the critical importance of ventilation and cleaning of air conditioning.

With time, the more unnoticed contagious cases exist, the more likely the quantity of infected people swells. Indeed, day after day, each missed case will potentially infect other people. As the missed cases pile up and infect others, at one stage, even testing – to say nothing of case tracing – may become difficult. The number of cases will be so numerous that we shall see the second wave emerge.

Considering the proportion of disease severity, the more people are infected, the more likely we will be in the case of an uncontrollable contagion with an increasingly intense second wave.

At this stage, we need to introduce other country specific characteristics. Indeed, we need to consider not only the health system but also the specific demographics of a zone, as the severity of the disease, thus hospitalisation, depends on other pathologies and on age (Robert Verity, et al., “Estimates of the severity of coronavirus disease 2019: a model-based analysis“, The Lancet Infectious Diseases, 23 March 2020). Furthermore severity of disease and hospitalisation may also depend on countries and thus domestic clinical studies may be better adapted.

The case of quarantine for arrivals on a territory

Considering the importance of travels for the spread of the pandemic, as highlighted in “The Hidden Origin of the COVID-19 and the Second Wave” (Helene Lavoix, The Red (Team) Analysis Society, 25 May 2020), we look here in more detail to the quarantine that would need to be set up at arrival in a country.

If a quarantine needs to be implemented to isolate someone who is potentially infectious, then this quarantine must last 28 days as detailed above. Such a quarantine will most probably be too long but it will cover the longest possible time of incubation. It will assume that a person was infected on the day of the start of the quarantine, and allow for the longest possible time of incubation.

If, for example, unknowingly the person had been infected 5 days before the start of the quarantine, then the quarantine could ideally be reduced to 23 days (28-5 days). But we do not have a way to know when infection took place. Because of this inability to know exactly when a person is infected, then people cannot be released before these 28 days. Even in this case, it would seem that we do not cover 100% of infections.

Thus, if we compare quarantine policies against this benchmark, we can evaluate the potential for a second wave. The usual 14 days standard tells us that we are missing 101 out of every 10 000 cases, as Lauer et al. highlighted. However, it is difficult to estimate how many people are concerned quantitatively.

Certainly, when the number of infected cases has been lowered thanks to lockdown as in many places, then quarantines may appear as unfair practice. However, if the virus does not change, unfortunately, there is no other way, as long as we have neither vaccination nor certain treatment.

For example, on 31 May 2020, one asymptomatic case was identified in China, which had arrived on a flight chartered from Germany to China, to try to re-kindle business (Stella Qiu, Ryan Woo, “China says 2 new coronavirus cases, asymptomatic case on German charter“, Reuters, 31 May 2020). This shows that even in a country that is said to have mastered its epidemic such as Germany, the virus circulate. Had China not tested business people at arrival and had a quarantine not existed, then the asymptomatic carrier would have been free to move around and infect people for up to 14 days in China (the duration of viral shedding for asymptomatic case). If one traveler was asymptomatic, it means s/he was infected and expelled virus during the flight. Thus, all other passengers may also be incubating. They thus all need to be quarantined. The risk of not doing so is too severe. Actually all passengers could also have been infected before boarding.

As the German symptomatic case arriving in China and as the too short generalised 14 days quarantine show, we are, globally, letting cases slip and move across countries and continents. Thus, social distancing measures, various hygienic measures and face masks here become even more important to try making sure these missed cases will infect as few people as possible.

As far as these individual measures are concerned, note that the burden is on each and every citizen. Somehow, that maybe considered as a test of the true capacity to democracy of a society. Meanwhile, cultural values will be important. For example, the obvious disregard many European populations, notably in capital cities, as well as many Americans, show for face masks and social distancing measures does not bode well for the ability to mitigate a second wave.

Other factors, however, such as population density, legitimacy, economic duress and inequality will also be critical to assess how much citizens will respect measures.

To conclude, once a detailed evaluation of each anti-COVID-19 measure is done for each country, we shall get a more precise assessment of the possibility for a second wave in that country. Using then each departure from the ideal, and characterisation of this departure, we shall be able to create a system of indicators that will be able to warn about happenstance of a second wave. Interestingly, this warning system may help steer policies and thus stop the very occurence of a second wave.

A similar system maybe created for each non-state actor. It will help assessing the potential of this actor as a future cluster and vector of the disease.

Now a crucial question remain, what if the SARS-CoV-2 and its illness, the COVID-19 change? This is what we shall see next.

Detailed Bibliographical References

Chang D, Mo G, Yuan X et al. “Time Kinetics of Viral Clearance and Resolution of Symptoms in Novel Coronavirus Infection“, Am J Resp Crit Care Med 2020. doi: 10.1164/rccm.202003-0524LE

Chew, Suok Kai. “SARS: how a global epidemic was stopped.” Bulletin of the World Health Organization vol. 85,4 (2007): 324. doi:10.2471/BLT.07.032763.

Chu, Derek K, Elie A Akl, Stephanie Duda, Karla Solo, Sally Yaacoub, Holger J Schünemann, on behalf of the COVID-19 Systematic Urgent Review
Group Effort (SURGE) study authors, “Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis“, The Lancet, 1 June 2020, DOI:https://doi.org/10.1016/S0140-6736(20)31142-9.

Gandhi, Monica, M.D., M.P.H., Deborah S. Yokoe, M.D., M.P.H., and Diane V. Havlir, M.D., “Asymptomatic Transmission, the Achilles’ Heel of Current Strategies to Control Covid-19“, The New England Journal of Medicine, 24 April 2020, DOI: 10.1056/NEJMe2009758.

He, X., Lau, E.H.Y., Wu, P. et al. “Temporal dynamics in viral shedding and transmissibility of COVID-19“. Nat Med 26, 672–675 (2020). https://doi.org/10.1038/s41591-020-0869-5

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X,
Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao
H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B., “Clinical
features of patients infected with 2019 novel coronavirus in Wuhan, China
“,
Lancet 2020; 395 (10223): 497-506.

Ing A.J., Cocks C, Green J. P., “COVID-19: in the footsteps of Ernest Shackleton“, Thorax, Published Online First: 27 May 2020. doi: 10.1136/thoraxjnl-2020-215091

Jewell NP, Lewnard JA, Jewell BL. “Caution Warranted: Using the Institute for Health Metrics and Evaluation Model for Predicting the Course of the COVID-19 Pandemic”. Ann Intern Med. 2020; [Epub ahead of print 14 April 2020]. doi: https://doi.org/10.7326/M20-1565

Kissler, Stephen M. Christine Tedijanto, Edward Goldstein, Yonatan H. Grad, Marc Lipsitch, “Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period“, Science, 14 April 2020 DOI: 10.1126/science.abb5793.

Korber, B, WM Fischer, S Gnanakaran, H Yoon, J Theiler, W Abfalterer, B Foley, EE Giorgi, T Bhattacharya, MD Parker, DG Partridge, CM Evans, TI de Silva, on behalf of the Sheffield COVID-19 Genomics Group, CC LaBranche, DC Montefiori, “Spike mutation pipeline reveals the emergence of a more transmissible form of SARS-CoV-2” bioRxiv 2020.04.29.069054; doi: https://doi.org/10.1101/2020.04.29.069054.

Lauer, Stephen A., MS, PhD; Kyra H. Grantz, BA; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; and Justin Lessler, PhD, “The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application“, Annals of Internal Medicine, Vol. 172 No. 9, 5 May 2020, https://doi.org/10.7326/M20-0504.

Meher K Prakash, “Quantitative COVID-19 infectiousness estimate correlating with viral shedding and culturability suggests 68% pre-symptomatic transmissions“, medRxiv 2020.05.07.20094789; doi: https://doi.org/10.1101/2020.05.07.20094789

Ruiyun Li, Sen Pei, Bin Chen, Yimeng Song, Tao Zhang, Wan Yang and Jeffrey Shaman, “Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)“, Science, 01 May 2020: Vol. 368, Issue 6490, pp. 489-493, DOI: 10.1126/science.abb3221

Verity, Robert, Lucy C Okell, Ilaria Dorigatti, Peter Winskill, Charles Whittaker*, Natsuko Imai, Gina Cuomo-Dannenburg, Hayley Thompson, Patrick G T Walker, Han Fu, Amy Dighe, Jamie T Griffin, Marc Baguelin, Sangeeta Bhatia, Adhiratha Boonyasiri, Anne Cori, Zulma Cucunubá, Rich FitzJohn, Katy Gaythorpe, Will Green, Arran Hamlet, Wes Hinsley, Daniel Laydon, Gemma Nedjati-Gilani, Steven Riley, Sabine van Elsland, Erik Volz, Haowei Wang, Yuanrong Wang, Xiaoyue Xi, Christl A Donnelly, Azra C Ghani, Neil M Ferguson, “Estimates of the severity of coronavirus disease 2019: a model-based analysis“, The Lancet Infectious Diseases, 23 March 2020)

Wei-jie Guan, Ph.D., Zheng-yi Ni, M.D., Yu Hu, M.D., Wen-hua Liang, Ph.D., Chun-quan Ou, Ph.D., Jian-xing He, M.D., Lei Liu, M.D., Hong Shan, M.D., Chun-liang Lei, M.D., David S.C. Hui, M.D., Bin Du, M.D., Lan-juan Li, M.D., et al., for the China Medical Treatment Expert Group for Covid-19, “Clinical Characteristics of Coronavirus Disease 2019 in China“, N Engl J Med, February 28, 2020; 382:1708-1720 DOI: 10.1056/NEJMoa2002032

Wölfel, R., Corman, V.M., Guggemos, W. et al., “Virological assessment of hospitalized patients with COVID-2019“, Nature (2020), 1 April 2020, https://doi.org/10.1038/s41586-020-2196-x

Yang R, Gui X, Xiong Y. Comparison of Clinical Characteristics of Patients with Asymptomatic vs Symptomatic Coronavirus Disease 2019 in Wuhan, China. JAMA Netw Open. 2020;3(5):e2010182. doi:10.1001/jamanetworkopen.2020.10182

Zhong et al., “Clinical characteristics of 2019 novel coronavirus infection in China“, 6 February 2020, medRxiv 2020.02.06.20020974.

Zhou R, Li F, Chen F, Liu H, Zheng J, Lei C, Wu X, Viral dynamics in
asymptomatic patients with COVID-19
, International Journal of Infectious Diseases (2020) 7 May 2020, doi: https://doi.org/10.1016/j.ijid.2020.05.030

Zhou F., Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054-62. Epub 2020/03/15

The Red (Team) Analysis Weekly – 28 May 2020

This is the 28 May 2020 issue of our weekly scan for political and geopolitical risks (open access).

Editorial: This week, as increasingly clear over the last month, many signals relate to and indicate that the COVID-19 pandemic’s cascading effects are at work. We highlight here some of those picked up by the scan and that deserve further monitoting.

The COVID-19 could help close the Thucydides trap: Again, the U.S. increases pressure on China, in and probably because of a far more difficult context. That context includes the tragic still-ongoing COVID-19 pandemic and its multiple complex impacts. The position of the U.S. in the world was already at stake before the pandemic. The COVID-19 so far seems to have revealed more fragility than strength, which may precipitate dangerous behaviour. Furthermore presidential elections are also looming, which may not be stabilising.

Europe and European states might be forced towards a new path, but do they still have the necessary might, creativity, vision, flexibility and self-belief to do so?

Meanwhile, Turkey goes on trying to take advantage of the COVID-19, playing actors against each other, with Libya as epicentre.

South Asia, also because of the COVID-19 induced food insecurity and disappearance of remittance, could be at high risk of civil unrest. Should these risks materialise, would that mean, again, potential coming disruption to supply-chains, notably for drugs, medicines and vaccines?

Using horizon scanning, each week, we collect weak – and less weak – signals. These point to new, emerging, escalating or stabilising problems. As a result, they indicate how trends or dynamics evolve.

The Scan

The 28 May 2020 scan→

Horizon scanning, weak signals and biases

We call signals weak, because it is still difficult to discern them among a vast array of events. However, our biases often alter our capacity to measure the strength of the signal. As a result, the perception of strength will vary according to the awareness of the actor. At worst, biases may be so strong that they completely block the very identification of the signal.

In the field of strategic foresight and warning, risk management and future studies, it is the job of good analysts to scan the horizon. As a result, they can perceive signals. Analysts then evaluate the strength of these signals according to specific risks and dynamics. Finally, they deliver their findings to users. These users can be other analysts, officers or decision-makers.

You can read a more detailed explanation in one of our cornerstone articles: Horizon Scanning and Monitoring for Warning: Definition and Practice.

The sections of the scan

Each section of the scan focuses on signals related to a specific theme:

  • world (international politics and geopolitics);
  • economy;
  • science including AI, QIS, technology and weapons, ;
  • analysis, strategy and futures;
  • the Covid-19 pandemic;
  • energy and environment.

However, in a complex world, categories are merely a convenient way to present information, when facts and events interact across boundaries.

The information collected (crowdsourced) does not mean endorsement.

Featured image: Milky Way above SPECULOOS / The Search for habitable Planets – EClipsing ULtra-cOOl Stars (SPECULOOS) is searching for Earth-like planets around tiny, dim stars in front of a panorama of the Milky Way. Credit: ESO/P. Horálek.

The Hidden Origin of the COVID-19 and the Second Wave

In this article we explore the way the COVID-19 pandemic was born and, hidden, spread globally. Learning from this very early process, we deduce initial key elements and indicators to monitor and control the COVID-19 second wave and recurrent ones.

With this series of articles we are looking for ways to better estimate the likelihood of a COVID-19 second wave and of recurrent ones, as well as the timing and intensity of these waves. These are crucial elements to inform scenario-building, early warning processes, as well as design and steering of policies.

Previously, we looked at epidemiological models, which told us that a second wave, followed by others, was the most likely scenario. Yet, we also found these models did not exactly fit what was happening in East Asia, in terms of timing of the exponential rise of cases and numbers of ICU beds needed. The models also diverged regarding the severity of the second wave.

We thus need to find other factors influencing the possible start of the second wave, its velocity and lethality. We also need a system that will be able to handle recurring waves, if any.

Once we have a better understanding of the way the sanitary situation may evolve, then we may also build larger political and geopolitical foresight. Note that we are concerned with fundamental dynamics of politics and security as we explained in “What is political risk“.

Here, we focus on the way the COVID-19 pandemic started and on its very early development throughout the world. Looking at a situation in a forward-looking way, even using hindsight, often brings a new perspective on our understanding of dynamics and underlying processes. We apply this approach here, building upon research in and findings from genomic epidemiology and phylogenetics. We look first at the birth of the virus, its date and at its zoonotic origin and deduce a first indicator to monitor. Then, we turn to the way the virus spread, unnoticed, in the cases of the UK, the U.S., Iceland, Australia, Italy, France, and Spain. Finally, we stress a major lesson that needs to be learned: travels are vectors of choice for the pandemic. We highlight a corresponding indicator. We also underline the very different timeframes for the early spread of the virus.

A new virus is born

Date of birth

When a new virus emerges and causes a disease, as is the case with the SARS-CoV-2 and the COVID-19, it can do so undetected for the very reason that it is new. Being novel, we, human beings, do not look for it. We certainly should set up new warning systems not to be taken by surprise, but this is another topic.

In our case, with hindsight and thanks to the incredibly fast and numerous research done in phylogenetics, we may estimate that the SARS-CoV-2 was born – i.e. it jumped to humans – between 6 October 2019 and 11 December 2019 (Table 1, Lucy van Dorp et al. “Emergence of genomic diversity and recurrent mutations in SARS-CoV-2“, Infection, Genetics and Evolution, 5 May 2020).

Nota: Phylogenetics is the study of evolutionary relationships among biological entities (EMBL-EBI training platform). “A phylogeny, also known as a tree, is an explanation of how sequences evolved, their genealogical relationships, and therefore how they came to be the way they are today” (Ibid.). You can find here other definitions for phylogeny and phylogenetics.
Thus, here we are using research that establishes the genealogy of the SARS-CoV2. Screenshot of the phylogeny of the SARS-CoV-2 at different dates are presented below.

Zoonotic origin

The SARS-CoV-2 belongs to the β‐coronavirus genus of the Coronaviridae family. Most scientists concur to consider the virus is highly likely of a zoonotic origin, i.e. it comes from an animal. However, we do not know yet with certainty which is the zoonotic source, even though a coronavirus hosted in the horseshoe bat shows genetical close identity (ibid.). The SARS-CoV-2 could be “a recombinant virus between bat and pangolin coronaviruses” (Jiao-Mei Huang, et al., “Evidence of the Recombinant Origin and Ongoing Mutations in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)“, bioRxiv 2020.03.16.993816).

Indicator

The zoonotic origin of the SARS-CoV-2 alerts us to possible further contagion across species, which should be closely monitored. We need to monitor human-to-animal and animal-to-human contagion.

For example, on 19 May 2020, the Dutch government sent a letter to parliament highlighting that a mink-to-human contagion was likely to have taken place in one of the four Dutch infected mink farms (Wageningen University and Research, “COVID-19 detected on four mink farms“, 20 May 2020). Research is ongoing on the topic (e.g. World Organisation for Animal Health).

Even in the case such infections remain few and far between, they may nonetheless start chains of contagion and thus favour future waves. Special attention is warranted, as the WHO explains in “Reducing animal-human transmission of emerging pathogens“. Impacts on biodiversity should also not be neglected. Meanwhile large impacts on actors involved are likely.

The new virus spreads, unnoticed

At the end of the autumn 2019, we thus have a completely new virus that has infected one person, then another and another. We, as human beings of the 21st century, only start thinking that something is amiss when people start being ill, with an illness that does not exactly fit with what we know. If people start dying, then we pay even more attention. The more people are ill or dying, the more we pay attention. However, by the time we reach this stage, the new virus may have spread a lot, or not, according to its characteristics.

Visualising the early spread of the SARS-CoV-2

This is exactly what happened with the SARS-CoV-2. It spread early. In the series of the four screenshots below, you will see the phylogeny of the SARS-CoV-2 up to 23 January 2020 and the corresponding transmission map, then the same up to 26 May 2020 (application Genomic epidemiology of novel coronavirus – Global subsampling, Maintained by the Nextstrain team. Enabled by data from GISAID).

The phylogenetic tree of SARS-CoV-2 up to 23 January 2020
Screenshot of the application Genomic epidemiology of novel coronavirus – Global subsampling
Maintained by the Nextstrain team. Enabled by data from GISAID
Transmission map up to 23 January 2020 (some links are hypotheses – see explanation on Nextstrain or GISAID website) – Screenshot of the application Genomic epidemiology of novel coronavirus – Global subsampling – Maintained by the Nextstrain team. Enabled by data from GISAID
The phylogenetic tree of SARS-CoV-2 up to 26 May 2020
Screenshot of the application Genomic epidemiology of novel coronavirus – Global subsampling
Maintained by the Nextstrain team. Enabled by data from GISAID
Transmission map up to 26 May 2020 (some links are hypotheses – see explanation on Nextstrain or GISAID website) – Screenshot of the application Genomic epidemiology of novel coronavirus – Global subsampling – Maintained by the Nextstrain team. Enabled by data from GISAID

Using genomic epidemiology and phylogeny, research further explored the early spread of the pandemic.

Early spread and multiple entry points in the UK, the U.S., Iceland, Australia

In their study, Lucy van Dorp et al. (Ibid.) found that, apart for China and to a point Italy – so far – each epidemic in the countries considered – the UK, the U.S., Iceland, Australia – had “been seeded by a large number of independent introductions of the virus”. This means that we did not only have one or two “patient(s) zero”, for each of these countries, but many of them. Furthermore, the authors highlight that the spread of the virus took place very early. It would have been useful if authors had detailed further how early was early (see figure S4, in supplementary material 5, not detailed enough for our purpose).

“The genomic diversity of the global SARS-CoV-2 population being recapitulated in multiple countries points to extensive worldwide transmission of COVID-19, likely from extremely early on in the pandemic.”

Lucy van Dorp et al. “Emergence of genomic diversity and recurrent mutations in SARS-CoV-2“, Infection, Genetics and Evolution, 5 May 2020

Spain: multiple entry points and possible start of circulation in mid-February

A similar phylogenetic study for Spain reached also the conclusion that the epidemic in Spain resulted from “multiple SARS-CoV-2 introductions” (Francisco Díez-Fuertes et al. “Phylodynamics of SARS-CoV-2 transmission in Spain“, bioRxiv 2020.04.20.050039).

Some of them could be traced to other European countries. Once in Spain, at least “two [SARS-CoV-2 introductions] resulted in the emergence of locally transmitted clusters, with further dissemination of one of them to at least 6 other countries”.

However, in the case of Spain, the virus introductions could have taken place between 14 and 18 February 2020 (Ibid.). This is much later than the timeframe Lucy van Dorp et al. suggested for the countries they studied (Ibid.), which is logical considering the route the virus took.

France: possible start of viral circulation between end of November 2019 and 23 December 2019

In the case of France, a new early COVID-19 case has now been found retrospectively. The patient was admitted in hospital on 27 December 2020 after four days of symptoms (Deslandes et al., “SARS-COV-2 was already spreading in France in late December 2019“, International Journal of Antimicrobial Agents, 3 May 2020).

The patient, without a travel history to China, was most probably infected with the SARS-CoV-2 before 23 December 2020, date of symptom onset. If we consider the probable length of incubation, then this patient could have been infected between 26 or 27 November (27 days) and 21 December 2019 (1,8 days) (for the incubation period, Stephen A. Lauer, MS, PhD et al., “The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application“, Annals of Internal Medicine, 5 May 2020). There is a higher likelihood it was infected between 7 December (15.6 days) and 21 December 2019 (1,8 days) (Ibid.).

If other cases confirm this study, then the virus could have started circulating in France well before it was officially noticed on 24 January 2020 (Deslandes et al., ibid.), and then exploded exponentially in March 2020. It is however impossible to draw immediate conclusions regarding the dynamics of the epidemic out of this sole case, because, as the cases of Spain, the UK, Iceland, the US and Australia show, France most probably knew multiple points of entry for the virus.

Italy: entry of virus between the second half of January and early February 2020 from Germany

In Italy, a study focusing on three patients from the early outbreak in Lombardy, a cluster of 16 cases reported on 21 February 2020, estimates that the “SARS‐CoV‐2 virus entered northern Italy between the second half of January and early February 2020” (Zehender G, Lai A, Bergna A, et al. “Genomic characterization and phylogenetic analysis of SARS‐COV‐2 in Italy“, J Med Virol, 29 March 2020).

The cases are all related to the 24 January 2020 asymptomatic contagion in Germany in a business meeting (Ibid.), as also found by Stefanelli et al. (“Whole genome and phylogenetic analysis of two SARS-CoV-2 strains isolated in Italy …“. Euro Surveill. 2020;25(13)). Genetically, Stefanelli et al. show that the viral clade in Lombardy is not directly related to the viral cluster of the Chinese tourists diagnosed in Rome on 29 January 2020 (Ibid.).

Lessons Learned and Indicators

The phylogenetic country studies we sampled here highlight crucial points in our quest for indicators regarding COVID-19 waves. Some of this points may be obvious or common sense, however, in the light of policy-decisions taken, it is worthwhile stressing them again.

Travels matter for the spread of a pandemic

Unsurprisingly, human travels, whatever the motivation, are the way through which the virus spreads. Actually, the virus spread internationally, thanks to our way of life, very early in the pandemic. Indeed, apart from Spain and Italy, the virus could have spread before China identified it faced a new coronavirus on 7 January 2020 (WHO first situation report), and before the WHO published its first situation report on 21 January 2020 (Ibid.).

On 27 January, the WHO advised “against the application of any restrictions of international traffic based on the information currently available on this event” With hindsight, had the WHO, on the contrary, advised against travels and been followed by all countries, then probably some countries, but not all, would have averted the pandemic.

Considering, however the ideological and economic emphasis on trade and travels, it was near-impossible for political authorities, be they international or national to decide to close all borders that early.

Because of the multiplication of virus entry points throughout countries so early in the pandemic process, then the travel restrictions’ measures that were initially solely directed against China – the country of visible outbreak – were insufficient. They probably contributed nonetheless to lower the number of infections. Hence, the timing of the exponential rise of COVID-19 cases was possibly delayed.

Yet, what should have been done is to apply pandemic-types measures, such as quarantines, to all travels immediately. Of course, because at the time we had no idea about the SARS-CoV-2 and the COVID-19, that was impossible. The only alternative would thus have been to completely close all borders.

As a result, considering the possible multiplication of new diseases in the future, because of climate change and loss of biodiversity, we may imagine that free intensive international travels as we have known will increasingly be something of the past. Assuming this is possible, and beyond the framework of the COVID-19 pandemic, a completely new system integrating both travels and more frequent and intense new diseases needs to be created.

COVID-19 social distancing exit strategies and travels: a second wave indicator

In Europe and the Middle East notably, we are facing multiple decisions across countries to reopen borders, in a way or another, in May, June and July 2020. Meanwhile, some travels will be authorised as exit strategy are implemented (e.g. Michelle Baran, “When Will We Be Able to Travel to Europe?“, AFAR, 14 May 2020; “Coronavirus: Emirates announces limited passenger flights for May“, Khaleej Times, 30 April 2020; “Press conference of the Croatian Minister of the Interior Davor Božinović: Croatia wants to open borders for business travellers for urgent personal and economic reasons after the COVID-19 pandemic caused by SARS-CoV-2 as of 11 May”, Seahelp, 9 May 2020, etc.).

In the light of the initial spread of the pandemic, these decisions to reopen borders and to re-authorise travels appear highly dangerous if we are not certain that very strict anti-COVID-19 measures, considering all parameters, are implemented. In the next article we identify these parameters: see Dynamics of contagion and the COVID-19 Second Wave – last part, the case of quarantine for arrivals on a territory.

Should holes in the surveillance net exist, then the virus will spread again. Thus, assessing travel reopening’s decisions and related measures in the light of what we know on the virus and the illness it causes will be an excellent indicator to estimate the possibility and intensity of the second wave. We shall need to assess and monitor this indicator not only nationally, but also possibly at company level, according to types of travels and routes.

A still elusive timing

As far as timing is concerned, the early start of the pandemic could suggest a longer timeframe for the period from the start of contagion to outbreak, i.e. cases starting to rise exponentially that are difficult or impossible to control.

If some identifiable trend emerged, then we could use it to crudely assess the start of a second wave and recurrent ones. Indeed, we could make an analogy between the very start of the COVID-19 and the situation post-social distancing exit, because most of the time, in the post-first wave framework, we do not know exactly how many people are infected and even less who is infected. The assessment would be crude, however, because, two differences between the start of the first wave and the post-first wave world operate in opposite directions. First, the number of infected people is much higher than at the very start of the pandemic, so the timeframe we would obtain would have to be shortened. On the other hand, we now have knowledge that did not exist and use measures that could not implemented at the very beginning of the pandemic. This should lengthen the time to a new possible outbreak, and even possibly make such an outbreak impossible.

To estimate the time it took between early infection and “start of outbreak proper”, we use the findings we collected earlier, and create the following table. We use the threshold of 50 identified cases of COVID-19 for the “start” of each national outbreak.


Estimated date for early infectionsStart of “outbreak”Time to “outbreak”
Chinabetween 6 October 2020 and 1st December 202095 cases on 23 January between 54 and 109 days
Italybetween the second half of January and early February 202093 cases on 23 Februarybetween 23 and 38 days
Francebetween 26 or 27 November (27 days) and 21 December 201961 cases on 2 Marchbetween 71 and 96 days
Spainbetween 14 and 18 February 202057 cases on 4 Marchbetween 10 and 14 days
Crude Estimate of the time between early infection and “start of COVID-19 outbreak” – Source: detailed above and for cases John Hopkins CSSE: Tracking the COVID-19 (ex 2019-nCoV) spread in real-time

Unfortunately, we obtain wide differences between countries, which is not very helpful for our purpose. Furthermore, we are not sure that all early cases have been identified and accounted for in each country, apart from China. We thus have to look for other approaches and factors if we want to find a useful way to improve our assessment of the timing of a second wave.

This is what we shall do with the next article, while continuing to identify useful indicators regarding the second wave and possible other waves.


Detailed Bibliographical References

Deslandes, A., V Berti, Y Tandjaoui-Lambotte MD, Chakib Alloui MD, E Carbonnelle MD, PhD, JR Zahar MD, PhD, S Brichler MD, PhD, Yves Cohen MD, PhD, “SARS-COV-2 was already spreading in France in late December 2019“, International Journal of AntimicrobialAgents, 3 May 2020, doi: https://doi.org/10.1016/j.ijantimicag.2020.106006

Díez-Fuertes, Francisco, María Iglesias Caballero, Sara Monzón, Pilar Jiménez, Sarai Varona, Isabel Cuesta, Ángel Zaballos, Michael M Thomson, Mercedes Jiménez, Javier García Pérez, Francisco Pozo, Mayte Pérez-Olmeda, José Alcamí, Inmaculada Casas, “Phylodynamics of SARS-CoV-2 transmission in Spain“, bioRxiv 2020.04.20.050039; doi: https://doi.org/10.1101/2020.04.20.050039.

Genomic epidemiology of novel coronavirus – Application: maintained by the Nextstrain team 

GISAID

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X,
Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao
H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B., “Clinical
features of patients infected with 2019 novel coronavirus in Wuhan, China
“,
Lancet 2020; 395 (10223): 497-506.

Huang, Jiao-Mei, Syed Sajid Jan, Xiaobin Wei, Yi Wan, Songying Ouyang, “Evidence of the Recombinant Origin and Ongoing Mutations in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)“, bioRxiv 2020.03.16.993816; doi: https://doi.org/10.1101/2020.03.16.993816

Lauer, Stephen A., MS, PhD; Kyra H. Grantz, BA; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; and Justin Lessler, PhD, “The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application“, Annals of Internal Medicine, Vol. 172 No. 9, 5 May 2020, https://doi.org/10.7326/M20-0504.

Lucy van Dorp, Mislav Acman, Damien Richard, Liam P. Shaw, Charlotte E. Ford, Louise Ormond, Christopher J.Owen, Juanita Pang, Cedric C.S.Tan, Florencia A.T. Boshier, Arturo Torres Ortiz, François Balloux, “Emergence of genomic diversity and recurrent mutations in SARS-CoV-2“, Infection, Genetics and Evolution, Available online 5 May 2020, https://doi.org/10.1016/j.meegid.2020.104351.

Stefanelli Paola, Faggioni Giovanni, Lo Presti Alessandra, Fiore Stefano, Marchi Antonella, Benedetti Eleonora, Fabiani Concetta, Anselmo Anna, Ciammaruconi Andrea, Fortunato Antonella, De Santis Riccardo, Fillo Silvia, Capobianchi Maria Rosaria, Gismondo Maria Rita, Ciervo Alessandra, Rezza Giovanni, Castrucci Maria Rita, Lista Florigio, on behalf of ISS COVID-19 study group. “Whole genome and phylogenetic analysis of two SARS-CoV-2 strains isolated in Italy in January and February 2020: additional clues on multiple introductions and further circulation in Europe“. Euro Surveill. 2020;25(13): pii=2000305. https://doi.org/10.2807/1560-7917.ES.2020.25.13.2000305

Zehender G, Lai A, Bergna A, et al. “Genomic characterization and phylogenetic analysis of SARS‐COV‐2 in Italy“, J Med Virol. 2020;1–4. https://doi.org/10.1002/jmv.25794

Zhong et al., “Clinical characteristics of 2019 novel coronavirus infection in China“, 6 February 2020, medRxiv 2020.02.06.20020974.


Featured image: Mexican free-tailed bats exiting Bracken Bat Cave – Nota: these bats are not those considered so far for the SARS-CoV-2 – The picture was chosen from an art and aesthetic point of view – photo credit: USFWS/Ann Froschauer / [Public Domain]


The Red (Team) Analysis Weekly – 21 May 2020

This is the 21 May 2020 issue of our weekly scan for political and geopolitical risks (open access)

Editorial: This week our main signals highlight notably and among many indications:

  • a possible mutation of the SARS-CoV-2 (China new cases);
  • domestic and international political and security impacts of the COVID-19 which are increasingly apparent;
  • and an interesting absent signal…
Continue reading “The Red (Team) Analysis Weekly – 21 May 2020”

COVID-19 and Food Insecurity Early Warning

This brief article is a first early warning about food insecurity resulting from the COVID-19 pandemic. The danger is rising and deserves further and more in-depth analysis and monitoring.

As the COVID-19 pandemic developed, we immediately added food insecurity on our watch list of issues to monitor (see our COVID-19 section).

To date, mid-May 2020, indications and signals have started accumulating.

We thus estimate that food insecurity must be added on the watchlist of possible threats to monitor. It warrants in-depth strategic foresight and warning analysis at global and country levels. The very high impact that such threat would have, were it to materialise substantially across countries, is sufficient to pay attention to the issue.

Below, we share with members and readers some early indications of the rise of the issue. We then highlight some points that must be considered in the framework of a strategic foresight and warning or risk analysis. These points should also help with monitoring. Finally, we provide a couple of useful online resources.

Nota Bene: Starting to monitor the rise of a possible danger or threat does not mean that the threat will materialise with absolute certainty. It means that the possibility to see that threat becoming a reality increases. Thus the evolution must be followed closely. Actors may start thinking about developing answers and responses accordingly.

Some early indications and signals

Russia

Polina Devitt, “UPDATE 4-Russia will suspend grain exports for 6 weeks if its quota runs out in mid-May“, Reuters, 17 April 2020.

Anatoly Medetsky and Megan Durisin  “Russia Halts Wheat Exports, Deepening Fears of Global Food Shortages” Time Magazine, 27 April 2020

Iran

Maha El Dahan, Parisa Hafezi, Jonathan Saul, “Exclusive: Iran hunts for grains as coronavirus compounds economic woes“, Reuters, 7 May 2020.

ABC.news, “Iran brings in military to battle locusts threatening crops worth billions“, 16 May 2020.

China

Naveen Thukral, Hallie Gu, “China urges food companies to boost supplies on fears of further COVID-19 disruption“, Reuters, 17 May 2020

U.S.

Meredith T. Niles, Farryl Bertmann, Emily H. Belarmino, Thomas Wentworth, Erin Biehl, Roni A. Neff, “The Early Food Insecurity Impacts of COVID-19“, medRxiv 2020.05.09.20096412; doi: https://doi.org/10.1101/2020.05.09.20096412

Locusts (Global)

Tzvi Joffre, “Locust swarms threaten Middle East, India, Africa amid COVID-19 outbreak“, Jerusalem Post, 17 May 2020

 Catherine Byaruhanga, “How do you fight a locust invasion amid coronavirus?“, BBC News, 25 April 2020

Yang Wanli, “Authorities call for Pakistan, China to unite on locust swarm“, China Daily, 18 March 2020.

Some important points to consider

The timeframe must be the whole COVID-19 disruption period, not only the short term with current stocks.

Possible logistics bottlenecks (e.g. port shutting down) and supply chain tensions must also be considered.

Estimates solely relying on markets cannot be trusted entirely, even more so considering the COVID-19 context. Markets have shown their incapacity to anticipate properly – as evidenced over the last months.

Impacts of countries’ actions, such as China, ramping up and protecting their supply, especially considering their weight, must be taken into account.

Meat supply must be actively monitored considering the spread of COVID-19 in slaughter houses and the Swine Fever (see Jean-Michel Valantin, “China, the African Swine Fever Pandemics and Geopolitics“, 14 October 2019, and The Midwest Floods, the Trade War and the Swine Flu Pandemic: The Agricultural and Food Super Storm is Here!, 3 June 2019).

Possible climate-change related events taking place during the period must not be forgotten.

Some resources

Famine Early Warning Systems Network: Home (Mainly country of interest to USAID: Central America, Africa, Afghanistan)

Food and Agricultural Organisation of the U.N. (FAO): Notably

Food Export Restrictions Tracker developed by David Laborde (IFPRI)

U.S. DEPARTMENT OF AGRICULTURE


Featured image: “Supermarket shelves that stock dry pasta varieties are almost empty due to panic-buying as the result of the COVID-19 coronavirus outbreak. This was taken at a Woolworths supermarket in Melbourne, Australia.” by Christopher Corneschi / CC BY-SA 4.0.


The U.S.-China COVID-19 Competition (2): America and Chimerica in Crisis

The COVID-19 pandemic is hammering the United States. Thus, it is pummeling the deep U.S.-China economic interdependency, also known as “Chimerica” (Jean-Michel Valantin, “The US-China Covid-19 Competition (1)”, The Red (Team) Analysis, April 17, 2020).

(Traduction française automatique par intelligence artificielle.)

The mammoth impact of the pandemic on the U.S. results from the shutting down of entire sectors of the economy. These are the effects of the lockdown and social distancing measures the U.S. political authorities implemented to counter the virus (Hélène Lavoix, “COVID 19- Worst case baseline scenarios, March 13 2020 and COVID 19 scenarios- Making sense of antiviral treatment”, The Red Team) Analysis, April 8 2020). Thus, the combination of those sanitary and economic shocks is tearing apart the very fabric of the American economy.

In the second article of this series, we investigate the strategic consequences of the COVID-19 pandemic on the China-U.S. relationship, from the “American front” perspective.

However, to understand those dynamics, one has to understand the way the crisis of the U.S. economy is deeply interacting with the Chinese one. This means that, as the U.S. economy slows down, this will also impact China, and reciprocally. Hence, the fundamental issue at stake is the status of the United States as a great power in a locked down and distanced world.

The geopolitics of an unconsumerist America

In order to slow the Covid-19 down in the continental U.S., the Federal government and the state governments implemented a mix of lockdown and social distancing policies. As everywhere in the world, those sanitary policies are hitting hard on the economic activity, especially on consumer spending.

This brutal slowdown of the economy has very deep consequences, because it also slows, if not stops, the U.S. consuming trend. This trend is fundamentally important for the U.S. and thus for the Chinese economy, because U.S. consumerism is the main driver of the U.S. economic growth (Peter Cohan, “Consumer spending is keeping the economy from shrinking – but a new survey of 10 000 Americans says that might end in 2020”, Inc.com, 4 December 2019).

Mass consuming is inherent to the U.S. agricultural and industrial development since the end of the 19th century. As it happens, the alliance of big oil, industry, finance, transport, and urban development induces an intimate relationship between economic growth and consuming growth (Kevin Philipps, Bad Money, Reckless Finance, failed politics and the global crisis of American capitalism, 2008).

This is turning into a massive problem because, since the 2008 financial crisis, consumption has become the main driver of the U.S. economic growth. Consumer spending accounts for 70% of the economic activity (Clark Merrefield, “Economic earthquake: consumer spending in the wake of the Coronavirus pandemic”, Journalist Resource – Harvard Kennedy School, 17 April 2020). The index of consumer sentiment that lost 30 points since March, at a historical low, highlights this trend (Carmen Reinicke, “Those 5 jarring economic signals flashed red this past week – and they show just how quickly a recession is descending on America”, Business insider, 19-04-2020).

The Covid -19 as the new “Limit(s) to Growth”

As it happens, between March and April 2020, more than 32.5 millions American have lost their jobs, because of the lockdown of the economy and of the social distancing of dozens of millions of people (Anneken Tappe, “Leading Indicator: 1 in 5 American workers filed for unemployment benefits since March”, CNN Business, May 7, 2020).

At the start of March 2020, 211.000 American people were unemployed. That was a historic low in unemployment. At the end of March, almost 7 million people were filing for unemployment benefits. Then, during April, more than 22 million more people lost their jobs. This means that one month of lockdown wiped out the 22 million jobs created since 2008 financial crisis (Anneken Tappe, ibid).


For Americans, losing their job means losing health insurance and any financial security. Thus, their consumer spending and purchasing power is drastically diminishing; worse still, their very subsistence is threatened. This gigantic professional, social and economic disaster embeds itself in the slowing down of the U.S. economy as a whole.  

Unemployment epidemic

This “shutdown” of the economy translates into a contraction of the whole economic activity. If, as a consequence of the shutdown, the U.S. GDP fell at a 4.8% annualized rate during the first quarter of 2020, then, according to JP Morgan and Bloomberg this could translate into a, a historic 40% contraction of the U.S. GDP during the second part of 2020 (Patti Domm, “JPMorgan now sees economy contracting by 40% in second quarter, and unemployment reaching 20%“, CNBC Markets, April 10, 2020). This catastrophic recessionary trend is tied to the systemic consequences of the pandemic, which reveals and amplifies the multiple vulnerabilities of the U.S. and global economy.

Towards the Abyss

The Federal Government tried to alleviate this tremendous shock through the USD 2 trillion relief act, in order to finance expanded unemployment, support for businesses and a direct check of USD 1.200 to people. However, by mid-April, the Small Business Administration ran out of its USD 346 billions relief fund after just two weeks (Mark Niquette and Jennifer Jacobs, “Small Business relief funds drained fast, with many shut out”, Bloomberg, 17 April 2020). Moreover, the combined consequences of the lockdown and of unemployment are triggering a mammoth fall in retail sales of 8.7% in March only.

Knowing that the previous worst slump was 3.8% in November 2008, the March 2020 fall is particularly stark. The same is true for industrial and manufacturing outputs, which lost respectively 6.3% and 5.4% in March. As we write, the April numbers are not known yet, but will undoubtedly be worse. Suffering from the same trend, the new residential construction market fell like a rock by 22.3% in March (Carmen Reinicke, ibid).

This integral slow-down of the U.S. economy is one of the drivers of the oil barrel prices fall. The prices went from around USD 50 to USD 20 to USD-37  at the end of April (“Oil price crashes below 0$ for the first time in history amid pandemic”, CGTN, 21 April 2020). It is also a consequence of the global shift to teleworking.

In the U.S., half of the workers are teleworking since the start of the Covid-19 crisis (Katherine Guyot, Isabel V. Sawhill, “Telecommuting will likely continue long after the pandemic”, Brookings, April 6, 2020). Home-working triggers a sharp decline in fuel, and thus oil, consumption. Furthermore, this trend is also radically diminishing the flows of petrodollars, which are irrigating the U.S. and the international financial system.

Chimerica: towards the (financial) dark side?

On the China-U.S. relationships front, this U.S. economic and social catastrophe is also triggering a massive geopolitical crisis. As it happens, the USD 300 billion U.S. trade deficit with China rests upon the purchase of “made in China”goods (Office of the United States Trade representative, “The People’s Republic of China – U.S-China Trade facts“). Thus, the diminishing U.S. consumption also means a lesser consumption of the exported Chinese industrial output in the U.S.. In other terms, the COVID-19 driven U.S. economic disaster is also turning the U.S.-China relationship into a mammoth geo-economic disaster.

Dialectics of recession

As we saw in “Chimerica”, the American economic activity is intimately linked to the Chinese economic growth. The expression Chimerica translates the quasi-intimate process of hybridation between these two mammoth national economies (Niall Ferguson, Xiang Xu, “Making Chimerica Great again”, Wiley one line Library, 21 December 2018).

This process emerges from the installation of thousands of U.S. industries and corporations in China since the 1980s. It creates the template for the mammoth trade relation between the two countries. In the same time, China buys huge amounts of the U.S. debt by purchasing Treasury bonds. In February 2020, China possessed USD 1,097 trillion of Treasury securities.

This sum amounts to 15.4% of U.S. foreign holdings. It turns China into the second largest foreign holder of U.S. debt, just after Japan and its USD 1.26 trillion (Adam Tooze, Crashed, How a decade of financial crises changed the world, 2019 and Jeffery Martin, “China economy has worst quarter in 40 years after Coronavirus lockdowns, leading the world into recession”, Newsweek, 4-17-20).

From trade war to cash war?

This relation is also the driver of the fantastic trade imbalance between China and the U.S.. As such, it is at the core of the trade war that President Donald Trump has been leading against China since 2018 (Jean-Michel Valantin, “The Midwest floods, the trade war and the pandemic swine flu: the agricultural and food super storm is here“, The Red (Team) Analysis Society, September 3, 2019). Since April 2018, Washington D.C. has imposed new tariffs on the majority of Chinese goods, while Beijing retaliates in kind, with variations on the agricultural products (“Factbox: nearly all goods traded by U.S and China will have tariffs by December 15”,  Reuters, October 10, 2019).

However, as we saw in Chimerica (1), the COVID-19 pandemic is dramatically slowing down the Chinese economy. Indeed, as highlighted here, the economic catastrophe in the U.S. makes it more difficult for its market to absorb Chinese goods. Hence the flows of cash going back to China decrease (Shane Croucher, “China, until recently America’s largest creditor, won’t be funding your stimulus check”, Newsweek, 4-22-20).

In other terms, terms, the pandemic is turning the Chimerica growth driver into a twin engine of dialectical recession. Indeed, the U.S. recession is fueling the trade, industrial and financial Chinese slowdown. In the same dynamic, this trend is reducing the financial capabilities of China to buy U.S treasuries.

In this financial environment, China is starting to sell U.S. bonds, in order to generate dollars. Beijing uses these dollars to buy yuans to support its own currency. Beijing thus tries to alleviate the domestic consequences of the 6.8% contraction of its economy during this first quarter. Those dollar sells tend to outweigh U.S. treasury security purchases. (Croucher, ibid).

Cultivating reciprocal (currency) vulnerabilities

This situation takes place at a very bad moment for the U.S. Indeed, the U.S. Treasury issues a tremendous flow of bonds in order to finance the 2 trillion dollars stimulus package. Currently, the Fed is the main buyer of U.S. debts. But the U.S economic authorities are starting to search for domestic investors (Croucher, ibid).

This situation could rapidly become problematic, given the huge flows of dollars both produced by D.C. and by China. Meanwhile, both are turbocharging their respective crisis, not to say recessions.

Thus, the deeply intricate interdependencies built in and upon Chimerica are becoming dialectics of vulnerabilities for the two super powers.

With the next article, we shall see how the dangerous crisis of Chimerica may also super charge its tense geopolitics.

Featured image: Cupertino, California, 10 April 2020, Friday 9-30 a.m. Commute by Travis Wise / CC BY 2.0

The Red (Team) Analysis Weekly – 14 May 2020

This is the 14 May 2020 issue of our weekly scan for political and geopolitical risks (open access): Very deep transformations are at work. However, there is also a very strong offensive to try keeping the pre-COVID-19 world afloat. Many actors only pay lip service to the idea of a fundamentally different world. Yet, probably, unfortunately for them, this time the cascading changes are fostered by something much stronger than their denial and wishful thinking: a virus and a pandemic and the havoc they create… to say nothing of related escalating international tensions and climate change.

Using horizon scanning, each week, we collect weak – and less weak – signals. These point to new, emerging, escalating or stabilising problems. As a result, they indicate how trends or dynamics evolve.

The 14 May 2020 scan→

Horizon scanning, weak signals and biases

We call signals weak, because it is still difficult to discern them among a vast array of events. However, our biases often alter our capacity to measure the strength of the signal. As a result, the perception of strength will vary according to the awareness of the actor. At worst, biases may be so strong that they completely block the very identification of the signal.

In the field of strategic foresight and warning, risk management and future studies, it is the job of good analysts to scan the horizon. As a result, they can perceive signals. Analysts then evaluate the strength of these signals according to specific risks and dynamics. Finally, they deliver their findings to users. These users can be other analysts, officers or decision-makers.

You can read a more detailed explanation in one of our cornerstone articles: Horizon Scanning and Monitoring for Warning: Definition and Practice.

The sections of the scan

Each section of the scan focuses on signals related to a specific theme:

  • world (international politics and geopolitics);
  • economy;
  • science including AI, QIS, technology and weapons, ;
  • analysis, strategy and futures;
  • the Covid-19 pandemic;
  • energy and environment.

However, in a complex world, categories are merely a convenient way to present information, when facts and events interact across boundaries.

The information collected (crowdsourced) does not mean endorsement.

Featured image: Milky Way above SPECULOOS / The Search for habitable Planets – EClipsing ULtra-cOOl Stars (SPECULOOS) is searching for Earth-like planets around tiny, dim stars in front of a panorama of the Milky Way. Credit: ESO/P. Horálek.

Models for the COVID-19 Second Wave

Europe, the Middle East, Oceania, part of South Asia and the U.S. progressively exit the COVID-19 lockdowns and relax the most severe social distancing measures.

In the meantime, China, Singapore and South Korea, the countries that were hit first and succeeded in controlling the first wave, appear to face different dynamics after easing of anti-COVID-19 measures.

South Korea appeared to have fully controlled local contagion, until 10 May (Hyonhee Shin, Josh Smith, “South Korea scrambles to contain nightclub coronavirus outbreak“, Reuters, 11 May 2020). In two days South Korea reported 69 new cases linked to nightclubs and bars in Seoul and races to test contact cases, still needing to trace more than 3000 people. Its borders are closed and entry is submitted to strict quarantines.

China seemed to fare quite well, despite struggling with clusters and imported cases notably in Heilongjiang (William Yang, “China tries to contain new coronavirus outbreak“, DW, 29 April 2020). Then, on 11 May, just above a months after the end of the lockdown, a new cluster arose in Wuhan, the original center of the epidemic, linked to asymptomatic cases (“China’s Wuhan reports first coronavirus cluster since lifting of lockdown“, Reuters, 11 May 2020). On 9 May, it was Northeastern Jilin province that reported a new small cluster, which triggered a lockdown for the city of Shulan (Ibid.).

Singapore knows what can be seen as a second wave, focused on migrant workers, with cases rising exponentially starting early April 2020 (James Crabtree, “How Singapore’s second wave is exposing economic inequalities“, New Statesman, 6 May 2020).

Meanwhile, history shows that for the 1918-1919 so-called “Spanish” Influenza pandemic, the second and third waves were more lethal than the first spring wave (Jeffery K. Taubenberger and David M Morens, “1918 Influenza: the mother of all pandemics,” Emerging infectious diseases vol. 12,1, 2006).

Thus, what are we to expect in the near future regarding this COVID-19 second wave?

Epidemiologists have modelled various types of scenarios to help policy-makers handle the pandemic and create responses that will mitigate, as much as possible, fatalities. This article looks at four such models and scenarios and highlight what they tell us about future waves of COVID-19. Comparing briefly the scenarios with the reality of the situation in China, Singapore and South Korea, we highlight the scenarios that look more likely and underline the need for further research focused on other factors.

The waves as a result of our interactions with the COVID-19

It is now generally accepted that we shall have to live with the COVID-19. The pandemic, whatever the shape of the outbreaks, is expected to persist until immunisation is reached, assuming this is possible. Immunisation will result either from vaccination or from natural immunity. At best, according to our estimates, and considering the need to manufacture billions of doses immunisation will not happen before winter 2022 (see Helene Lavoix, The COVID-19 Pandemic, Surviving and Reconstructing, The Red Team Analysis Society, 24 March 2020). This timeframe does not take into account the time necessary for an immense mass vaccination campaign.

Actors handled the first outbreak of the COVID-19 or first wave as they could, considering that all countries were caught unprepared, with possibly the exception of South Korea. A range of measures were created and applied, including a stringent lockdown across the world, that allowed to handle the surprise and mitigate fatalities. The main objective of these measures was to stop the contagion while not seeing health systems break down. What we successfully did was not to end the epidemic but to change its course. We avoided an immediate possible worst case scenario (Helene Lavoix, Worst Case Baseline Scenarios for the COVID-19 Pandemic, The Red Team Analysis Society, 24 March 2020).

However, the price to pay was that activity stopped, with an immense cost to ways of life, including the economy.

Now, we are entering a new phase, where we shall start learning to live with the COVID-19. The fear is that once activity restarts, then the epidemic will spread and develop again, bringing about a second wave, with its corollary of death, sufferings and danger to see health systems break down. Political authorities are thus rushing to design sets of measures and policies that should allow us living with the COVID-19, instead of being frozen by the danger, until another kind of death takes us all.

The possibility of a second wave, and of next waves in general depend upon the interactions between the virus, and notably the epidemiology of the SARS-CoV-2,, and the responses and actions the various actors will design and implement.

We are thus both dependent for our activity upon the waves of COVID-19 while, in the same time, also contributing to create and shape them.

Second and recurring waves

The Imperial College COVID-19 Response Team March study

First and foremost, we have the influential study of the Imperial College COVID-19 Response Team, Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand (16 March 2020). Many governments used this report to build their first wave’s lockdown policies.

(A partir de ce point, traduction française automatique par intelligence artificielle.)

In this study, the objective is to minimise fatalities, which demands not overwhelming hospitals and notably the number of intensive care units bed requirements (ICU). The policy measures taken into account are as follows:

  • Case isolation in the home (CI),
  • Voluntary home quarantine (for 14 days – HQ),
  • Social distancing of those over 70 years of age (SDO),
  • Social distancing of entire population (similar to lockdown – SD),
  • Closure of schools and universities (PC).

The study, among other critical factors, considers the R0 (R-nought) or basic reproduction number of an infectious disease. This is a measure that represents “the expected number of secondary cases produced by a typical infected individual early in an epidemic” (O Diekmann; J.A.P. Heesterbeek and J.A.J. Metz (1990). “On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations”Journal of Mathematical Biology 28: 356–382). They “examine values between 2.0 and 2.6”, which is in the range of most estimates. They also take into account the acquired immunity against the SARS-CoV-2 and consider it to be similar to what is obtained against the seasonal influenza, i.e. re-infection cannot reoccur the next season.

With this model, the Imperial College COVID-19 Response Team finds that, for a R0=2.2, after the end of the first wave and once the social distancing of entire population measures are relaxed and school and universities re-open, assuming all other measures stay in place, a new wave starts. It triggers the need to start a new period of social distancing of entire population and school and university closure one month after the beginning of the relaxation.

As a whole, over two years, the full range of measures “is in force approximately 2/3 of the time” (p.12). In two years, we thus have, excluding the first wave, eleven waves of two months each, however the apex of each wave is lower. The second wave thus starts immediately once the lockdown stops, but starts being experienced as such one month after the exit strategy, when the need for SD is triggered.

Imperial College COVID-19 Response Team – 16 March study, p. 12 – “Figure 4: Illustration of adaptive triggering of suppression strategies in GB, for R0=2.2, a policy of all four interventions considered, an “on” trigger of 100 ICU cases in a week and an “off” trigger of 50 ICU cases.”

Although further monitoring will be needed, this seems to correspond approximately to the new clusters emerging in China and South Korea. Yet, we are still far in these two countries from the Imperial College estimated needs in ICU one month after exit from lockdown, as shown in the diagram above for example.

The Harvard T.H. Chan School of Public Health’s model

Scientists of the Harvard T.H. Chan School of Public Health created a model allowing notably for different sensitivity of the virus to seasonality (Stephen M. Kissler, et al. “Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period“, Science, 14 April 2020).

It obtained scenarios similar to those of the Imperial College, with recurring waves into 2022 “requiring social distancing measures to be in place between 25% (for wintertime R0 = 2 and seasonality…) and 75% (for wintertime R0 = 2.6 and no seasonality …) of that time”.

Obviously, the lower the R0 considered and the more important the seasonality factor, the shorter the social distancing period.

Here, as for the Imperial College’s model, the second wave would start immediately as social distancing measures are relaxed. In the Harvard model, in the U.S. case, new social distancing measures would be needed one month after the end of SD if the virus is not seasonal. If the virus is seasonal and if the first wave took place in Spring, as is more or less the case in the U.S., new social distancing measures would be needed 2,5 months after the end of SD.

The emergence of new clusters in South Korea and China in May would tend to indicate that the virus is not or not strongly seasonal. The Singapore case and the April wave anyway showed that heat and humidity do not appear to deter the virus and the disease.

Three possible scenarios for the CIDRAP

On 30 April 2020, the Centre for Infectious Disease Research and Policy (CIDRAP) of the University of Minnesota published “The future of the COVID-19 pandemic: lessons learned from pandemic influenza,” (Kristine Moore, MD, MPH, Marc Lipsitch, DPhil, John Barry, MA, and Michael Osterholm, PhD, MPH).

Pointing out useful similarities but also differences between influenza and the SARS-CoV-2, notably a higher viral transmissibility for the latter, the CIDRAP outlines three possible scenarios. These scenarios give the outlook of the future wave but are not precise enough to allow estimating when the second wave will start.

The first scenario of the CIDRAP is very similar to the scenario of the Imperial College and of Harvard T.H. Chan School of Public Health. According to this first scenario, “the first wave of COVID-19 in spring 2020 is followed by a series of repetitive smaller waves that occur through the summer and then consistently over a 1- to 2-year period, gradually diminishing sometime in 2021.” The strength and timing of waves may vary with the efficiency of the controlling measures taken as well as according to other demographic and geographic factors. This first scenario also expects that full SD measures may have to be implemented regularly.

CIDRAP’s second scenario is inspired by the pattern of the 1918-1919 Spanish Flu pandemic. “The first wave of COVID-19 in spring 2020 is followed by a larger wave in the fall or winter of 2020 and one or more smaller subsequent waves in 2021.” Thus, the main difference with the Imperial College and Harvard scenarios is first about intensity. The second wave is the most lethal. Second, it is about timing. The second wave would take place in the next fall or winter. Finally it is about the number of subsequent waves following the second one, creating two sub-scenarios: only one further wave or subsequent smaller waves.

Considering what is happening in China, Singapore and South Korea, the timing does not seem to correspond. The difference probably comes from the measures implemented for the COVID-19, which are likely to have “artificially” stopped the first wave, compared with the 1918 Influenza pandemic. However, the possibility of a more lethal second wave is serious enough to keep this scenario and further detail comparatively the 1918 pandemic and the COVID-19.

The third CIDRAP scenario is different from the previous models. It envisions that “the first wave of COVID-19 in spring 2020 is followed by a “slow burn” of ongoing transmission and case occurrence, but without a clear wave pattern.” In that case the most severe social distancing measures will not have to be reimplemented but “cases and deaths will continue to occur”.

This scenario does not fit what took place in Singapore. It may be, however, too early to discard it. It may also not be universal. In some countries, excess death and sufferings are not acceptable, while there is always the risk that contagion spreads again exponentially. Thus, even a few cases would trigger SD measures, as possibly in New Zealand, or in Shulan in China (e.g. Amy Gunia, “Why New Zealand’s Coronavirus Elimination Strategy Is Unlikely to Work in Most Other Places“, Time, 28 April 2020; Ibid.).

Mobility and Second wave

The most recent study, again by the Imperial College COVID-19 Response Team, focuses on Italy (Report 20: Using mobility to estimate the transmission intensity of COVID-19 in Italy: A subnational analysis with future scenarios, 4 May 2020).

It models scenarios for a relaxation of isolation measures on 4 May 2020, using increase in mobility as a proxy. A second wave is quasi in-built in their model as mobility is the parameter used to make “the time related reproduction number or effective reproduction number (Rt)” change. Thus, what the model tells us is the extent of infection and death in excess, i.e. the size of the wave.

In the first scenario modelled, mobility increases by 20% above pre-lockdown levels, and in the second, it increases by 40%. However, these scenarios do not account for other anti-COVID-19 measures such as school closures, hygiene, face masks, or testing and contact tracing. It only focuses on the mobility factor.

The first finding, unsurprisingly, is that the situation varies according to region. This could indicate that the way China or Germany, for example, handle the COVID-19 could be the way forward, at least as far as the mobility factor is concerned.

In scenario 1 (20% mobility) the number of excess death rises above 100 on approximately 8 June 2020 in Piedmont, 20 June in Veneto and 13 July in Tuscany, before to rise exponentially.

In scenario 2 (40% mobility) the number of excess death rises above 100 on approximately 28 May in Piedmont, 4 June in Veneto, 10 June in Tuscany, 22 June in Lombardy and 4 July in Emilia Romagna and Liguria, before to rise exponentially.

As the authors highlight, these scenarios must be seen as worst case scenarios, knowing that other measures will be implemented.

Thus, except in the third scenario of the CIDRAP, all epidemiological models suggest that we shall face a second wave. Most models consider also following recurring waves.

Now, a brief comparison with the dynamics of the epidemic in China, South Korea and Singapore tend to indicate that the models and scenarios anticipating recurring waves are the most likely. It could also indicate that models are pessimistic in terms of the timing of the second wave, except in the case of Singapore. Yet, in Singapore, other factors not included in the epidemiological models are also at work. Meanwhile, the size thus lethality of the second wave remains a high impact uncertainty that must be considered carefully.

Can we thus find other factors that could help improving assessments of the coming waves? These factors would make foresight even more actionable. They would thus contribute to the design of efficient policies. This is what we shall see with the next article.

Further Bibliographical references

Taubenberger, Jeffery K, and David M Morens. “1918 Influenza: the mother of all pandemics.” Emerging infectious diseases vol. 12,1 (2006): 15-22. doi:10.3201/eid1201.050979

Featured image: Image par Elias Sch. de Pixabay [Public Domain]

The Red (Team) Analysis Weekly – 7 May 2020

This is the 7 May 2020 issue of our weekly scan for political and geopolitical risks (open access): The U.S. challenged by both the COVID-19 and China, virus mutations, economic dread, and more…

Using horizon scanning, each week, we collect weak – and less weak – signals. These point to new, emerging, escalating or stabilising problems. As a result, they indicate how trends or dynamics evolve.

The 7 May 2020 scan→

Horizon scanning, weak signals and biases

We call signals weak, because it is still difficult to discern them among a vast array of events. However, our biases often alter our capacity to measure the strength of the signal. As a result, the perception of strength will vary according to the awareness of the actor. At worst, biases may be so strong that they completely block the very identification of the signal.

In the field of strategic foresight and warning, risk management and future studies, it is the job of good analysts to scan the horizon. As a result, they can perceive signals. Analysts then evaluate the strength of these signals according to specific risks and dynamics. Finally, they deliver their findings to users. These users can be other analysts, officers or decision-makers.

You can read a more detailed explanation in one of our cornerstone articles: Horizon Scanning and Monitoring for Warning: Definition and Practice.

The sections of the scan

Each section of the scan focuses on signals related to a specific theme:

  • world (international politics and geopolitics);
  • economy;
  • science including AI, QIS, technology and weapons, ;
  • analysis, strategy and futures;
  • the Covid-19 pandemic;
  • energy and environment.

However, in a complex world, categories are merely a convenient way to present information, when facts and events interact across boundaries.

The information collected (crowdsourced) does not mean endorsement.

Featured image: Milky Way above SPECULOOS / The Search for habitable Planets – EClipsing ULtra-cOOl Stars (SPECULOOS) is searching for Earth-like planets around tiny, dim stars in front of a panorama of the Milky Way. Credit: ESO/P. Horálek.