Time to use effective tricks to limit social contagion of the Covid-19

Apr 22, 2020
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J. Bughin

Dr Jacques Bughin, UN consultant, Solvay Business School ULB, Portulans Institute and G20Y, former Director McKinsey Global Institute, and senior partner McKinsey & Company.

Table of Contents:

Introduction
Breaking good: some Chinese evidence
Self-induced changes: insights from incentive theory and other behavioral sciences

1. Introduction

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March 9

In previous articles (available here), I mentioned that one of the critical drivers of how Covid-19 will end up as a small epidemy, or a large pandemic, is highly dependent on the level of reproduction rate in the population, \(R_0\), as well as its dynamics, \(R_t\), through time. If this rate breaches the threshold, \(R_0>1\), and remains (long) above it, we may face a difficult pandemic issue.

Win or break is now

While I have estimated the reproduction rate for the coronavirus looks to be just above 2.7 for the Hubei region in China (60 million inhabitants), that is, one contaminated person infects 2.7 others, when fitted to daily confirmed cases across the last 10 weeks, we are just at the time when the exponential play may materially affect the time spread of the infectious disease for the region.

In China, we are currently roughly at 60,000 recorded contaminations, or roughly 0.1% of total population, and a reproduction rate of 1.5 or 3 gives 20 times less infections, but does not change the needle in the total affected population. However, let us put ourselves 6 weeks down the road. If the reproduction rates remain constant, the difference of infections between \(R_0=3\) and \(R_0=1.5\) explodes to be a 30-fold factor (see my high level simulation, in Table 1).

We thus clearly see that actions must be fast to curb the exponential threat in the first weeks of the outbreak. The plan must be executed not only be through limiting the infectious level of individuals’ contacts, but also, and more easily, through a significant reduction in the number of infectious contacts with (if any, the most at risk portion of) the population.

Table 1 - After 10 weeks the critical take off starts: Hubei region, recorded only contamined cases¹

	March 10 (12 weeks)	Early May (18 weeks)
R₀=1.5	4,500 cases	120,000 cases
R₀=2	13,000 cases	700,000 cases
R₀=2.5	40,000 cases	2,800,000 cases
R₀=3	105,000 cases	3,600,000 cases
For info, maximum likelihood data fit
R₀=2.7	60,000 cases	3,100,000 cases
Range of attacks	(>0 to 0.2% of pop)	(>0.2% to 5% of pop)

Note: My estimates, rounded figures, cycles of 2 weeks of contamination, growth \(g\) of cases computed as \(g=0.5\times \sqrt(8\times R_0)^{-1}\), where \(R_0\) is scaled down based on the flu power law of contamination; growth scaled down by portion of immune contagion

2. Breaking good: some Chinese evidence

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This is precisely what China has done, and what Italy has been launching this Sunday, by creating major quarantines, and larger set of controls of contacts within their affected regions.

The jury is still out for Italy, but this is a very high priority to make it work there, as first set of data may seem to suggest that Italy is more like what the Hubei region was in China by early January, with an average \(R_0\) that is more in the range of 4 to 6, and a fatality rate, closer to above 3% range, a rate higher than my original view expressed in my previous article.

We have more perspectives on China. Provided data reported are right, early data may seem to prove that it works, as the average \(R_0=2.7\) across the 10 weeks is in fact the result of a constant decline across the recent weeks, from \(R_0=6\) by the time the epidemy was recognized early January by the Chinese authorities, to possibly below 1, by late February (see my recent post).²

Imposing quarantines, traffic blocages, on school closure are actions of highly disruptive nature, even if rather effective. However, it is important to recognize that:

All measures must be done altogether to really put a major break. For exemple, research tends to demonstrate that about 1/3 of all contacts happen at work/school; 1/3 within communities and 1/3 in close family settings. And that typically, the average individual, at 45 years old, is impacted by the three sources of contacts.
Behavioral responses to those policies are critical to have the imposed barriers effective enough to curb outbreak and eliminate the risk of a pandemic disease.³ ⁴

We are thus in need to look as well as self-induced behavior changes.

3. Self-induced changes: insights from incentive theory and other behavioral sciences

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Further to policies enforcement, there should be indeed more subtle cases to push people to change their own behavior. Here are three examples that have proven effective in battling epidemics:

Divert to virtual contacts. In the case of the current China outbreaks, digital is playing an important role, both to spot spreaders, to reduce asymetric information among cohorts, by color coding citizens in terms of their degree of transmitting their illness but also by releasing new and attractive digital entertainment and online social applications that let some Chinese people to privilege the use of more digital life styles, instead of face-to-face social interactions.⁵

Build the right SoCoMo awareness. When a disease breaks out, one may hope that awareness may lead people not only to protect themselves, but also take measures to reduce their susceptibility.

Alas, in a well-mixed population, the epidemic threshold is not changed dramatically by national campaigns, because the outbreak is usually happening in a much more localised manner before it broadens to multiple groups. SoCoMo (social, local, and mobile) tools seem to be much more effective to target and localise behavioral response in the proximity of an outbreak, and may thus seem to be much better at curb the outbreak of a disease and if individuals networks face a high-level of clustering.

Mathematical simulations show that localized and peers media can significantly reduce the frequency of contacts, and that this peer media effect has likley been a driving cause of curbing the outbreak of SARS in China by 2009.⁶ This has been confirmed in the case study of the SARS epidemic in Taiwan, where local social learning effects seem to lead to a reduction of up to 30% of contacts, in that case, study related to doctor visits.⁷

Make risks tangible. A large set of studies has confirmed that both reduction of contacts and use of other precautionary behaviors tend to larger among indiviuduals who worried about someone close, either as close friend, or in their household contracting the disease.⁸

© Jacques Bughin. Written March 09. Comments more than welcome. All errors are mine. References listed as they are found in the text

Those numbers are likely a fraction of total infection, this means that under larger figure the dynamics are even more skewed agaisnt large \(R_0\). ↩
See also Sun, et al. 2020, Tracking and Predicting COVID-19 Epidemic in China Mainland, medRxiv; who essentially come to the same conclusions as mine. ↩
See Li, et al. 2020, Estimating the Efficacy of Traffic Blockage and Quarantine for the Epidemic Caused by 2019-nCoV (COVID-19), medRxiv. ↩
Note: The broad use of masks by more than 75% of the Wuhan population was also driven by police requirements, but this rather plays on the effectiveness of contacts; see Qian, et al. 2020, Psychological responses, behavioral changes and public perceptions during the early phase of the COVID-19 outbreak in China: a population based cross-sectional survey, medRxiv. ↩
[https://www.bloomberg.com/press-releases/2020-03-04/westwin-research-shows-how-chinese-consumer-behavior-changed-as-a-result-of-the-coronavirus-covid-19-outbreak] (https://www.bloomberg.com/press-releases/2020-03-04/westwin-research-shows-how-chinese-consumer-behavior-changed-as-a-result-of-the-coronavirus-covid-19-outbreak). ↩
See Funk et al, 2009, The spread of awareness and its impact on epidemic outbreaks, Proceedings of the National Academy of Sciences of the United States of America. ↩
see Bennett, D., Chiang, C.F. and Malani, A., 2015, Learning during a crisis: The SARS epidemic in Taiwan, Journal of Development Economics. ↩
Kim et al., 2009, Public Risk Perceptions and Preventive Behaviors During the 2009 H1N1 Inﬂuenza Pandemic, Disaster Medicine and Public Heath. ↩