Key questions for modelling COVID-19 exit strategies

Abstract

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute ‘Models for an exit strategy’ workshop (11–15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Will an outbreak exceed available resources for control? Estimating the risk from invading pathogens using practical definitions of a severe epidemic

“…Practical use of the approaches that we have developed might also require the wide range of interventions that are introduced in outbreak responses to be integrated into the models explicitly. One way in which control can be included is to consider the effective reproduction number when the pathogen arrives in the system instead of the basic reproduction number, since the effective reproduction number accounts for interventions [26,[81][82][83][107][108][109]. In that situation, the results that we presented would be unchanged (except that e.g.…”

“…However, for detailed descriptions of control to be included in estimates of the severe epidemic risk, more complex interventions should be included in model simulations. Since models are often used to test possible control strategies [7,12,20,45,81,[114][115][116], this is a simple extension of the results presented here.…”

“…For example, if the definition of a severe epidemic is linked to the availability of beds in treatment centres (as may be the case when the ‘concurrent size’ metric is used), then infected individuals in treatment centres could be included in the model explicitly (for an example in which we consider three different models of an Ebola epidemic with different levels of complexity, see electronic supplementary material, text S5). Other definitions of severe epidemics could be used, potentially considering factors such as access to healthcare; limited healthcare access is a particular challenge in low resource settings [ 81 ]. It would also be possible to require multiple criteria to be satisfied for an outbreak to be classified as a severe epidemic.…”

“…for a single value of M ). The parameter regime in which R 0 is close to one is important in many epidemiological systems since the aim of pre-emptive control strategies is often to reduce R 0 below one (or, when an outbreak has started, to reduce the time-varying or effective reproduction number below one [81][82][83][84]).…”

The bumpy road to achieve herd immunity in COVID-19

“…[20] Deciphering populational immunity in different regions and countries for this type of infection, we must gather information on population heterogeneity, transmission and the accumulation of herd immunity. [21] Nevertheless, there are still many hurdles in achieving a proper efficient vaccine against the COVID-19 disease. Herd immunity in infectious diseases.…”