Imperial College COVID-19 response team country-level projection results

On March 26, the Imperial College COVID-19 response team released a paper projecting the global impact of the COVID-19 pandemic under different strategies for mitigation and suppression.

The data sources appendix links to an Excel table including the projections results for most countries under 5 scenarios:

1. An unmitigated epidemic – a scenario in which no action is taken.

2. Mitigation including population-level social distancing – assessing the maximum reduction in the scale of the epidemic that can be achieved through a uniform reduction in the rate of social mixing, short of complete suppression.

3. Mitigation including enhanced social distancing of the elderly – the same as scenario 2 but with individuals aged 70 years old and above reducing their social contact rates by 60%.

4. Suppression, i.e. the implementation of wide-scale intensive social distancing (modeled as a 75% reduction in interpersonal contact rates) with the objective of rapidly suppressing transmission and minimize near-term cases and deaths. They explore different epidemiological triggers (deaths per 100,000 population) for initiating the suppression strategy:

o Scenario 4a: Suppression triggered at 1.6 deaths per 100,000 population per week

o Scenario 4b: Suppression triggered at 0.2 deaths per 100,000 population per week

Globally, they estimate that a completely unmitigated COVID-19 epidemic would lead to 7 billion infections for a R0 of 3.0 (range 2.4-3.3). Applying estimates of the age-specific IFRs from China, this could result in a staggering 40 million deaths (range 35-42).

The figure below displays the estimated number of deaths globally under the five scenarios considered.

The estimates from the Imperial College COVID-19 response team account for the age structure of the population of each country. They have also estimated and modeled the rates of social mixing and contacts across generations using existing household surveys, but because such surveys were not available for each country, they have used an estimate from a country in each region and applied it to the other countries in the same region. Data on the number of hospital beds per 1,000 population were taken from the World Bank Development Indicators for 201 countries but, since many of those numbers were not recent, they used a boosted regression tree-based modelling approach to generate current estimates of hospital beds per 1,000 population. Intensive Care Unit Capacity estimates have been derived from systematic reviews. The published data was sparse and yielded a total of 57 data points describing the number of ICU beds per 100 hospital beds across countries belonging to the World Bank’s 4 income strata (LIC, LMIC, UMIC and HIC).

Beyond the five scenarios projected, the Imperial College Excel table allows varying the baseline R0 and the intensity of social distancing measures.