How will projection parameters vary across country characteristics?

As we consider how the trajectory of the pandemic can vary across the world, for example between high income and lower income countries, a few variables are likely to matter.

First, the age-specific infection fatality ratios (IFR) and the number of deaths per people infected could vary across countries. Because this is a new disease, we have only limited data available. Most projection models currently use estimates from China, since this is an outbreak that, for now, seems to have run its course and extensive data has been collected (see for example the estimates from Verity et al. 2020). Of course, nothing guarantees that the age-specific IFRs will be the same across settings and the caveats I made earlier about how infections and deaths are counted apply to the available estimates. For example, Eran Bendavid and Jay Bhattacharya have warned that the current numbers for the IFR might be substantially overestimated because of the strong selection bias in testing.

What seems clear is that the IFR varies greatly with age as older people are at much higher risk of dying. Since countries’ age structures vary considerably, any reasonable projection needs to take them into account. They are easy to obtain from the UN World Population Prospects site. Given the younger average age distribution of populations in developing countries, projections will usually predict a lower incidence of severe disease, hospitalization and deaths in those contexts. But it is important to be careful here, because it is not yet clear whether the higher morbidity and mortality among old people mainly comes from the fact that older people have other health conditions or from the fact that immune systems generally weaken with age. It is probably a combination of both. The data from China shows that mortality rates increase steeply with age but are also strongly associated with comorbidities (preexisting conditions such as cardiovascular disease, diabetes, chronic respiratory disease, hypertension and cancer).

The rate of social mixing between people and crucially the rate of mixing across generations will also vary across countries and is expected to be higher in the developing world, in part because it is quite frequent there to have several generations living in the same household. The rate of social mixing determines what epidemiologists call R0, the basic reproduction number, i.e. the average number of people infected by each infected person. It is a crucial parameter in any SIR model, and it is the number that containment and physical distancing measures try to reduce. There is still quite a lot of uncertainty about estimates of R0 for COVID-19, but, in the absence of any mitigation measure, it is currently estimated to be between 2.4 and 3.3. Most epidemiological projection tools allow to vary R0, both the baseline number and to account for containment measures.

Finally, hospital, especially intensive care unit (ICU) capacity and overall quality of care varies widely across countries. This is also a key parameter, in particular to project the number of deaths, because a substantial part of the mortality is expected to occur if and when the number of cases in need of (intensive) care will exceed existing capacity and overwhelm the health systems.