Objective:

In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to guide decision making and inform the public health response.

Findings:

Impacts of other infectious diseases historically and current models of COVID-19 in LMIC suggest there will be a considerable difference in mortality between high-income and low-income countries. For instance, mortality rates from the 1918–1920 Spanish flu pandemic were significantly higher in Asia, Sub-Saharan Africa, and Latin America compared with North America and Europe. To date, there are no models for COVID-19 in Latin America despite the shift in the epicentre to South America. However, there have been a limited number of modelling studies for Africa that provide a framework for modelling COVID-19 more broadly for LMIC. For example, one study used synthetic contact matrices, shifted the age-specific probability of becoming severely infected (compared with estimates from high-income countries), and simulated community led interventions such as neighbourhood house swaps. The results indicated that an unmitigated epidemic would lead to millions of clinical cases (e.g., 4.1 million during the first year of the epidemic in Niger) and that interventions would only confer partial protection.