Through forecasting the disease burden and comparing intervention strategies, modelling has been a key part of the public policy response to the COVID-19 pandemic.
Governments across the world have justified implementing policies based on science, data, and information gleaned from these models. However, as we have learned through previous outbreaks, the science of modelling/forecasting an epidemic can be uncertain.
Policies adopted by governments due to disease forecasting will have wide-ranging consequences—not only on the epidemic. Without appropriately considering this uncertainty, understanding model limitations, and contextualising local characteristics, policymakers might misuse models.
Before COVID-19, the 2014-2015 West African Ebola epidemic (EVD epidemic), was one of the most heavily modelled outbreaks in history. Within the first two months of the COVID-19 pandemic, 31 mathematical models were developed. Despite the clear differences in the two outbreaks, the EVD epidemic can help us draw lessons to improve COVID-19 modelling and its reach in policymaking. This article discusses some of these learnings.