models of COVID-19

How epidemiological models of COVID-19 help us estimate the true number of infections

by Charlie Giattino August 24, 2020

The main publication on the pandemic is here: Coronavirus Pandemic (COVID-19).

We are grateful to the researchers whose work we cover in this post for giving helpful feedback and suggestions. Thank you. Reuse our work freely

A key limitation in our understanding of the COVID-19 pandemic is that we do not know the true number of infections. Instead, we only know of infections that have been confirmed by a test – the confirmed cases. But because many infected people never get tested,1 we know that confirmed cases are only a fraction of true infections. How small a fraction though?

To answer this question, several research groups have developed epidemiological models of COVID-19. These models use the data we have – confirmed cases and deaths, testing rates, and more – plus a range of assumptions and epidemiological knowledge to estimate true infections and other important metrics.

The chart here shows the mean estimates of the true number of daily new infections in the United States from four of the most prominent models.2 For comparison, the number of confirmed cases is also shown.

• Imperial College London (ICL)
• The Institute for Health Metrics and Evaluation (IHME)
• Youyang Gu (YYG)
• The London School of Hygiene & Tropical Medicine (LSHTM)

Two things are clear from this chart: All four models agree that true infections far outnumber confirmed cases. But the models disagree by how much, and how infections have changed over time.

When the number of confirmed cases in the US peaked in late July, the IHME and LSHTM models estimated that the true number of infections was about twice as high as confirmed cases, the ICL model estimated it was nearly three times as high, and Youyang Gu’s model estimated it was more than six times as high. Back in March the estimated discrepancy between confirmed cases and true infections was even many times higher.

In this post we examine these four models and how they differ by unpacking their essential elements: what they are used for, how they work, the data they are based on, and the assumptions they make.

We also aim to make the model estimates easily accessible in our interactive charts, allowing you to quickly explore different models of the pandemic for most countries in the world. To do this simply click “Change country” at the bottom of each chart.

Three of the four models we look at are “SEIR”3 models,4 which simulate how individuals in a population move through four states of a COVID-19 infection: being Susceptible, Exposed, Infectious, and Recovered (or deceased). How individuals move through these states is determined by different model “parameters,” of which there are many. Two key ones are the effective reproduction number (Rt)5 – how many other people a person with COVID-19 infects at a given time – and the infection fatality rate (IFR) – the percent of people infected with a disease who die from it.

You can learn more about how SEIR models work by exploring these resources:

• Youyang Gu’s Model Details (for a brief read)
• COVID Act Now’s COVID Data 101: What is an SEIR model? (for a brief video)
• Bruno Gonçalves’s Epidemic Modeling 102: All CoVID-19 models are wrong, but some are useful (for a more in-depth read)