COVID-19: ModelingHow to Model a Pandemic

By Christian Yates

Published 26 March 2020

There is, however, a little known but highly successful field of science working in the background to unpick the mysteries of infectious disease. As I explore in The Maths of Life and Death, mathematical epidemiology is playing a crucial role in the fight against large-scale infectious diseases such as COVID-19.

Disease has afflicted humans ever since there have been humans. Malaria and tuberculosis are thought to have ravaged Ancient Egypt more than 5,000 years ago. From AD 541 to 542 the global pandemic known as “the Plague of Justinian” is estimated to have killed 15–25% of the world’s 200-million population. Following the Spanish conquest of Mexico, the native population dropped from around 30 million in 1519 to just three million 50 years later. Today we are battling to control the spread of COVID-19, which has the potential to cause the most deadly pandemic in human history.

There is, however, a little known but highly successful field of science working in the background to unpick the mysteries of infectious disease. As I explore in The Maths of Life and Death, mathematical epidemiology is playing a crucial role in the fight against large-scale infectious diseases such as COVID-19.

With basic mathematical models, researchers can begin to forecast the progression of diseases and understand the effect of interventions on disease spread. With more complex models, we can start to answer questions about how to efficiently allocate limited resources or tease out the consequences of public health interventions, like closing pubs and banning gatherings.

Insights from mathematical modelling are vital to ensuring that authorities can prevent as many deaths as possible. As the COVID-19 pandemic escalates, here’s a look inside the modelling that experts use to try and stay one step ahead of the virus.

The S-I-R Model
One of the simplest mathematical models of disease spread splits the population into three basic categories according to disease status. People who have not yet had the disease are labelled “susceptibles”. Everyone is assumed to be born susceptible and capable of being infected. Those who have contracted the disease and are capable of passing it to susceptibles are the “infectives”. The third group are euphemistically referred to as the “removed” class. These are the people who have had the disease and recovered and are now immune, or those who have died. These “removed” individuals no longer contribute to the spread of the disease.

This is referred to as the S-I-R model. From dengue fever in Latin America to swine fever in the Netherlands and norovirus in Belgiumthe S-I-R model can provide vital lessons for how to prevent diseases spreading.