Competition and collaboration: Understanding interacting epidemics can unlock better disease forecasts

Competition and collaboration: Understanding interacting epidemics can unlock better disease forecasts

A new algorithm increases scientists' abilities to accurately model mutually dependent spreading processes, from virus outbreaks to disinformation on social media

JUNE 07, 2021
SHARE

SHARE

This simulation shows optimal blocking of one the epidemics originating from the area of Leeds in the U.K., by accounting for collaboration with another epidemic process spreading from the area of Greater London. The spread through the transport on the road network is typical for livestock epidemics, as it was the case in the 2001 Foot and Mouth epidemic in the U.K.

by Andrey Lokhov

Epidemiological models took center stage throughout the COVID-19 pandemic, providing important information about the spread of the virus through communities and the world. But the spotlight on these models also illuminated their shortcomings. Early in the pandemic, several models were criticized for their lack of accuracy by either over or underestimating infection and death rates. This is understandable given that, early on, little data was available to feed these models. As the pandemic progressed and more data became available, the better they got.

But the new epidemiological models are still far from perfect. A recently developed algorithm aims to improve them by focusing on additional forces critical to spread but too often overlooked.

Until now, epidemiological models that forecast how viruses spread through populations have struggled to include concepts of collaboration among various diseases themselves that, once in the human body, increase the chance of a co-infection. For example, people living with HIV are 15 to 22 times more likely to get tuberculosis, and a person cannot contract hepatitis D unless they are already infected with hepatitis B.

Read the rest of the story as it appeared in Discover.

SUBSCRIBE TO OUR NEWSLETTER

Sign up to receive the latest news and feature stories from Los Alamos National Laboratory