Cooperative epidemics can lead to major outbreaks

The meeting of two spreading epidemics can lead to disastrous and heightened effects. An example of this is the 1918 combined spreading of Spanish flu and pneumonia, which infected over 500 million people across the globe – 27% of the global population at the time.

Byungjoon Min and Claudio Castellano designed an algorithm based on the message-passing approach to study cooperative epidemics and calculate the probability of outbreaks.

The message-passing approach treats each pathogen as a message that is either passed or not passed between nodes in a network. If a node is already infected with one pathogen, its probability of being infected by both pathogens increases. Additionally, the probability that the second pathogen is transmitted to neighboring nodes grows.

“In order to predict and control realistic systems, analysis on networked systems at the level of individual node has been called for,” said Castellano. “The message-passing approach allows to accurately predict cooperative epidemics on complex networks at the level of individuals.”

Using this approach, the authors were able to identify influential nodes in cooperative epidemics. They confirmed their theoretical predictions with numerical simulations.

“In order to minimize the damage from epidemic spreading, we need to predict and control the spread of contagious diseases,” said Castellano. “The message-passing theory and its application to identify influential spreaders provide an efficient way to control epidemic spreading.”

The authors believe applying this model to systems that correspond to a population such as a city will inform a realistic prediction of the spreading of epidemics.

Source: “Message-passing theory for cooperative epidemics,” by Byungjoon Min and Claudio Castellano, Chaos (2020). The article can be accessed at https://doi.org/10.1063/1.5140813 .