General model for coupled infectious diseases clarifies how COVID synchronizes with the flu

Interactions between different diseases pose questions about how to model and predict disease spread. This paper is particularly pronounced when diseases, such as COVID-19 and the seasonal flu, have symptoms that can be difficult to distinguish. These scenarios increase the complexity of the models with equations that don’t present analytical solutions.

A pair of researchers developed a general model for two coupled infectious diseases. By analyzing three scenarios for how infections interact and generalizing them to non-symmetric cases of COVID-19 and seasonal influenza, Rodriguez and Eguiluz found that coupling between diseases leads to their synchronization.

“Other works on interacting diseases have focused on the final number of affected individuals or on the minimum infectivity an infection needs to spread in a population,” said author Jorge Rodriguez. “In this work, we study the delay between the peak of one infection and the peak of the other, showing that both in cooperative and competitive scenarios, the peaks approach, implying a higher strain on healthcare systems.”

The group simulated the interaction between diseases through both competitive and cooperative mechanisms of spreading. Such coupling had a stronger effect on the dynamics of influenza, reducing durations of flu seasons by as much as one-half in some cases.

Cooperative coupling tended to cause diseases to synchronize earlier, whereas competitive coupling led to later synchronization. The delayed synchronization, with lower peak incidences due to cross-immunity effects, would likely result in less strain on the healthcare facilities.

The group hopes to expand their work to address the interaction between more than two diseases.

Source: “Coupling between infectious diseases leads to synchronization of their dynamics,” by Jorge P. Rodríguez and Victor M Eguíluz, Chaos (2022). The article can be accessed at https://doi.org/10.1063/5.0137380 .