Epidemics of infectious disease often come in waves, but the causes of these waves aren’t clear, frustrating efforts to predict or mitigate them. Are waves of infection caused by transmission seasonality, viral mutations, implementation of public health interventions, or something else? Claus Kadelka and colleagues model how human behavior, in response to information about disease risk, can create waves. There is frequently a lag between infection prevalence and the information about that prevalence reaching the public. Once the information reaches the public, that information may motivate masking and social distancing. Incorporating this lag into models can produce multi-wave dynamics, as infection spreads rapidly before the public takes countermeasures, then is dampened until the public relaxes its behavioral response. The authors note that their model does not include factors, including the severity of the disease, “epidemic fatigue,” or economic constraints, nor does it capture the complexities of delays in information availability, including media fatigue with epidemic stories. According to the authors, the simple version of the behavioral model nevertheless could help explain recurrent patterns seen in real-world epidemics, such as early stages of the COVID-19 epidemic in the United States, and underscores the importance of integrating social and operational factors into infectious disease models.
PNAS Nexus
Adaptive human behavior and delays in information availability autonomously modulate epidemic waves
27-May-2025