Laboratory

Population Dynamics and Sustainable Well-Being

Project

The Interplay Between Population and Infectious Disease Dynamics

Daniela Perrotta, Jordan Klein, Emanuele Del Fava; in Collaboration with Alessia Melegaro (Bocconi University, Milan, Italy), Daniela Paolotti (ISI Foundation, Institute for Scientific Interchange, Torino, Italy)

Detailed Description

Infectious diseases have long posed a major threat to humankind. Throughout history, we have repeatedly witnessed their emergence (or re-emergence), which has sometimes resulted in devastating epidemics. The Ebola outbreak from 2014 to 2016, the Zika outbreak from 2015 to 2016, and the COVID-19 pandemic are only a few examples. Understanding and predicting the spatiotemporal dynamics of disease spread, as well as the social and behavioral responses shaping them, are therefore essential for ensuring effective epidemic responses and evidence-based decision-making. At the same time, epidemiology has undergone a methodological revolution driven by the availability of large-scale digital data. This project leverages new data collection schemes, digital trace and climate data, and mathematical and computational models to generate new insights into infectious disease dynamics.

One line of research this project focuses on is how population and social structures shape infectious disease transmission and impact. This includes research that investigates relationships between changing population structures and social contact patterns using primary data collection schemes (Del Fava et al. 2021). We have also examined the relationship between excess mortality and human mobility using mobile phone data, analyzing the efficacy of public health interventions in curbing the spread of SARS-CoV-2 in the United Kingdom (Basellini et al. 2021). Building on these approaches, recent work integrates frameworks from social epidemiology, including fundamental cause theory and related perspectives that conceptualize social conditions as fundamental drivers of disease, and uses spatially explicit epidemiological models to examine how structural inequalities shape the spatiotemporal evolution of infectious disease disparities. This approach has been applied to Brazil to study how differences in population vulnerability during the COVID-19 pandemic influenced epidemic trajectories and mortality inequalities (Klein 2025). Looking forward, we plan to study climate-sensitive infectious diseases, particularly vector-borne diseases, using mechanistic modeling approaches to identify drivers of inequalities in vulnerability and health outcomes, and using potential adaptation measures to address these inequalities in a changing climate.

A complementary line of research focuses on the behavioral mechanisms that shape infectious disease dynamics. Drawing on established theories of health-related behaviors, such as the health belief model, we used online survey data to assess behavioral changes of individuals in response to infectious diseases, including influenza-like illness (Gozzi et al. 2020) and COVID-19 (De Gaetano et al. 2023). Within this framework, vaccination behavior represents a key and policy-relevant outcome of these adaptive processes. Using longitudinal data from the United Kingdom, we show that COVID-19 vaccination decisions are strongly influenced by household context: Individuals living with vaccine-skeptical family members were significantly less likely to be vaccinated, with evidence of reinforcing “echo chamber” effects, particularly among children (Leone et al, 2026).

Extending this approach to cross-national settings, we examine how structural factors and individual perceptions interact to produce variation in vaccination behaviors. By combining primary data collection with comparative analyses and integrating behavioral insights with epidemiological modeling frameworks, we aim to contribute to data-driven insights into behavioral responses to infectious diseases, and to improve our ability to understand and predict how human behavior influences disease transmission and the effectiveness of public health interventions.

Looking ahead, this research program aims to integrate demographic processes, social structure, behavioral responses, and environmental change into statistical and mechanistic models of infectious disease dynamics. By bringing these dimensions together, we seek to better understand how interacting social and behavioral forces shape disease dynamics and disparate population health outcomes under conditions of demographic and climatic change.

Decrease of excess mortality rates in London following reductions in human mobility during the early phase of the COVID-19 pandemic.

The relationship between excess mortality and changes in human mobility (left) reveals the projected estimates of deaths saved (right) in London thanks to the public health interventions enforced during the first wave of the COVID-19 pandemic in 2020. © Basellini, U.; Alburez-Gutierrez, D.; Del Fava, E.; Perrotta, D.; Bonetti, M.; Camarda, C. G.; Zagheni, E. Linking excess mortality to mobility data during the ?rst wave of COVID-19 in England and Wales SSM-Population Health 14, 1–18. (2021)

Research Keywords:

Data and Surveys, Health Care, Public Health, Medicine, and Epidemiology

Region keywords:

World

Links:

The COVID-19 Health Behavior Survey