Data informed epidemiological-behavioural modelling

TL;DR

This paper integrates social science data into epidemiological models to better understand how awareness and behaviors influence disease spread, emphasizing the importance of data-driven approaches for effective epidemic mitigation.

Contribution

It introduces a data-informed behavioral-epidemiological model that combines empirical social data with classical models to analyze behavioral responses and policy impacts.

Findings

Awareness and information spreading influence behavioral responses.

Centralized regulations can effectively mitigate epidemics.

Data-driven models improve understanding of social-epidemiological dynamics.

Abstract

Augmenting classical epidemiological models with information from the social sciences helps unveil the interplay between contagion dynamics and social responses. However, multidisciplinary integration of social analysis and epidemiological modelling is often challenging, due to scarcity of vast and reliable data sources and because ad hoc modelling assumptions may not reproduce empirically observed patters. Here, we test the hypothesis that awareness and information spreading straightforwardly translates into behavioural responses, analysing empirical data to generate insights about their dynamics and relationships. We employ such results to build a data-informed behavioural-epidemiological model that elucidates the impact of compliant behaviours and the role of centralised regulations in mitigating epidemics. We investigate model properties and its benefits in integrating theoretical modelling and data.