Identifying the main drivers of transmission in the early phase of the COVID-19 pandemic in Portugal
Year: 2024
Type: article
Abstract: In this study, we employed a modeling approach to describe how changes in age-specific epidemiological characteristics, such as behaviour, i.e. contact patterns, susceptibility and infectivity, influence the basic reproduction number $$R_0$$ , while accounting for heterogeneity in transmission. We computed sensitivity measures related to $$R_0$$ , that describe the relative contribution of each age group towards overall transmission. Additionally, we proposed a new indicator that provides the expected relative change in the number of new infections, given a public health intervention. Studying the outbreak of COVID-19 in Portugal during March 2020, our results show that the main drivers of transmission were individuals 30–59 years old. Furthermore, by studying the impact of imposed changes in susceptibility and infectivity, our results demonstrate that a 10% decrease in susceptibility for the 30–39 years old results in a incidence reduction after 3 generations of approximately 17% in this age group and 4–6% reduction as an indirect effect in the remaining age groups. The presented methodology provides tools to inform the allocation strategy of mitigation measures in an outbreak of an infectious disease. Its inherent versatility enables the easy incorporation of data specific to various populations, facilitating a comparative analysis of epidemic control effects across different countries.
Source: Scientific Reports
Authors: Constantino Caetano, Leonardo Angeli, Irma Varela-Lasheras, Pietro Coletti, Luisa Morgado, Pedro Lima, Lander Willem, Baltazar Nunes, Niel Hens
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,590
Awards: Efficient and rapidly SCAlable EU-wide evidence-driven Pandemic response plans through dynamic Epidemic data assimilation, Public health decision making with stochastic individual-based models: a translational framework driven by advances in health economics, model inference and reinforcement learning (ACCELERATE), G2975900969, G434337789, Epidemic intelligence to minimize 2019-nCoV’s public health, economic and social impact in Europe, G5933744728, Centro de Matemática Computacional e Estocástica, G6997658380
OpenAlex ID: W4405841598
