Impact of inter-city interactions on disease scaling
Nathalia A. Loureiro, Camilo R. Neto, Jack Sutton, Matjaz Perc,, Haroldo V. Ribeiro

TL;DR
This study models how inter-city interactions, via commuting networks, influence infectious disease scaling in Brazilian cities, revealing complex relationships between population, connectivity, and disease spread.
Contribution
It introduces a novel scaling framework incorporating commuting data to better predict disease incidence across cities, outperforming traditional models.
Findings
Disease cases scale variably with population and commuters.
Growth in small cities can reduce disease cases under certain conditions.
Larger cities face increased disease spread due to socioeconomic factors.
Abstract
Inter-city interactions are critical for the transmission of infectious diseases, yet their effects on the scaling of disease cases remain largely underexplored. Here, we use the commuting network as a proxy for inter-city interactions, integrating it with a general scaling framework to describe the incidence of seven infectious diseases across Brazilian cities as a function of population size and the number of commuters. Our models significantly outperform traditional urban scaling approaches, revealing that the relationship between disease cases and a combination of population and commuters varies across diseases and is influenced by both factors. Although most cities exhibit a less-than-proportional increase in disease cases with changes in population and commuters, more-than-proportional responses are also observed across all diseases. Notably, in some small and isolated cities,…
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Taxonomy
TopicsCOVID-19 epidemiological studies
