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Risk-mediated dynamic regulation of effective contacts de-synchronizes outbreaks in metapopulation epidemic models.

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Title: Risk-mediated dynamic regulation of effective contacts de-synchronizes outbreaks in metapopulation epidemic models.
Authors: Zunker, Henrik1 henrik.zunker@dlr.de, Dönges, Philipp2,3, Lenz, Patrick1, Contreras, Seba1,2,3 seba.contreras@ds.mpg.de, Kühn, Martin J.1,4 martin.kuehn@dlr.de
Source: Chaos, Solitons & Fractals. Oct2025:Part 2, Vol. 199, pN.PAG-N.PAG. 1p.
Subjects: Epidemiological models, Metapopulation (Ecology), Epidemics, Geospatial data, Infectious disease transmission, Ordinary differential equations, COVID-19, Resource allocation
Geographic Terms: Germany
Abstract: Metapopulation epidemic models help capture the spatial dimension of infectious disease spread by dividing heterogeneous populations into separate but interconnected communities, represented by nodes in a network. In the event of an epidemic, an important research question is, to what degree is the spatial information (i.e., regional or national) relevant for mitigation and (local) policymakers? This study investigates the impact of different levels of information on nationwide epidemic outcomes, modeling the reaction to the measured hazard as a feedback loop reducing contact rates in a metapopulation model based on ordinary differential equations (ODEs). Using COVID-19 and high-resolution mobility data for Germany of 2020 as a case study, our model revealed two markedly different regimes depending on the maximum contact reduction. In the first regime of (modest) mitigation , gradually increasing maximum contact reduction from zero to moderate levels delayed and spread out the onset of infection waves while gradually reducing the peak values. This effect was more pronounced when the contribution of regional information was low relative to national data. In the opposite suppression regime, the feedback-induced contact reduction is strong enough to extinguish local outbreaks and decrease the mean and variance of the peak day distribution, thus regional information was more important. When suppression or elimination is impossible, ensuring local epidemics are desynchronized helps to avoid hospitalization or intensive care bottlenecks by reallocating resources from less-affected areas. • Metapopulation model with dynamic contact regulation on different spatial scales. • Dynamic contact regulation yields two regimes: disease mitigation and suppression. • Modest regulation (mitigation) desynchronizes and delays the onset of epidemic waves. • High regulation (suppression) extinguishes local outbreaks but synchronizes them. • Mitigation benefits from national data; suppression, from regional data. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:Metapopulation epidemic models help capture the spatial dimension of infectious disease spread by dividing heterogeneous populations into separate but interconnected communities, represented by nodes in a network. In the event of an epidemic, an important research question is, to what degree is the spatial information (i.e., regional or national) relevant for mitigation and (local) policymakers? This study investigates the impact of different levels of information on nationwide epidemic outcomes, modeling the reaction to the measured hazard as a feedback loop reducing contact rates in a metapopulation model based on ordinary differential equations (ODEs). Using COVID-19 and high-resolution mobility data for Germany of 2020 as a case study, our model revealed two markedly different regimes depending on the maximum contact reduction. In the first regime of (modest) mitigation , gradually increasing maximum contact reduction from zero to moderate levels delayed and spread out the onset of infection waves while gradually reducing the peak values. This effect was more pronounced when the contribution of regional information was low relative to national data. In the opposite suppression regime, the feedback-induced contact reduction is strong enough to extinguish local outbreaks and decrease the mean and variance of the peak day distribution, thus regional information was more important. When suppression or elimination is impossible, ensuring local epidemics are desynchronized helps to avoid hospitalization or intensive care bottlenecks by reallocating resources from less-affected areas. • Metapopulation model with dynamic contact regulation on different spatial scales. • Dynamic contact regulation yields two regimes: disease mitigation and suppression. • Modest regulation (mitigation) desynchronizes and delays the onset of epidemic waves. • High regulation (suppression) extinguishes local outbreaks but synchronizes them. • Mitigation benefits from national data; suppression, from regional data. [ABSTRACT FROM AUTHOR]
ISSN:09600779
DOI:10.1016/j.chaos.2025.116782