ArticlePloS one2021
Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Tracking Dynamics of Superspreading Through Contacts, Exposures, and Transmissions in Edge-Based Network Epidemics.Bulletin of mathematical biology · 2026Article
- Estimating the effect of self-protection on transmission dynamics of SARS-CoV-2 in Germany in 2021: a modelling study.BMC public health · 2026Article
- Unifying non-Markovian dynamics and agent heterogeneity in scalable stochastic networks.Nature communications · 2026Article
- Host heterogeneity and unpredictability in parasite outbreaks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A systematic approach to classifying and evaluating heterogeneity measures.Royal Society open science · 2025Article
- Hotspot model shows how location-based superspreading accelerates and reshapes epidemics.PNAS nexus · 2025Article
- Changes in social contact patterns in Germany during the SARS-CoV-2 pandemic - an analysis based on the COVIMOD study.BMC infectious diseases · 2025Article
- Transmission matrix parameter estimation of COVID-19 evolution with age compartments using ensemble-based data assimilation.PloS one · 2025Article
- Agent-based modeling to estimate the impact of lockdown scenarios and events on a pandemic exemplified on SARS-CoV-2.Scientific reports · 2024Article
- An algorithm to build synthetic temporal contact networks based on close-proximity interactions data.PLoS computational biology · 2024Article
- Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model.Scientific reports · 2023Article
- Heterogeneity is a key factor describing the initial outbreak of COVID-19.Applied mathematical modelling · 2023Article
- Inference in epidemiological agent-based models using ensemble-based data assimilation.PloS one · 2022Article
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Authors and funding
3 authors.
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Abstract
In the recent COVID-19 pandemic, mathematical modeling constitutes an important tool to evaluate the prospective effectiveness of non-pharmaceutical interventions (NPIs) and to guide policy-making. Most research is, however, centered around characterizing the epidemic based on point estimates like the average infectiousness or the average number of contacts. In this work, we use stochastic simulations to investigate the consequences of a population's heterogeneity regarding connectivity and individual viral load levels. Therefore, we translate a COVID-19 ODE model to a stochastic multi-agent system. We use contact networks to model complex interaction structures and a probabilistic infection rate to model individual viral load variation. We observe a large dependency of the dispersion and dynamical evolution on the population's heterogeneity that is not adequately captured by point estimates, for instance, used in ODE models. In particular, models that assume the same clinical and transmission parameters may lead to different conclusions, depending on different types of heterogeneity in the population. For instance, the existence of hubs in the contact network leads to an initial increase of dispersion and the effective reproduction number, but to a lower herd immunity threshold (HIT) compared to homogeneous populations or a population where the heterogeneity stems solely from individual infectivity variations.
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