ArticleTheoretical biology & medical modelling2009
A mechanistic model of infection: why duration and intensity of contacts should be included in models of disease spread.
Article in Theoretical biology & medical modelling, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Social Contact Surveys to Inform Transmission Models of Close-contact Infections.Epidemiology (Cambridge, Mass.) · 2019Pooled it
- Household crowding, social mixing patterns and respiratory symptoms in seven countries of the African meningitis belt.PloS one · 2014Trial
- REPLAY: a GPU-accelerated tool for temporal contact network epidemiology.BMC medical informatics and decision making · 2025Article
- Social contact patterns in South Korea: an analysis of a survey conducted in 2023-2024.BMC infectious diseases · 2025Article
- Article
- Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model.Scientific reports · 2023Article
- Contact patterns of UK home delivery drivers and their use of protective measures during the COVID-19 pandemic: a cross-sectional study.Occupational and environmental medicine · 2023Article
- A combined optimization-simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities.Journal of air transport management · 2023Article
- Modelling the dynamic relationship between spread of infection and observed crowd movement patterns at large scale events.Scientific reports · 2022Article
- Estimated reproduction ratios in the SIR model.The Canadian journal of statistics = Revue canadienne de statistique · 2021Article
- Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas.Scientific reports · 2021Article
- Social contact patterns among employees in 3 U.S. companies during early phases of the COVID-19 pandemic, April to June 2020.Epidemics · 2021Article
- Home alone? Effect of weather-induced behaviour on spread of SARS-CoV-2 in Germany.Economics and human biology · 2021Article
- Trip duration drives shift in travel network structure with implications for the predictability of spatial disease spread.PLoS computational biology · 2021Article
- Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections.PLoS computational biology · 2021Article
- Digital proximity tracing on empirical contact networks for pandemic control.Nature communications · 2021Article
- Analytical approach to solve the problem of aircraft passenger boarding during the coronavirus pandemic.Transportation research. Part C, Emerging technologies · 2021Article
- Estimating density limits for walking pedestrians keeping a safe interpersonal distancing.Scientific reports · 2021Article
- The relationship between cyclonic weather regimes and seasonal influenza over the Eastern Mediterranean.The Science of the total environment · 2021Article
- Modeling epidemic spreading through public transit using time-varying encounter network.Transportation research. Part C, Emerging technologies · 2021Article
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1 author.
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Abstract
backgroundMathematical models and simulations of disease spread often assume a constant per-contact transmission probability. This assumption ignores the heterogeneity in transmission probabilities, e.g. due to the varying intensity and duration of potentially contagious contacts. Ignoring such heterogeneities might lead to erroneous conclusions from simulation results. In this paper, we show how a mechanistic model of disease transmission differs from this commonly used assumption of a constant per-contact transmission probability.
methodsWe present an exposure-based, mechanistic model of disease transmission that reflects heterogeneities in contact duration and intensity. Based on empirical contact data, we calculate the expected number of secondary cases induced by an infector (i) for the mechanistic model and (ii) under the classical assumption of a constant per-contact transmission probability. The results of both approaches are compared for different basic reproduction numbers R0.
resultsThe outcomes of the mechanistic model differ significantly from those of the assumption of a constant per-contact transmission probability. In particular, cases with many different contacts have much lower expected numbers of secondary cases when using the mechanistic model instead of the common assumption. This is due to the fact that the proportion of long, intensive contacts decreases in the contact dataset with an increasing total number of contacts.
conclusionThe importance of highly connected individuals, so-called super-spreaders, for disease spread seems to be overestimated when a constant per-contact transmission probability is assumed. This holds particularly for diseases with low basic reproduction numbers. Simulations of disease spread should weight contacts by duration and intensity.
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