ArticlePLoS computational biology2017
Dose-response relationships for environmentally mediated infectious disease transmission models.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 93 citations in OpenAlex.
- Modeling environmentally mediated rotavirus transmission: The role of temperature and hydrologic factors.Proceedings of the National Academy of Sciences of the United States of America · 2018Pooled it
- From Trait-Structured Within-Host Dynamics to SIR Models: A Multiscale Framework With Re-Exposure.Bulletin of mathematical biology · 2026Article
- Estimating Environmental Transmission Risk From Host Movement Data.Ecology and evolution · 2025Article
- A river in crisis: water quality, microbial burden, and public health implications of a South African urban river.Applied and environmental microbiology · 2025Article
- Sensitivity analysis and dynamics of brucellosis infection disease in cattle with control incident rate by using fractional derivative.Scientific reports · 2025Article
- Research gaps and priorities for quantitative microbial risk assessment (QMRA).Risk analysis : an official publication of the Society for Risk Analysis · 2024Article
- Applications of Quantitative Microbial Risk Assessment to Respiratory Pathogens and Implications for Uptake in Policy: A State-of-the-Science Review.Environmental health perspectives · 2024Review
- Combining a parsimonious mathematical model with infection data from tailor-made experiments to understand environmental transmission.Scientific reports · 2023Article
- Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies.Scientific data · 2023Article
- Leveraging infectious disease models to interpret randomized controlled trials: Controlling enteric pathogen transmission through water, sanitation, and hygiene interventions.PLoS computational biology · 2022Article
- Agroecology influencesHeliyon · 2022Article
- A mesoscale agent based modeling framework for flow-mediated infection transmission in indoor occupied spaces.Computer methods in applied mechanics and engineering · 2022Article
- Why the Spectral Radius? An intuition-building introduction to the basic reproduction number.Bulletin of mathematical biology · 2022Article
- Increased airborne transmission of COVID-19 with new variants, implications for health policies.Building and environment · 2022Article
- SARS-CoV-2 Droplet and Airborne Transmission Heterogeneity.Journal of clinical medicine · 2022Article
- Dilution of Epidemic Potential of Environmentally Transmitted Infectious Diseases for Species with Partially Overlapping Habitats.The American naturalist · 2022Article
- How ventilation behaviour contributes to seasonality in airborne disease transmission.GMS hygiene and infection control · 2022Article
- Article
- Association between meteorological indicators and COVID-19 pandemic in Pakistan.Environmental science and pollution research international · 2021Article
- Inferring environmental transmission using phylodynamics: a case-study using simulated evolution of an enteric pathogen.Journal of the Royal Society, Interface · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Environmentally mediated infectious disease transmission models provide a mechanistic approach to examining environmental interventions for outbreaks, such as water treatment or surface decontamination. The shift from the classical SIR framework to one incorporating the environment requires codifying the relationship between exposure to environmental pathogens and infection, i.e. the dose-response relationship. Much of the work characterizing the functional forms of dose-response relationships has used statistical fit to experimental data. However, there has been little research examining the consequences of the choice of functional form in the context of transmission dynamics. To this end, we identify four properties of dose-response functions that should be considered when selecting a functional form: low-dose linearity, scalability, concavity, and whether it is a single-hit model. We find that i) middle- and high-dose data do not constrain the low-dose response, and different dose-response forms that are equally plausible given the data can lead to significant differences in simulated outbreak dynamics; ii) the choice of how to aggregate continuous exposure into discrete doses can impact the modeled force of infection; iii) low-dose linear, concave functions allow the basic reproduction number to control global dynamics; and iv) identifiability analysis offers a way to manage multiple sources of uncertainty and leverage environmental monitoring to make inference about infectivity. By applying an environmentally mediated infectious disease model to the 1993 Milwaukee Cryptosporidium outbreak, we demonstrate that environmental monitoring allows for inference regarding the infectivity of the pathogen and thus improves our ability to identify outbreak characteristics such as pathogen strain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.