ArticleTropical medicine and infectious disease2021
Analytical Estimation of Data-Motivated Time-Dependent Disease Transmission Rate: An Application to Ebola and Selected Public Health Problems.
Article in Tropical medicine and infectious disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Robust Inverse Reconstruction of Time-Varying Transmission Rates Across Model Structures and Incidence Forms.Bulletin of mathematical biology · 2026Article
- Pig carcass decomposition dynamics: Insights into carcass disposal for emergency animal disease management.Australian veterinary journal · 2025Article
- A novel indicator in epidemic monitoring through a case study of Ebola in West Africa (2014-2016).Scientific reports · 2024Article
- Phylogenetic inference of inter-population transmission rates for infectious diseases.Briefings in bioinformatics · 2024Article
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Authors and funding
6 authors.
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Abstract
Obtaining reasonable estimates for transmission rates from observed data is a challenge when using mathematical models to study the dynamics of ?infectious? diseases, like Ebola. Most models assume the transmission rate of a contagion either does not vary over time or change in a fixed pre-determined adhoc ways. However, these rates do vary during an outbreak due to multitude of factors such as environmental conditions, social behaviors, and public-health interventions deployed to control the disease, which are in-part guided by changing size of an outbreak. We derive analytical estimates of time-dependent transmission rate for an epidemic in terms of either incidence or prevalence using a standard mathematical SIR-type epidemic model. We illustrate applicability of our method by applying data on various public health problems, including infectious diseases (Ebola, SARS, and Leishmaniasis) and social issues (obesity and alcohol drinking) to compute transmission rates over time. We show that time-dependent transmission rate estimates can have a large variation, depending on the type of available data and other epidemiological parameters. Time-dependent estimation of transmission rates captures the dynamics of the problem better and can be utilized to understand disease progression more accurately.
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