ArticleEpidemics2022
Structural identifiability of compartmental models for infectious disease transmission is influenced by data type.
Article in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Identifiability in Epidemic Models with Prior Immunity and Under-Reporting.Bulletin of mathematical biology · 2026Article
- Article
- Influenza A/H3N2 epidemiology in England during the 2025 to 2026 season: a mathematical modelling study.Communications health · 2026Article
- Structural and practical identifiability of within-host models of virus dynamics - a review.Current opinion in systems biology · 2025Article
- Calibration of transmission-dynamic infectious disease models: A scoping review and reporting framework.PLoS computational biology · 2025Article
- Using COVID-19 pandemic perturbation to model RSV-hMPV interactions and potential implications under RSV interventions.Nature communications · 2025Article
- Accurate Estimation of Individual Transmission Rates Through Contact Analytics Using UWB Based Indoor Location Data.International Conference on Smart Applications, Communications and Networking : SmartNets. International Conference on Smart Applications, Communications and Networking · 2024Article
- Article
- Why the Spectral Radius? An intuition-building introduction to the basic reproduction number.Bulletin of mathematical biology · 2022Article
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3 authors.
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Abstract
If model identifiability is not confirmed, inferences from infectious disease transmission models may not be reliable, so they might result in misleading recommendations. Structural identifiability analysis characterises whether it is possible to obtain unique solutions for all unknown model parameters, given the model structure. In this work, we studied the structural identifiability of some typical deterministic compartmental models for infectious disease transmission, focusing on the influence of the data type considered as model output on the identifiability of unknown model parameters, including initial conditions. We defined 26 model versions, each having a unique combination of underlying compartmental structure and data type(s) considered as model output(s). Four compartmental model structures and three common data types in disease surveillance (incidence, prevalence and detected vector counts) were studied. The structural identifiability of some parameters varied depending on the type of model output. In general, models with multiple data types as outputs had more structurally identifiable parameters, than did models with a single data type as output. This study highlights the importance of a careful consideration of data types as an integral part of the inference process with compartmental infectious disease transmission models.
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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.