ArticleJournal of mathematical biology2022
Limits of epidemic prediction using SIR models.
Article in Journal of mathematical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Determining disease attributes from epidemic trajectories.Infectious Disease Modelling · 2026Article
- Efficient reporting delay calibration in spatial metapopulation models for reconstructing cross-regional epidemic dynamics.Biosafety and health · 2026Article
- A semi-mechanistic modeling strategy for infectious diseases forecasting: Error correction and probabilistic prediction.Biosafety and health · 2026Article
- Combining data assimilation of states and parameters for more precise infectious disease prediction.One health (Amsterdam, Netherlands) · 2025Article
- Mapping Incidence and Prevalence Peak Data for SIR Modeling Applications.Journal of mathematical biology · 2025Article
- Quantifying phage infectivity from characteristics of bacterial population dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Assessing bias in susceptible-infected-recovered estimation from aggregated epidemic data.Royal Society open science · 2025Article
- Review
- Harnessing artificial intelligence for enhanced public health surveillance: a narrative review.Frontiers in public health · 2025Review
- Quantum-Like Approaches Unveil the Intrinsic Limits of Predictability in Compartmental Models.Entropy (Basel, Switzerland) · 2024Article
- The disutility of compartmental model forecasts during the COVID-19 pandemic.Frontiers in epidemiology · 2024Article
- Accurately summarizing an outbreak using epidemiological models takes time.Royal Society open science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The Susceptible-Infectious-Recovered (SIR) equations and their extensions comprise a commonly utilized set of models for understanding and predicting the course of an epidemic. In practice, it is of substantial interest to estimate the model parameters based on noisy observations early in the outbreak, well before the epidemic reaches its peak. This allows prediction of the subsequent course of the epidemic and design of appropriate interventions. However, accurately inferring SIR model parameters in such scenarios is problematic. This article provides novel, theoretical insight on this issue of practical identifiability of the SIR model. Our theory provides new understanding of the inferential limits of routinely used epidemic models and provides a valuable addition to current simulate-and-check methods. We illustrate some practical implications through application to a real-world epidemic data set.
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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.