ArticleScience advances2022
Lack of practical identifiability may hamper reliable predictions in COVID-19 epidemic models.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Resolving parameter uncertainty in SIR models through population-level serological surveillance: A synthetic study.Infectious Disease Modelling · 2026Article
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- Optimal algorithms for controlling infectious diseases in real time using noisy infection data.PLoS computational biology · 2025Article
- A Systematic Computational Framework for Practical Identifiability Analysis in Mathematical Models Arising from Biology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Large-scale epidemiological modelling: scanning for mosquito-borne diseases spatio-temporal patterns in Brazil.Royal Society open science · 2025Article
- Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting.Viruses · 2025Article
- A Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters.Infectious Disease Modelling · 2024Article
- Quantum-Like Approaches Unveil the Intrinsic Limits of Predictability in Compartmental Models.Entropy (Basel, Switzerland) · 2024Article
- Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024Article
- A data-driven analysis on the mediation effect of compartment models between control measures and COVID-19 epidemics.Heliyon · 2024Article
- Reconstructing higher-order interactions in coupled dynamical systems.Nature communications · 2024Article
- How robust are estimates of key parameters in standard viral dynamic models?PLoS computational biology · 2024Article
- System identifiability in a time-evolving agent-based model.PloS one · 2024Article
- A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile.Journal of biological dynamics · 2023Article
- On Parameter Identifiability in Network-Based Epidemic Models.Bulletin of mathematical biology · 2023Article
- Article
- The Structural Identifiability of a Humidity-Driven Epidemiological Model of Influenza Transmission.Viruses · 2022Article
- A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Application to COVID-19.Viruses · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compartmental models are widely adopted to describe and predict the spreading of infectious diseases. The unknown parameters of these models need to be estimated from the data. Furthermore, when some of the model variables are not empirically accessible, as in the case of asymptomatic carriers of coronavirus disease 2019 (COVID-19), they have to be obtained as an outcome of the model. Here, we introduce a framework to quantify how the uncertainty in the data affects the determination of the parameters and the evolution of the unmeasured variables of a given model. We illustrate how the method is able to characterize different regimes of identifiability, even in models with few compartments. Last, we discuss how the lack of identifiability in a realistic model for COVID-19 may prevent reliable predictions of the epidemic dynamics.
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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.