ArticleInfectious Disease Modelling2020
Why is it difficult to accurately predict the COVID-19 epidemic?
Article in Infectious Disease Modelling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 240 papers, 1 of them a synthesis that pooled it.
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Who cites it
240 citing papers in PubMed, 1 synthesis or guideline pooled it, 687 citations in OpenAlex.
- Seroprevalence of SARS-CoV-2 Antibodies in Africa: A Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2022Pooled it
- Resolving parameter uncertainty in SIR models through population-level serological surveillance: A synthetic study.Infectious Disease Modelling · 2026Article
- Decoding Viral Transmission Dynamics in Structured Populations Using a Microfluidic Herd-Immunity-on-a-Chip Platform.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Mechanistic Understanding of Pandemic Dynamics: A Multiscale Algorithmic Framework.Life (Basel, Switzerland) · 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
- Understanding pandemics through molecular gas dynamics.PNAS nexus · 2025Article
- Spread of the plague in Venice, 1630-1631: epidemic entropy in a "natural experiment".medRxiv : the preprint server for health sciences · 2025Article
- Estimating the effect of contact tracing during the early stage of an epidemic.Infectious Disease Modelling · 2025Article
- Impact of COVID-19 vaccinations in India: a state-wise analysis.BMC public health · 2025Article
- An effective drift-diffusion model for pandemic propagation and uncertainty prediction.Biophysical reports · 2024Article
- A tight fit of the SIR dynamic epidemic model to daily cases of COVID-19 reported during the 2021-2022 Omicron surge in New York City: A novel approach.Statistical methods in medical research · 2024Article
- SARS-CoV-2 Seropositivity in Nursing Home Staff and Residents during the First SARS-CoV-2 Wave in Flanders, Belgium.Viruses · 2024Article
- A data-driven analysis on the mediation effect of compartment models between control measures and COVID-19 epidemics.Heliyon · 2024Article
- Mathematical Assessment of the Role of Human Behavior Changes on SARS-CoV-2 Transmission Dynamics in the United States.Bulletin of mathematical biology · 2024Article
- How robust are estimates of key parameters in standard viral dynamic models?PLoS computational biology · 2024Article
- An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study.Scientific reports · 2024Article
- Predicting Covid-19 pandemic waves with biologically and behaviorally informed universal differential equations.Heliyon · 2024Article
- The manifestation and causes of public panic in the early stage of COVID-19 in China: a framework based on consciousness-attitude-behavior.Frontiers in public health · 2024Article
- Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia.PLOS global public health · 2024Article
180 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.
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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.