ArticleMedical journal, Armed Forces India2021
Systematic review of predictive mathematical models of COVID-19 epidemic.
Article in Medical journal, Armed Forces India, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic.International journal of environmental research and public health · 2022Pooled it
- Pricing decisions of converted and expanded enterprises for medical supplies under major public health emergencies.Frontiers in public health · 2026Article
- Did COVID-19 surveillance system sensitivity change after Omicron? a retrospective observational study in England.BMC infectious diseases · 2025Observational
- Mathematical Contact Tracing Models for the COVID-19 Pandemic: A Systematic Review of the Literature.Healthcare (Basel, Switzerland) · 2025Review
- What Lessons can Be Learned From the Management of the COVID-19 Pandemic?International journal of public health · 2025Review
- Mathematical modelling and analysis for the co-infection of viral and bacterial diseases: a systematic review protocol.BMJ open · 2024Article
- Impact assessment of self-medication on COVID-19 prevalence in Gauteng, South Africa, using an age-structured disease transmission modelling framework.BMC public health · 2024Article
- Article
- Genomic profiling and spatial SEIR modeling of COVID-19 transmission in Western New York.Frontiers in microbiology · 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
- Pharmaceutical and non-pharmaceutical interventions for controlling the COVID-19 pandemic.Royal Society open science · 2023Article
- An analysis of the waning effect of COVID-19 vaccinations.Genomics & informatics · 2023Article
- Leveraging an epidemic-economic mathematical model to assess human responses to COVID-19 policies and disease progression.Scientific reports · 2023Article
- A Multi-SCALE Community Network-Based SEIQR Model to Evaluate the Dynamic NPIs of COVID-19.Healthcare (Basel, Switzerland) · 2023Article
- Hybrid discrete-time-continuous-time models and a SARS CoV-2 mystery: Sub-Saharan Africa's low SARS CoV-2 disease burden.Journal of mathematical biology · 2023Article
- Modeling COVID-19 hospital admissions and occupancy in the Netherlands.European journal of operational research · 2023Article
- The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management.Frontiers in public health · 2023Review
- Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting.PloS one · 2023Article
- A Model for Predicting the Duration of Viral Shedding in Patients Who Had Been Hospitalized with Mild COVID-19: A Single-Center Retrospective Study.Infection and drug resistance · 2023Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVarious mathematical models were published to predict the epidemiological consequences of the COVID-19 pandemic. This systematic review has studied the initial epidemiological models.
methodsArticles published from January to June 2020 were extracted from databases using search strings and those peer-reviewed with full text in English were included in the study. They were analysed as to whether they made definite predictions in terms of time and numbers, or contained only mathematical assumptions and open-ended predictions. Factors such as early vs. late prediction models, long-term vs. curve-fitting models and comparisons based on modelling techniques were analysed in detail.
resultsAmong 56,922 hits in 05 databases, screening yielded 434 abstracts, of which 72 articles were included. Predictive models comprised over 70% (51/72) of the articles, with susceptible, exposed, infectious and recovered (SEIR) being the commonest type (mean duration of prediction being 3 months). Common predictions were regarding cumulative cases (44/72, 61.1%), time to reach total numbers (41/72, 56.9%), peak numbers (22/72, 30.5%), time to peak (24/72, 33.3%), hospital utilisation (7/72, 9.7%) and effect of lockdown and NPIs (50/72, 69.4%). The commonest countries for which models were predicted were China followed by USA, South Korea, Japan and India. Models were published by various professionals including Engineers (12.5%), Mathematicians (9.7%), Epidemiologists (11.1%) and Physicians (9.7%) with a third (32.9%) being the result of collaborative efforts between two or more professions.
conclusionThere was a wide diversity in the type of models, duration of prediction and the variable that they predicted, with SEIR model being the commonest type.
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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.