ArticleScientific reports2021
A multi-method approach to modeling COVID-19 disease dynamics in the United States.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Association of gross domestic product with equitable access to childhood vaccines in 195 countries: a systematic review and meta-analysis.BMJ global health · 2025Pooled it
- Modelling the impact of behavioural interventions during pandemics: A systematic review.PloS one · 2025Pooled it
- Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties.Nature communications · 2023Article
- Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions.Complex & intelligent systems · 2023Article
- Using simulation modelling and systems science to help contain COVID-19: A systematic review.Systems research and behavioral science · 2022Article
- Assessing the Dynamic Outcomes of Containment Strategies against COVID-19 under Different Public Health Governance Structures: A Comparison between Pakistan and Bangladesh.International journal of environmental research and public health · 2022Article
- Article
- Optimization in the Context of COVID-19 Prediction and Control: A Literature Review.IEEE access : practical innovations, open solutions · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model's state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model's two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.
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Registered trials
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