A Mass-Conservation Model for Stability Analysis and Finite-Time Estimation of Spread of Covid-19
Bibliographic Data
| ID | 22108298 |
|---|---|
| Authors | Hossein Rastgoftar (0000-0001-9212-7161, University of Michigan), Ella Atkins (0000-0003-2132-6256, University of Michigan) |
| Year | 2021 |
| Volume | 8 |
| Issue | 4 |
| Pages | 930-937 |
| Publication date | 2021-08-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2021.3050476 |
| PMID | 37982039 |
| OpenAlex | W3128566833 |
| Language | EN |
| References cited | 31 |
The COVID-19 global pandemic has significantly impacted people throughout the United States and the World. While it was initially believed the virus was transmitted from animal to human, person-to-person transmission is now recognized as the main source of community spread. This article integrates data into physics-based models to analyze stability of the rapid COVID-19 growth and to obtain a data-driven model for spread dynamics among the human population. The proposed mass-conservation model is used to learn the parameters of pandemic growth and to predict the growth of total cases, deaths, and recoveries over a finite future time horizon. The proposed finite-time prediction model is validated by finite-time estimation of the total numbers of infected cases, deaths, and recoveries in the United States from March 12, 2020 to December 9, 2020
Environmental health · Epidemic model · Estimation · Geography · Machine learning · Pandemic · Population · Statistics · Telecommunications · Computer Science · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Engineering · Mathematics · Medicine · SARS-CoV-2 and COVID-19 Research
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Covid-19
| Highly cited | No |
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