Leveraging Artificial Intelligence for Pandemic Management: Case of COVID‐19 in the United States
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During pandemics, ML methods integrating community mobility and census data outperform traditional models in forecasting disease spread.
“During pandemics, ML methods integrating community mobility and census data outperform traditional models in forecasting disease spread [16].”
ML models incorporating socioeconomic variables consistently outperform traditional clinical markers in predicting risk.
“ML models incorporating socioeconomic variables consistently outperform traditional clinical markers in predicting risk.”
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Salehi, A., Babaei, A., Šimić, V., & Tırkolaee, E. B. (2026) · Informatics for Health and Social Care · doi:10.1080/17538157.2025.2606853 · Cited by 1 · see citing works
Razzano, A. V. (2026) · Health Science Reports · doi:10.1002/hsr2.73083
- Authors
- Ahmadi, E., & Maihami, R.
- Affiliations
- Mercer University (US)
- Corresponding author
- Ahmadi, Ehsan
- Venue
- Big Data Research
- Publisher
- Elsevier BV
- Published
- 2025-05
- Volume
- 40
- Pages
- 100529
- ISSN
- 2214-5796 (ISSN-L)
- OpenAlex
- W4409252692
- Cited by
- 2
- References
- 71
- Citations by year
- 2026: 2
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Big Data Research (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Keywords
- COVID-19 pandemic, pandemic forecasting, cross-learning, multi-source data fusion, time series forecasting, hospitalization, vaccination coverage, community mobility, socioeconomic factors, Google search trends, ARIMAX, transformer models, random forest, United States
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