Combination forecasting of COVID-19 in Bangladesh: guiding public health policy through integrated time series, ML, and DL models
Sharmin Akther, Amartay Kumar Dhar, Jakia Sultana Pingky · 2025
Read Combination forecasting of COVID-19 in Bangladesh: guiding public health policy through integrated time series, ML, and DL models by Sharmin Akther, Amartay Kumar Dhar, Jakia Sultana Pingky (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12817659.txt.
Description
Sharmin Akther, Amartay Dhar, and Jakia Pingky built a combined forecasting model for Covid-19 in Bangladesh in 2025, aimed at guiding public health policy. Forecasting an epidemic well is hard anywhere, and harder in a low-resource health system. Many global studies used machine learning for Covid, but few built ensemble forecasts shaped to Bangladesh.
Their weighted ensemble joins classic time series models with machine learning and deep learning ones, including recurrent networks suited to sequences. Each part forecasts daily new cases, and the weights favor whichever models have earned trust. Readers get a local forecasting stack rather than an imported one: policy in a resource-poor setting needs predictions built for its own data and limits.
Catalog record
| CREATOR | Sharmin Akther, Amartay Kumar Dhar, Jakia Sultana Pingky |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 39 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |