Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness
Petra Norlund, Jamal Jokar Arsanjani, Jesper M. Paasch · 2026
Read Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness by Petra Norlund, Jamal Jokar Arsanjani, Jesper M. Paasch (2026) 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-13205768.txt.
Description
The COVID-19 pandemic produced a flood of maps and forecasts that tracked the virus across place and time. Petra Norlund, Jamal Jokar Arsanjani, and Jesper Paasch review that work in a systematic sweep of 363 studies from 2020 to mid-2025. They sort the studies by region, method, data sources, and purpose.
Classic Bayesian and compartment models dominated, though machine learning and hybrid designs grew over time. About 70 percent of studies mixed several data streams, such as movement, weather, and social data, but few mixed three or more. Coverage leaned heavily toward rich countries, leaving poorer regions under-mapped. Models using finer local detail and social variables better supported targeted action. The authors urge fairer data systems and clearer reporting of uncertainty for the next pandemic.
Catalog record
| CREATOR | Petra Norlund, Jamal Jokar Arsanjani, Jesper M. Paasch |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 55 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |