Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic...

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• The COVID-19 pandemic necessitated rapid development of spatio-temporal models for public health.
• A comprehensive synthesis of these models' evolution, geographic scope, and equity implications is lacking.

Purpose of the Study:

• To conduct a global systematic review of spatio-temporal models used during the COVID-19 pandemic.
• To analyze trends in modeling approaches, data integration, geographic distribution, and equity.

Main Methods:

• Systematic review of 363 peer-reviewed studies (Jan 2020-Aug 2025) using PRISMA 2020 guidelines.
• Classification of studies by geographic scale, modeling approach, data streams, and analytical purpose.

Main Results:

• Bayesian and compartmental models were dominant, with increasing use of machine learning and hybrid methods over time.
• 70% of studies integrated multiple data streams, but few combined three or more.
• Geographic coverage was concentrated in high-income regions, underrepresenting low- and middle-income countries.
• Models using finer spatial scales and socio-demographic data better supported targeted risk and intervention analysis.

Conclusions:

• Multi-source data integration and improved geographic representativeness are crucial for effective pandemic modeling.
• Transparent uncertainty communication and FAIR-aligned, equity-aware data infrastructures are essential for future preparedness.
• Addressing geographic and data biases in modeling is vital for equitable public health responses.