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Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa
A Potgieter1, I N Fabris-Rotelli1, Z Kimmie2
1Department of Statistics, University of Pretoria, Pretoria, South Africa.
Frontiers in Big Data
|November 8, 2021
Summary
Understanding human mobility is key to tracking infectious disease spread, like COVID-19. This study compares different mobility data sources to see how well they reflect regional movement patterns for better disease modeling.
Area of Science:
- Epidemiology
- Spatial Analysis
- Data Science
Background:
- The COVID-19 pandemic highlighted the need to understand human mobility for disease control.
- Accurate modeling of SARS-CoV-2 spread requires comprehensive human mobility data.
Purpose of the Study:
- To compare various human mobility data sources.
- To assess the congruence of different mobility data in representing regional movement.
- To guide the selection of appropriate mobility data for epidemiological modeling.
Main Methods:
- Construction of spatial weight matrices using four distinct methods.
- Analysis of mobility data at multiple spatial resolutions.
- Comparison of data source outputs via hierarchical clustering.
- Inclusion of inter-unit distance and spatial covariates in matrix construction.
Main Results:
- Different mobility data sources provide varying insights into regional movement.
- The choice of data source impacts the representation of mobility patterns.
- Spatial resolution significantly influences the interpretation of mobility data.
Conclusions:
- Mobility data comparison is crucial for effective epidemiological modeling.
- Understanding data source characteristics aids in selecting the most suitable data for specific research questions.
- This research offers guidance on leveraging diverse mobility data for public health insights.
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