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Developments in statistical inference when assessing spatiotemporal disease clustering with the tau statistic.
Timothy M Pollington1,2, Michael J Tildesley3, T Déirdre Hollingsworth2
1MathSys CDT, University of Warwick, UK.
Spatial Statistics
|April 5, 2021
Summary
The tau statistic accurately assesses disease clustering. New methods improve its spatial clustering range estimates, enhancing disease control decisions.
Area of Science:
- Epidemiology
- Biostatistics
- Spatial Analysis
Background:
- The tau statistic is crucial for analyzing spatiotemporal disease clustering using geolocation and symptom onset data.
- Assessing the precision and range of clustering estimates is vital for effective public health interventions.
Purpose of the Study:
- To evaluate methods that may bias tau statistic clustering range estimates.
- To develop and validate an improved spatial bootstrap method for the tau statistic.
Main Methods:
- Re-analysis of an open-access measles dataset using the tau statistic.
- Development of a modified Loh & Stein spatial bootstrap sampling method tailored for the tau statistic.
- Comparison of bootstrap tau estimates with previously published results.
Main Results:
- Evidence against null hypotheses of no clustering and no inhibition was found using the global envelope test.
- The developed tau-specific bootstrap method yielded estimates with 24% lower sampling error.
- The estimated spatial clustering endpoint increased by 110% (61.0 m vs. 29 m), with a 342% increase in the clustering area of elevated disease odds.
Conclusions:
- The modified bootstrap method significantly enhances the precision and range estimation of the tau statistic.
- Accurate implementation of the tau statistic is essential for reliable disease control policy decisions.
- Improved clustering analysis can reduce inaccuracies in public health strategies.
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