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Endemic-epidemic models to understand COVID-19 spatio-temporal evolution.
Alessandro Celani1, Paolo Giudici2
1Dipartimento di Scienze Economiche e Sociali, Polytechnic University of Marche, Piazzale Raffaele Martelli 8, 60121 Ancona, Italy.
Spatial Statistics
|July 26, 2021
Summary
We developed a new statistical model to track COVID-19 spread over time and geography. This endemic-epidemic model was applied to northern Italy, showing its utility in monitoring contagion dynamics.
Area of Science:
- Epidemiology
- Biostatistics
- Statistical Modeling
Background:
- The COVID-19 pandemic presented significant challenges in monitoring disease transmission.
- Understanding spatial and temporal contagion dynamics is crucial for effective public health interventions.
Purpose of the Study:
- To introduce a novel negative binomial space-time autoregression model.
- To apply this model for monitoring COVID-19 contagion dynamics in both time and space.
Main Methods:
- Development of a negative binomial space-time autoregression model.
- Empirical analysis using data from provinces in northern Italy.
- Focus on regions heavily impacted by COVID-19 and similar non-pharmaceutical interventions.
Main Results:
- The proposed model effectively captures the spatio-temporal patterns of COVID-19 spread.
- Demonstrated the model's applicability in a real-world scenario with significant outbreak impact.
Conclusions:
- The endemic-epidemic model offers a robust framework for real-time contagion monitoring.
- This approach can aid public health authorities in managing infectious disease outbreaks.
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