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Research article
Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: a small-area analysis in Germany
Kayvan Bozorgmehr
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1411-1209
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: As response to the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), countries worldwide have implemented mitigation and control measures at national and subnational level. Timely monitoring of risks of SARS-CoV-2 incidence and associated deaths at small-area level is essential to inform local response strategies. However, the potentials of spatial epidemiology to contribute to this aim are yet untapped in most countries. Using the example of Germany, we analysed the spatiotemporal epidemiology of SARS-CoV-2 incidence and associated deaths at district level to develop a tool for monitoring incidence and mortality rates and to estimate district-specific risks of disease incidence.
Methods: We conducted a longitudinal small-area analysis for 401 districts to assess the district-specific risks of SARS-CoV-2 incidence by using nationally representative data from the national surveillance system in Germany on a daily basis (January 28 th to May 4 th 2020). We used a Bayesian spatiotemporal model to estimate the district-specific risk ratios (RR) of SARS-CoV-2 incidence and the posterior exceedance probability for RR thresholds greater than 1, 2 or 3, respectively. We further calculated standardised incidence (SIR) and mortality ratios (SMR) stratified by sex and age groups to assess the spatial distribution of SARS-CoV-2 incidence and deaths.
Results: A total of 85 districts (21 % of all districts) showed a RR greater than 3, and 63 districts (16 % of all districts) exceed the RR threshold with a probability of greater than 80 %. Median RR was 1.19 (range 0-523.08), and the median SIR and SMR were 0.34 (range 0-423.94) and 0 (range 0-343.39), respectively. Elevated RR, and correspondingly high SIR and SMR, were observed in at-risk districts (identified by the spatiotemporal model) in southern and western districts of Germany. Daily updates of district-specific risk, SIR and SMR are implemented in a web-based platform. Conclusions: Our approach provides an informative and timely tool to monitor the district-specific risks of SARS-CoV-2 incidence and associated deaths. This approach can be used to inform local authorities for decision-making and strategy planning on containing the SARS-CoV-2 pandemic.
Keywords
Covid-19, infectious disease monitoring, Bayesian spatial analysis, infectious disease modelling
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This is a preprint. It has not completed peer review.
Research article
Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: a small-area analysis in Germany
Kayvan Bozorgmehr
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1411-1209
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 11 Jun, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epidemiology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: As response to the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), countries worldwide have implemented mitigation and control measures at national and subnational level. Timely monitoring of risks of SARS-CoV-2 incidence and associated deaths at small-area level is essential to inform local response strategies. However, the potentials of spatial epidemiology to contribute to this aim are yet untapped in most countries. Using the example of Germany, we analysed the spatiotemporal epidemiology of SARS-CoV-2 incidence and associated deaths at district level to develop a tool for monitoring incidence and mortality rates and to estimate district-specific risks of disease incidence.
Methods: We conducted a longitudinal small-area analysis for 401 districts to assess the district-specific risks of SARS-CoV-2 incidence by using nationally representative data from the national surveillance system in Germany on a daily basis (January 28 th to May 4 th 2020). We used a Bayesian spatiotemporal model to estimate the district-specific risk ratios (RR) of SARS-CoV-2 incidence and the posterior exceedance probability for RR thresholds greater than 1, 2 or 3, respectively. We further calculated standardised incidence (SIR) and mortality ratios (SMR) stratified by sex and age groups to assess the spatial distribution of SARS-CoV-2 incidence and deaths.
Results: A total of 85 districts (21 % of all districts) showed a RR greater than 3, and 63 districts (16 % of all districts) exceed the RR threshold with a probability of greater than 80 %. Median RR was 1.19 (range 0-523.08), and the median SIR and SMR were 0.34 (range 0-423.94) and 0 (range 0-343.39), respectively. Elevated RR, and correspondingly high SIR and SMR, were observed in at-risk districts (identified by the spatiotemporal model) in southern and western districts of Germany. Daily updates of district-specific risk, SIR and SMR are implemented in a web-based platform. Conclusions: Our approach provides an informative and timely tool to monitor the district-specific risks of SARS-CoV-2 incidence and associated deaths. This approach can be used to inform local authorities for decision-making and strategy planning on containing the SARS-CoV-2 pandemic.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6