tetano
Editor, Senior Moderator
Spat Spatiotemporal Epidemiol
. 2021 Aug;38:100433.
doi: 10.1016/j.sste.2021.100433. Epub 2021 May 21.
Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: A small-area analysis in Germany
Sven Rohleder[SUP] 1 [/SUP], Kayvan Bozorgmehr[SUP] 2 [/SUP]
Affiliations
Abstract
Timely monitoring of incidence risks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and associated deaths at small-area level is essential to inform containment strategies. We analysed the spatiotemporal epidemiology of the SARSCoV- 2 pandemic at district level in Germany to develop a tool for disease monitoring. We used a Bayesian spatiotemporal model to estimate the district-specific risk ratios (RR) of SARS-CoV-2 incidence and the posterior probability (PP) for exceedance of RR thresholds 1, 2 or 3. Of 220 districts (55% of 401 districts) showing a RR > 1, 188 (47%) exceed the RR threshold with sufficient certainty (PP ≥ 80%) to be considered at high risk. 47 districts show very high (RR > 2, PP ≥ 80%) and 15 extremely high (RR > 3, PP ≥ 80%) risks. The spatial approach for monitoring the risk of SARS-CoV-2 provides an informative basis for local policy planning.
Keywords: Bayesian spatial analysis; Covid-19; Infectious disease modelling; Infectious disease monitoring; SARS-CoV-2.
. 2021 Aug;38:100433.
doi: 10.1016/j.sste.2021.100433. Epub 2021 May 21.
Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: A small-area analysis in Germany
Sven Rohleder[SUP] 1 [/SUP], Kayvan Bozorgmehr[SUP] 2 [/SUP]
Affiliations
- PMID: 34353525
- DOI: 10.1016/j.sste.2021.100433
Abstract
Timely monitoring of incidence risks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and associated deaths at small-area level is essential to inform containment strategies. We analysed the spatiotemporal epidemiology of the SARSCoV- 2 pandemic at district level in Germany to develop a tool for disease monitoring. We used a Bayesian spatiotemporal model to estimate the district-specific risk ratios (RR) of SARS-CoV-2 incidence and the posterior probability (PP) for exceedance of RR thresholds 1, 2 or 3. Of 220 districts (55% of 401 districts) showing a RR > 1, 188 (47%) exceed the RR threshold with sufficient certainty (PP ≥ 80%) to be considered at high risk. 47 districts show very high (RR > 2, PP ≥ 80%) and 15 extremely high (RR > 3, PP ≥ 80%) risks. The spatial approach for monitoring the risk of SARS-CoV-2 provides an informative basis for local policy planning.
Keywords: Bayesian spatial analysis; Covid-19; Infectious disease modelling; Infectious disease monitoring; SARS-CoV-2.