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      Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: a small-area analysis in Germany

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          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.

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          Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

          Håvard Rue, Sara Martino, Nicolas Chopin (2009)
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            GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

            Abolfazl Mollalo, Behzad Vahedi, Kiara Rivera (2020)
            During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been announced, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.
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              Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics

              Maged Kamel Boulos, Estella Geraghty (2020)
              In December 2019, a new virus (initially called ‘Novel Coronavirus 2019-nCoV’ and later renamed to SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China, and rapidly spread to other parts of China and other countries around the world, despite China’s massive efforts to contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, geographic information systems and methods, including, among other application possibilities, online real-or near-real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time, are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight against infectious disease outbreaks and epidemics.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Spat Spatiotemporal Epidemiol
                Journal ID (iso-abbrev): Spat Spatiotemporal Epidemiol
                Title: Spatial and Spatio-Temporal Epidemiology
                Publisher: Elsevier Ltd.
                ISSN (Print): 1877-5845
                ISSN (Electronic): 1877-5853
                Publication date PMC-release: 21 May 2021
                Publication date (Electronic): 21 May 2021
                Electronic Location Identifier: 100433
                Affiliations
                [1 ]Department of Population Medicine and Health Services Research, School of Public Health, Bielefeld University, Universitätsstraße 25, 33501 Bielefeld, Bielefeld, Germany
                [2 ]Section for Health Equity Studies & Migration, Department of General Practice and Health Services Research, University Hospital Heidelberg, Marsilius Arkaden (Turm West), Im Neuenheimer Feld 130.3, 69120 Heidelberg, Heidelberg, Germany
                Author notes
                [* ]Address for correspondence: Prof. Dr. Kayvan Bozorgmehr (MD, M.Sc.), Head, Dept. of Population Medicine and Health Services Research, School of Public Health, Bielefeld University, Universitätsstraße 25, 33501 Bielefeld, Phone: +49 521 106 6311; Fax: +49 6221 56 1972.
                Article
                Publisher Item ID: S1877-5845(21)00032-0 Publisher ID: 100433
                DOI: 10.1016/j.sste.2021.100433
                PMC ID: 8139365
                SO-VID: 43fa0f8b-c8a9-4e9f-8f88-0e24bfeed703
                Copyright © © 2021 Elsevier Ltd. All rights reserved.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 22 December 2020
                Date revision received : 26 March 2021
                Date accepted : 10 May 2021
                Categories
                Subject: Article

                Keywords: covid-19,sars-cov-2,infectious disease monitoring,bayesian spatial analysis,infectious disease modelling
                Data availability:
                Keywords: covid-19, sars-cov-2, infectious disease monitoring, bayesian spatial analysis, infectious disease modelling

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