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A first insight about spatial dimension of COVID-19: analysis at municipality level.

ABSTRACT: BACKGROUND:This paper is about spatial patterns of by corona virus disease-2019 (COVID-19). METHODS:Using data for the first 21 weeks from municipalities in Catalonia, we analyse whether reported positive cases appear randomly or following some kind of spatial dependence. Global and local measures of spatial autocorrelation are used. RESULTS:There are some clusters alongside Catalan municipalities that change over time. CONCLUSIONS:Use of spatial analysis techniques is suggested to identify spatial disease patterns and to provide spatially disaggregated public health policy recommendations.

SUBMITTER: Arauzo-Carod JM

PROVIDER: S-EPMC7454828 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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A first insight about spatial dimension of COVID-19: analysis at municipality level.

Arauzo-Carod Josep-Maria JM

Journal of public health (Oxford, England) 20210401 1

<h4>Background</h4>This paper is about spatial patterns of by corona virus disease-2019 (COVID-19).<h4>Methods</h4>Using data for the first 21 weeks from municipalities in Catalonia, we analyse whether reported positive cases appear randomly or following some kind of spatial dependence. Global and local measures of spatial autocorrelation are used.<h4>Results</h4>There are some clusters alongside Catalan municipalities that change over time.<h4>Conclusions</h4>Use of spatial analysis techniques ...[more]

PMID: 32808010

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Project description:BackgroundResponse to the coronavirus disease 2019 (COVID-19) pandemic calls for precision public health reflecting our improved understanding of who is the most vulnerable and their geographical location. We created three vulnerability indices to identify areas and people who require greater support while elucidating health inequities to inform emergency response in Kenya.MethodsGeospatial indicators were assembled to create three vulnerability indices; Social VulnerabilityIndex (SVI), Epidemiological Vulnerability Index (EVI) and a composite of the two, that is, Social Epidemiological Vulnerability Index (SEVI) resolved at 295 subcounties in Kenya. SVI included 19 indicators that affect the spread of disease; socioeconomic deprivation, access to services and population dynamics, whereas EVI comprised 5 indicators describing comorbidities associated with COVID-19 severe disease progression. The indicators were scaled to a common measurement scale, spatially overlaid via arithmetic mean and equally weighted. The indices were classified into seven classes, 1-2 denoted low vulnerability and 6-7, high vulnerability. The population within vulnerabilities classes was quantified.ResultsThe spatial variation of each index was heterogeneous across Kenya. Forty-nine northwestern and partly eastern subcounties (6.9 million people) were highly vulnerable, whereas 58 subcounties (9.7 million people) in western and central Kenya were the least vulnerable for SVI. For EVI, 48 subcounties (7.2 million people) in central and the adjacent areas and 81 subcounties (13.2 million people) in northern Kenya were the most and least vulnerable, respectively. Overall (SEVI), 46 subcounties (7.0 million people) around central and southeastern were more vulnerable, whereas 81 subcounties (14.4 million people) were least vulnerable.ConclusionThe vulnerability indices created are tools relevant to the county, national government and stakeholders for prioritisation and improved planning. The heterogeneous nature of the vulnerability indices underpins the need for targeted and prioritised actions based on the needs across the subcounties.

Dataset Information

A first insight about spatial dimension of COVID-19: analysis at municipality level.

Publications

A first insight about spatial dimension of COVID-19: analysis at municipality level.

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