Evidence map›Paper›PMID 36219835›Full record

ArticleJMIR public health and surveillance2022

Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation.

Tjeerd van der Ploeg, Robbert J J Gobbens

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

2 authors at 2 institutions in 2 countries.

Tjeerd van der PloegFaculty of Health, Sports and Social Work, Inholland University of Applied Sciences, Amsterdam, Netherlands.ORCID 0000-0003-0429-6753
Robbert J J GobbensFaculty of Health, Sports and Social Work, Inholland University of Applied Sciences, Amsterdam, Netherlands.ORCID 0000-0001-6225-5189
Inholland University of Applied Sciences · NLUniversity of Antwerp · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities.

objectiveWe aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques.

methodsWe collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands.

resultsThe final prediction model had an R

conclusionsData about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.

Indexed as

COVID-19AgedAlgorithmsChildCitiesCoughHumansNetherlandsParticulate MatterParticulate MattercongestioncoughCOVID-19data mergingdeathDutch public domaindyspneafatiguefeverinfectionsmodeling techniquemunicipality propertiespandemicpneumoniaprediction modelpublicpublic healthstatic datasurveillancesymptomsvariable selectionvirusWuhan

Identifiers

PMID36219835
PMCPMC9586255
OpenAlexW4304758283

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.