Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Apr 2, 2022
Date Accepted: Oct 9, 2022
Date Submitted to PubMed: Oct 11, 2022
Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation
ABSTRACT
Background:
Background The coronavirus disease (COVID-19) was first identified in December 2019 in the city of Wuhan in 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.
Objective:
Objective We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality using the properties of 355 municipalities in the Netherlands.
Methods:
Methods We 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 1-1-2020 to 9-5-2021. We used the modelling techniques random forest and multiple fractional polynomials to construct a predictive model in predicting the cumulative number of confirmed infections per 10,000 inhabitants in a municipality.
Results:
Results Important properties in predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhab- itants in a municipality were the PM10 concentration in the air, the percentage Labour Party voters, and the number of children in the households. The prediction model had a Rsq of 0.63.
Conclusions:
Conclusions Collecting data about municipality properties in relation to the cumulative number of confirmed infections in a municipality can give insight in the most important properties predicting the cumulative number of confirmed infections per 10,000 inhabitants for a municipality.
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