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Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method

COVID-19 country spikes have been reported at varying temporal scales as a result of differences in the disease-driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between socio-economic, weather, demographic and...

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Autores principales: Dlamini, Sabelo Nick, Dlamini, Wisdom Mdumiseni, Fall, Ibrahima Socé
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9367839/
https://www.ncbi.nlm.nih.gov/pubmed/35954524
http://dx.doi.org/10.3390/ijerph19159171
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author Dlamini, Sabelo Nick
Dlamini, Wisdom Mdumiseni
Fall, Ibrahima Socé
author_facet Dlamini, Sabelo Nick
Dlamini, Wisdom Mdumiseni
Fall, Ibrahima Socé
author_sort Dlamini, Sabelo Nick
collection PubMed
description COVID-19 country spikes have been reported at varying temporal scales as a result of differences in the disease-driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between socio-economic, weather, demographic and health variables with the reported cases of COVID-19 in Eswatini using the maximum likelihood estimation method for count data. A generalized Poisson regression (GPR) model was fitted with the data comprising 15 covariates to predict COVID-19 risk in the whole of Eswatini. The results show that the variables that were key determinants in the spread of the disease were those that included the proportion of elderly above 55 years at 98% (95% CI: 97–99%) and the proportion of youth below the age of 35 years at 8% (95% CI: 1.7–38%) with a pseudo R-square of 0.72. However, in the early phase of the virus when cases were fewer, results from the Poisson regression showed that household size, household density and poverty index were associated with reported COVID-19 cases in the country. We then produced a disease-risk map of predicted COVID-19 in Eswatini using variables that were selected by the regression model at a 5% significance level. The map could be used by the country to plan and prioritize health interventions against COVID-19. The identified areas of high risk may be further investigated to find out the risk amplifiers and assess what could be done to prevent them.
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spelling pubmed-93678392022-08-12 Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method Dlamini, Sabelo Nick Dlamini, Wisdom Mdumiseni Fall, Ibrahima Socé Int J Environ Res Public Health Article COVID-19 country spikes have been reported at varying temporal scales as a result of differences in the disease-driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between socio-economic, weather, demographic and health variables with the reported cases of COVID-19 in Eswatini using the maximum likelihood estimation method for count data. A generalized Poisson regression (GPR) model was fitted with the data comprising 15 covariates to predict COVID-19 risk in the whole of Eswatini. The results show that the variables that were key determinants in the spread of the disease were those that included the proportion of elderly above 55 years at 98% (95% CI: 97–99%) and the proportion of youth below the age of 35 years at 8% (95% CI: 1.7–38%) with a pseudo R-square of 0.72. However, in the early phase of the virus when cases were fewer, results from the Poisson regression showed that household size, household density and poverty index were associated with reported COVID-19 cases in the country. We then produced a disease-risk map of predicted COVID-19 in Eswatini using variables that were selected by the regression model at a 5% significance level. The map could be used by the country to plan and prioritize health interventions against COVID-19. The identified areas of high risk may be further investigated to find out the risk amplifiers and assess what could be done to prevent them. MDPI 2022-07-27 /pmc/articles/PMC9367839/ /pubmed/35954524 http://dx.doi.org/10.3390/ijerph19159171 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dlamini, Sabelo Nick
Dlamini, Wisdom Mdumiseni
Fall, Ibrahima Socé
Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method
title Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method
title_full Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method
title_fullStr Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method
title_full_unstemmed Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method
title_short Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method
title_sort predicting covid-19 infections in eswatini using the maximum likelihood estimation method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9367839/
https://www.ncbi.nlm.nih.gov/pubmed/35954524
http://dx.doi.org/10.3390/ijerph19159171
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