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

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  • Sabelo Nick Dlamini

    (Department of Geography, University of Eswatini, Kwaluseni, Manzini M200, Eswatini
    World Health Organization, 1211 Geneva, Switzerland)

  • Wisdom Mdumiseni Dlamini

    (Department of Geography, University of Eswatini, Kwaluseni, Manzini M200, Eswatini)

  • Ibrahima Socé Fall

    (World Health Organization, 1211 Geneva, Switzerland)

Abstract

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.

Suggested Citation

  • Sabelo Nick Dlamini & Wisdom Mdumiseni Dlamini & Ibrahima Socé Fall, 2022. "Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method," IJERPH, MDPI, vol. 19(15), pages 1-12, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9171-:d:873011
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    References listed on IDEAS

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    1. Giovanni Pasquali & Shane Godfrey, 2022. "Governance of Eswatini Apparel Regional Value Chains and the Implications of Covid-19," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 34(1), pages 473-502, February.
    2. William H. Greene, 1994. "Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models," Working Papers 94-10, New York University, Leonard N. Stern School of Business, Department of Economics.
    3. Demombynes,Gabriel, 2020. "COVID-19 Age-Mortality Curves Are Flatter in Developing Countries," Policy Research Working Paper Series 9313, The World Bank.
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