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Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method
Sabelo Nick Dlamini1,2, Wisdom Mdumiseni Dlamini1, Ibrahima Socé Fall2
1Department of Geography, University of Eswatini, Kwaluseni, Manzini M200, Eswatini.
Summary
COVID-19 spread in Eswatini was significantly driven by the proportion of elderly (over 55) and youth (under 35). Early cases were linked to household size, density, and poverty, informing public health interventions.
Area of Science:
- Epidemiology
- Public Health
- Biostatistics
Background:
- COVID-19 transmission dynamics vary globally due to diverse influencing factors.
- Understanding country-specific drivers of COVID-19 spread is crucial for effective control.
Purpose of the Study:
- To investigate the association between socio-economic, weather, demographic, and health variables and COVID-19 cases in Eswatini.
- To predict COVID-19 risk and identify key determinants of disease spread within Eswatini.
Main Methods:
- Utilized maximum likelihood estimation for count data analysis.
- Employed a generalized Poisson regression (GPR) model with 15 covariates to predict COVID-19 risk.
- Developed a disease-risk map based on significant regression variables (p < 0.05).
Main Results:
- The proportion of elderly (above 55) was a key determinant (98% association), followed by youth (under 35) (8% association).
- Early in the pandemic, household size, density, and poverty index were associated with reported cases.
- A pseudo R-square of 0.72 indicated a strong model fit.
Conclusions:
- Demographic factors, particularly age distribution, significantly influence COVID-19 spread in Eswatini.
- A disease-risk map can guide public health planning and intervention prioritization.
- Further investigation into high-risk areas is recommended to identify and mitigate risk amplifiers.
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