The COVID-19 pandemic has exhibited complex, multiwave dynamics with substantial spatial and temporal heterogeneity. In South Africa, repeated waves, driven by variant emergence, shifting public health policies, and uneven vaccine uptake, posed significant challenges to real-time surveillance and predictive modeling. There is a growing need for statistical frameworks that can capture these dynamics while offering interpretable insights for public health planning. We applied a spatio-temporal endemic-epidemic model to daily COVID-19 case counts across nine South African provinces from March 2020 to July 2022. The final model included fixed effects for time trends, seasonality, variant dominance, lagged vaccination coverage, government stringency, and weekend reporting patterns. Spatial transmission was modeled using power-law distance weights, and province-specific random intercepts were included in all components. Transmission was decomposed into endemic (background), autoregressive (within-province), and neighbourhood (interprovincial) contributions. Model validation involved 14-day internal forecasting, with predictive accuracy evaluated using 95% prediction intervals. The final model provided the best fit based on AIC, mean log score, and dominant epidemic eigenvalue. Local transmission dominated overall spread, especially in provinces with sustained epidemic activity. The neighbourhood component highlighted Gauteng and Western Cape as key sources of spatial transmission. Omicron dominance significantly increased both background and interprovincial transmission, while higher vaccination coverage was associated with reduced spatial spread. The model achieved good forecasting performance, with most observed values falling within 95% prediction intervals. Divergence after Day 10 in forecasts suggested early signals of new wave onset. This study shows that the endemic–epidemic model offers a practical and interpretable way to monitor COVID-19 transmission across South Africa’s provinces. By combining spatial structure, temporal patterns, and relevant covariates, the framework helps identify dominant transmission routes and detect emerging changes in epidemic pressure. These features make the model useful for near-real-time surveillance and for guiding locally targeted public-health responses, particularly in settings where resources and response capacity vary across regions.
Aloni et al. (Sat,) studied this question.