Projecting malaria elimination in Thailand using Bayesian hierarchical spatiotemporal models

Abstract

Thailand has set a goal of eliminating malaria by 2024 in its national strategic plan. In this study, we used the Thailand malaria surveillance database to develop hierarchical spatiotemporal models to analyze retrospective patterns and predict Plasmodium falciparum and Plasmodium vivax malaria incidences at the provincial level. We first describe the available data, explain the hierarchical spatiotemporal framework underlying the analysis, and then display the results of fitting various space-time formulations to the malaria data with the different model selection metrics. The Bayesian model selection process assessed the sensitivity of different specifications to obtain the optimal models. To assess whether malaria could be eliminated by 2024 per Thailand's National Malaria Elimination Strategy, 2017-2026, we used the best-fitted model to project the estimated cases for 2022-2028. The study results based on the models revealed different predicted estimates between both species. The model for P. falciparum suggested that zero P. falciparum cases might be possible by 2024, in contrast to the model for P. vivax, wherein zero P. vivax cases might not be reached. Innovative approaches in the P. vivax-specific control and elimination plans must be implemented to reach zero P. vivax and consequently declare Thailand as a malaria-free country.

Figure 1

Annual number of P. falciparum and P. vivax malaria cases from the national passive surveillance database during 2015–2021. Note: Total = Pf + Pv. (Figure prepared using RStudio 2022.02.3 + 492 "Prairie Trillium", www.r-project.org/).

Figure 2

Annual number of cases (log scale) in 77 Thai provinces and 6 Hotspot Provinces, during 2015–2021: (a) P. falciparum cases and (b) P. vivax cases. Note: log(1) = 0 and log(0) = − In. (Figure prepared using RStudio 2022.02.3 + 492 "Prairie Trillium", www.r-project.org/).

Figure 3

Maps of observed surveillance (2015–2021) and prediction (2022–2028) of total P. Falciparum cases per 100,000 population at provincial level. (Figure prepared using RStudio 2022.02.3 + 492 "Prairie Trillium", www.r-project.org/).

Figure 4

Maps of observed surveillance (2015–2021) and prediction (2022–2028) of total P. Vivax cases per 100,000 population at provincial level. (Figure prepared using RStudio 2022.02.3 + 492 "Prairie Trillium", www.r-project.org/).

Figure 5

Maps of prediction (2022–2028) of indigenous P. falciparum cases (top row) and P. vivax cases (bottom row) per 100,000 population at provincial level. (Figure prepared using RStudio 2022.02.3 + 492 "Prairie Trillium", www.r-project.org/).