Related Experiment Video
Updated: May 24, 2025
Methods to Increase the Sensitivity of High Resolution Melting Single Nucleotide Polymorphism Genotyping in Malaria
Published on: November 10, 2015
Mapping malaria in Thailand: A Bayesian spatio-temporal analysis of national surveillance data
Khanittha Pratumchart1, Kavin Thinkhamrop2, Kulwadee Suwannatrai3
1Department of Parasitology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Objectives:
Malaria, caused by protozoan parasites of the genus Plasmodium, remains prevalent in tropical and subtropical regions. This study employed Bayesian spatio-temporal analysis to assess malaria incidence patterns and identify environmental and climatic correlates across Thailand at the district level.
Methods:
We analysed national malaria surveillance data using Bayesian hierarchical models to examine spatio-temporal patterns in malaria incidence. The model incorporated random effects to account for unobserved heterogeneity across locations and over time, enabling robust inferences on the relationships between environmental and climatic factors and malaria incidence.
Results:
This analysis revealed seasonal malaria incidence patterns related to environmental and climatic factors, particularly Plasmodium vivax and Plasmodium falciparum. A 1°C increase in maximum temperature at a 6-month lag was associated with an 8% increase in P. vivax incidence (relative risk [RR] = 1.08; 95% credible interval [CrI]: 1.06-1.10). Additionally, a 0.1-unit increase in normalised difference vegetation index corresponded to an 11.96-fold increase in P. vivax cases (95% CrI: 9.36-15.38), while each 100 mm increase in precipitation led to an 8% rise (RR: 1.08; 95% CrI: 1.06-1.09). For P. falciparum, a 0.1-unit increase in normalised difference vegetation index correlated with an 11.59-fold increase in incidence (95% CrI: 8.29-16.16). The risk of P. falciparum increased by 15% per 100 mm increase in precipitation (RR = 1.15; 95% CrI: 1.13-1.17) and by 4% for each 1°C rise in maximum temperature (RR = 1.04; 95% CrI: 1.02-1.06). Elevated incidence was predominantly observed along the Thai-Cambodian and Thai-Myanmar borders, with central Thailand classified as low risk.
Conclusion:
These findings highlight the significance of integrating environmental and climatic factors into malaria control strategies. The insights gained can guide the Thai government's resource allocation for effective surveillance, treatment, and preventive measures, ultimately supporting malaria control and elimination efforts in the region.
