TY - JOUR
T1 - Mapping malaria in Thailand
T2 - A Bayesian spatio-temporal analysis of national surveillance data
AU - Pratumchart, Khanittha
AU - Thinkhamrop, Kavin
AU - Suwannatrai, Kulwadee
AU - Sudathip, Prayuth
AU - Kitchakarn, Suravadee
AU - Tam, Le Thanh
AU - Soukavong, Mick
AU - Varnakovida, Pariwate
AU - Boonmars, Thidarut
AU - Wisetmora, Ampas
AU - Moukomla, Sitthisak
AU - Clements, Archie C A
AU - Wangdi, Kinley
AU - Suwannatrai, Apiporn T
N1 - © 2025 John Wiley & Sons Ltd.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Bayesian analysis
KW - climatic
KW - envrionmental
KW - malaria
KW - modelling
KW - Plasmodium
KW - spatio-temporal analysis
KW - Thailand
U2 - 10.1111/tmi.14101
DO - 10.1111/tmi.14101
M3 - Article
C2 - 40047195
SN - 1360-2276
SP - 1
EP - 13
JO - Tropical and Geographical Medicine
JF - Tropical and Geographical Medicine
ER -