Spatiotemporal Clustering of Malaria in South Sumatra, Indonesia, Based on Surveillance Data, 2018–2022

Background Malaria remains a pressing public health issue in South Sumatra, Indonesia, despite significant progress in global elimination efforts. This study aimed to identify the spatiotemporal clustering of malaria cases from 2018 to 2022 across six endemic regencies: Muara Enim, Lahat, Musi Rawas, Ogan Komering Ulu (OKU), OKU Selatan (OKUS) and OKU Timur (OKUT). Methods We analysed malaria surveillance data obtained from the Malaria Surveillance Information System (SISMAL) of Indonesia, which was geocoded at the village level. Spatial and temporal trends were examined using Anselin’s Local Moran’s I to detect local spatial autocorrelation and Kulldorff’s space‐time scan statistic to identify significant spatiotemporal clusters. Analyses were conducted using GeoDa, SaTScan and QGIS. Results A total of 13 high–high (HH) clusters were identified in OKUS and Muara Enim in 2018, while 12 HH clusters appeared in Muara Enim in 2019. Between 2020 and 2022, no HH clusters were detected, although several high–low (HL) outliers persisted. This study identified four statistically significant space‐time clusters ( p < 0.01) between 2018 and 2022. The spatiotemporal patterns were shaped by environmental changes, particularly land‐use dynamics and seasonal variations, while human mobility further contributed to transmission heterogeneity. Conclusions Despite an overall decline in malaria cases, the persistence of HL outliers signals ongoing transmission risk and the need for geographically targeted interventions. Spatiotemporal clustering analysis offers crucial insights for enhancing surveillance sensitivity and refining malaria control strategies. Strengthening integrated vector management and adaptive public health responses is essential to accelerate progress toward malaria elimination in endemic regions, such as South Sumatra.