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Malaria is one of Ethiopia's most targeted communicable diseases for elimination. Malaria transmission varies significantly across space and time; and Ethiopia had space–time disparity in its transmission intensities. Considering heterogeneity and transmission intensity at the district level could play a crucial role in malaria prevention and elimination. This study aimed to explore temporal, spatial, and spatiotemporal clusters of malaria incidence in northwest Ethiopia. The analysis is based on monthly malaria surveillance data of districts and collected from the Amhara public health institute. The Kulldorff's retrospective space–time scan statistics using a discrete Poisson model were used to detect temporal, spatial, and space–time clusters of malaria incidence with and without adjusting the altitude + LLIN arm. Monthly malaria incidence had seasonal variations, and higher seasonal indices occurred in October and November. The temporal cluster occurred in the higher transmission season between September and December annually. The higher malaria incidence risk occurred between July 2012 and December 2013 (LLR = 414,013.41, RR = 2.54, P &lt; 0.05). The purely spatial clustering result revealed that the most likely cluster occurred in the north and northwest parts of the region while secondary clusters varied in years. The space–time clusters were detected with and without considering altitude + LLIN arm. The most likely space–time cluster was concentrated in northwestern and western parts of the region with a high-risk period between July 2012 and December 2013 (LLR = 880,088.3, RR = 5.5, P &lt; 0.001). We found eight significant space–time clusters using the altitude + LLIN arm. The most likely space–time cluster occurred in the western and northwestern parts of the region in July 2012–December 2013 (LLR = 886,097.7, RR = 5.55, P &lt; 0.05). However, secondary clusters were located in eastern, northwestern, western parts of regions, which had different cases and relative risks in each cluster. Malaria transmission had temporal, spatial, and space–time variation in the region at the district level. Hence, considering these variations and factors contributing to malaria stratification would play an indispensable role in preventing and controlling practices that ultimately leads to malaria eliminations.

Detection of temporal, spatial and spatiotemporal clustering of malaria incidence in northwest Ethiopia, 2012–2020