Spatio‐temporal distribution and hotspots of Plasmodium knowlesi infections in Sarawak, Malaysian Borneo

• Main Authors: Nur Emyliana, Yunos, Sharkawi, Hamidi Mohamad, Hii, King Ching, Hu, Ting Huey, Dayang Shuaisah, Awang Mohamad, Rosli, Nawal, Tarmiji, Masron, Balbir, Singh, Divis, Paul Cliff Simon
• Format: Article
• Language: English
• Published: Springer Nature 2022
• Subjects: QR Microbiology; RA0421 Public health. Hygiene. Preventive Medicine
• Online Access: http://ir.unimas.my/id/eprint/40173/3/Spatio%E2%80%91temporal%20-%201.pdf; http://ir.unimas.my/id/eprint/40173/; https://www.nature.com/srep/; https://doi.org/10.1038/s41598-022-21439-2

Plasmodium knowlesi infections in Malaysia are a new threat to public health and to the national efforts on malaria elimination. In the Kapit division of Sarawak, Malaysian Borneo, two divergent P. knowlesi subpopulations (termed Cluster 1 and Cluster 2) infect humans and are associated with long-tailed macaque and pig-tailed macaque hosts, respectively. It has been suggested that forest-associated activities and environmental modifications trigger the increasing number of knowlesi malaria cases. Since there is a steady increase of P. knowlesi infections over the past decades in Sarawak, particularly in the Kapit division, we aimed to identify hotspots of knowlesi malaria cases and their association with forest activities at a geographical scale using the Geographic Information System (GIS) tool. A total of 1064 P. knowlesi infections from 2014 to 2019 in the Kapit and Song districts of the Kapit division were studied. Overall demographic data showed that males and those aged between 18 and 64 years old were the most frequently infected (64%), and 35% of infections involved farming activities. Thirty-nine percent of Cluster 1 infections were mainly related to farming surrounding residential areas while 40% of Cluster 2 infections were associated with activities in the deep forest. Average Nearest Neighbour (ANN) analysis showed that humans infected with both P. knowlesi subpopulations exhibited a clustering distribution pattern of infection. The Kernel Density Analysis (KDA) indicated that the hotspot of infections surrounding Kapit and Song towns were classified as high-risk areas for zoonotic malaria transmission. This study provides useful information for staff of the Sarawak State Vector-Borne Disease Control Programme in their efforts to control and prevent zoonotic malaria.