Cryptosporidiosis and cyclosporiasis are caused by waterborne coccidian protozoan parasites of the genera Cryptosporidium and Cyclospora, respectively. This study was conducted to detect Cryptosporidium and Cyclospora oocysts from environmental water abstracted by drinking water treatment plants and recreational activities in Sarawak, Malaysia. Water samples (12 each) were collected from Sungai Sarawak Kanan in Bau and Sungai Sarawak Kiri in Batu Kitang, respectively. In addition, 6 water samples each were collected from Ranchan Recreational Park and UNIMAS Lake at Universiti Malaysia Sarawak, Kota Samarahan, respectively. Water physicochemical parameters were also recorded. All samples were concentrated by the iron sulfate flocculation method followed by the sucrose floatation technique. Cryptosporidium and Cyclospora were detected by modified Ziehl-Neelsen technique. Correlation of the parasites distribution with water physicochemical parameters was analysed using bivariate Pearson correlation. Based on the 24 total samples of environmental water abstracted by drinking water treatment plants, all the samples (24/24; 100%) were positive with Cryptosporidium, and only 2 samples (2/24; 8.33%) were positive with Cyclospora. Based on the 12 total samples of water for recreational activities, 4 samples (4/12; 33%) were positive with Cryptosporidium, while 2 samples (2/12; 17%) were positive with Cyclospora. Cryptosporidium oocysts were negatively correlated with dissolved oxygen (DO).
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.