Spatial Cluster Change of Schistosoma japonicum Transmission Foci in Indonesia During the Schistosomiasis Elimination Program.

PURPOSE: The Government of Indonesia committed to eliminating schistosomiasis by 2025. Collaboratively snail control became one of the crucial strategies to ensure that the prevalence of Schistosoma japonicum in Oncomelania hupensis lindoensis reaches zero by the end of the program. This research investigated the spatial cluster change of S. japonicum transmission foci in Indonesia between 2017 and 2021. METHODS: We mapped the snail foci, collected the snails, and calculated the snail density. We also conducted laboratory tests to detect the existence of cercariae in the snails. Identified infected snails were used to calculate the infection rate (IR) or snails' prevalence of schistosome cercariae among freshwater snails. We then analysed the spatial cluster using the Getis-Ord Gi* statistic to identify the hot and cold spots. RESULTS: The 5-year schistosomiasis elimination program successfully declined 18.84% of the snail foci and reduced 40.37% of the infected snail foci. Local spatial autocorrelation of snail density and infection rate identified that in 2017 and 2021, the number of cold spots decreased by 53.91% and 0%, while hot spots increased by 2.63% and 56.1%. The presence of more hot spots suggests a rise in the number of foci with high snail density and infection rates. The implementation of snail control was not optimal, and the parasite transmission through domestic animals still existed, causing the spatial cluster of hot spots to change during this period. Most hotspots have been observed near settlements, primarily in cocoa plantations, developed and deserted rice fields, grassland, and bush wetlands. CONCLUSION: During the schistosomiasis elimination program, the number of hot spots increased while cold spots decreased, and there were notable changes in the geographical distribution of hot spots, indicating a shift in the clustering pattern of schistosomiasis cases. The findings become essential for policymakers, particularly in selecting priority areas for intervention. In the Discussion section, we demonstrated the selection process based on the existence of hot and cold spots. Furthermore, we proposed that enhancing cross-sector integration is crucial, particularly in connection with the management of S. japonicum transmission through domestic animals.

A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia.

Here we present a geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference dataset collected by visual interpretation of very high spatial resolution imagery, in a national-scale crowdsourcing campaign (targeting seven generic LC classes) and a series of expert workshops (targeting seventeen detailed LC classes) in Indonesia. The interpreters were citizen scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the quality assessment indicators. We envisage that the dataset will be relevant for: (1) the LC mapping community (researchers and practitioners), i.e., as reference data for training machine learning algorithms and map accuracy assessment (with appropriate quality-filters applied), and (2) the citizen science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of contributions from the crowd/non-experts, demonstrated for LC mapping in Indonesia for the first time to our knowledge, within the context of complementing traditional data collection by expert interpreters.

Spatial modelling of malaria cases associated with environmental factors in South Sumatra, Indonesia.

BACKGROUND: Malaria, a parasitic infection, is a life-threatening disease in South Sumatra Province, Indonesia. This study aimed to investigate the spatial association between malaria occurrence and environmental risk factors. METHODS: The number of confirmed malaria cases was analysed for the year 2013 from the routine reporting of the Provincial Health Office of South Sumatra. The cases were spread over 436 out of 1613 villages. Six potential ecological predictors of malaria cases were analysed in the different regions using ordinary least square (OLS) and geographically weighted regression (GWR). The global pattern and spatial variability of associations between malaria cases and the selected potential ecological predictors was explored. RESULTS: The importance of different environmental and geographic parameters for malaria was shown at global and village-level in South Sumatra, Indonesia. The independent variables altitude, distance from forest, and rainfall in global OLS were significantly associated with malaria cases. However, as shown by GWR model and in line with recent reviews, the relationship between malaria and environmental factors in South Sumatra strongly varied spatially in different regions. CONCLUSIONS: A more in-depth understanding of local ecological factors influencing malaria disease as shown in present study may not only be useful for developing sustainable regional malaria control programmes, but can also benefit malaria elimination efforts at village level.