METHODS: A cross-sectional study was conducted among 214 respondents in northeastern Malaysia using a multi-stage stratified random sampling method. The study population was divided into two groups based on geographical locations: urban and rural. All data were entered and analyzed using the IBM Statistics for Social Sciences (SPSS) version 22.0 software for Windows (IBM, Armonk, NY, USA). The continuous variables were presented using mean and standard deviation (SD), whereas the categorical variables were described using frequency and percentage. Multiple logistic regression was performed to determine the associated factors for good KABP toward leptospirosis among the respondents.
RESULTS: It was found that 52.8% of respondents had good knowledge, 84.6% had positive attitudes, 59.8% had positive beliefs, and 53.7% had satisfactory practices. There were no significant sociodemographic factors associated with knowledge and practice, except for educational status, which was significant in the attitude and belief domains. Those with higher education exhibited better attitudes (Odds Ratio (OR) 3.329; 95% Coefficient Interval (CI): 1.140, 9.723; p = 0.028) and beliefs (OR 3.748; 95% CI: 1.485, 9.459; p = 0.005). The communities in northeastern Malaysia generally have good knowledge and a high level of positive attitude; however, this attitude cannot be transformed into practice as the number of people with satisfactory practice habits is much lower compared to those with positive attitudes. As for the belief domain, the communities must have positive beliefs to perceive the threat of the disease.
CONCLUSIONS: Our current health program on preventing leptospirosis is good in creating awareness and a positive attitude among the communities, but is not sufficient in promoting satisfactory practice habits. In conclusion, more attention needs to be paid to promoting satisfactory practice habits among the communities, as they already possess good knowledge and positive attitudes and beliefs.
METHODS: An agent-based model (ABM) is a relatively new approach that provides a framework for analyzing the heterogeneity of the interactions, along with biological and environmental factors in such complex systems. The objective of this research is to design and develop an ABM that uses Geospatial Information System (GIS) capabilities, biological behaviors of vectors and reservoir hosts, and an improved Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model to explore the spread of ZCL. Various scenarios were implemented to analyze the future ZCL spreads in different parts of Maraveh Tappeh County, in the northeast region of Golestan Province in northeastern Iran, with alternative socio-ecological conditions.
RESULTS: The results confirmed that the spread of the disease arises principally in the desert, low altitude areas, and riverside population centers. The outcomes also showed that the restricting movement of humans reduces the severity of the transmission. Moreover, the spread of ZCL has a particular temporal pattern, since the most prevalent cases occurred in the fall. The evaluation test also showed the similarity between the results and the reported spatiotemporal trends.
CONCLUSIONS: This study demonstrates the capability and efficiency of ABM to model and predict the spread of ZCL. The results of the presented approach can be considered as a guide for public health management and controlling the vector population .
METHODOLOGY/PRINCIPAL FINDINGS: A total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. Predictions were then made throughout Southeast Asia, including regions where infection data are sparse. The resulting map predicts areas of high risk for P. knowlesi infection in a number of countries that are forecast to be malaria-free by 2025 (Malaysia, Cambodia, Thailand and Vietnam) as well as countries projected to be eliminating malaria (Myanmar, Laos, Indonesia and the Philippines).
CONCLUSIONS/SIGNIFICANCE: We have produced the first map of P. knowlesi malaria risk, at a fine-scale resolution, to identify priority areas for surveillance based on regions with sparse data and high estimated risk. Our map provides an initial evidence base to better understand the spatial distribution of this disease and its potential wider contribution to malaria incidence. Considering malaria elimination goals, areas for prioritised surveillance are identified.