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 .
METHODS: In 2013, a total of 1744 dried blood spots (DBS) were obtained from residents of 8 longhouses who appeared healthy. Subsequently, 251 venous blood samples were collected from residents of 2 localities in 2014 based on the highest number of submicroscopic cases from prior findings. Thin and thick blood films were prepared from blood obtained from all participants in this study. Microscopic examination were carried out on all samples and a nested and nested multiplex PCR were performed on samples collected in 2013 and 2014 respectively.
RESULTS: No malaria parasites were detected in all the Giemsa-stained blood films. However, of the 1744 samples, 29 (1.7%) were positive for Plasmodium vivax by PCR. Additionally, of the 251 samples, the most prevalent mono-infection detected by PCR was Plasmodium falciparum 50 (20%), followed by P. vivax 39 (16%), P. knowlesi 9 (4%), and mixed infections 20 (8%).
CONCLUSIONS: This research findings conclude evidence of Plasmodium by PCR, among samples previously undetectable by routine blood film microscopic examination, in local ethnic minority who are clinically healthy. SMM in Belaga district is attributed not only to P. vivax, but also to P. falciparum and P. knowlesi. In complementing efforts of programme managers, there is a need to increase surveillance for SMM nationwide to estimate the degree of SMM that warrant measures to block new transmission of malaria.
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.