Community-Based Risk Mapping against Vector-Borne Disease Outbreak Using Smartphone Microscope and Geospatial Technology

Malaria is a vector-borne disease spread by the bites of female Anopheles mosquitoes. According to WHO, 247 million new cases of malaria worldwide were identified in 95 countries and territories only in 2021. There are an estimated 600,000 deaths caused by malaria worldwide each year. Thus, effective preventive measures are needed to control this threat. The geographic distribution of Anopheles mosquitoes at the community level is often lacking, and yet this knowledge is vital for malaria prevention efforts. Currently, field data sampling in remote rural areas is challenging due to accessibility issues and lack of people with skills to collect samples and report results. To this end, developing the methodology to collect information on larvae distribution in rural areas using simplified tools for the local population would be effective to predict and prevent the potential malaria outbreak. This study proposes a methodology for collecting larvae sample images using a smartphone microscope in collaboration with the local community in Lao People’s Democratic Republic (PDR) and briefly analyzes the Anopheles’ habitat and patient distribution for malaria case management by overlaying spatial maps. The methodology of collecting and identifying Anopheles larvae samples using a combination of smartphone microscopes and community-based sampling was verified. This methodology could be extended to other vector-borne diseases such as dengue, which requires quicker response against the outbreak. As dengue tends to spread in urban areas, the methodology for risk mapping could be improved by incorporating other data such as geo-tagged social media information. This study shows how integrating community-based data and information with other geospatial data can effectively work to predict and prevent the outbreak of communal disease.