Abstract
Melioidosis, an emerging tropical infectious disease and a global threat, lacks a disease prediction model owing to the limited data on case incidences and associated factors. This article focuses on spatial data analysis of melioidosis patients in India, a tropical country considered to be endemic for the disease, to identify gaps in disease reporting. In this study, we screened over 20,000 articles and identified 1,694 patients diagnosed with melioidosis in India between 1953 and 2023. We performed a correlative analysis of patient profiles, case-reporting centres, common misdiagnosed etiologies, susceptible populations, agrometeorological and socioeconomic factors. Our findings suggest that melioidosis can affect individuals of all ages, with a higher prevalence among farmers and individuals with diabetes mellitus, especially adults aged 40-55 years. Most cases are reported during the monsoon season (June to September). Numerous favorable conditions for Burkholderia pseudomallei growth exist across India. However, most reported cases are from the south, suggesting under-reporting, under-diagnosis, or misdiagnosis. A "Melioidosis Checklist Index" has been developed as a surveillance tool to enhance case reporting. The melioidosis checklist index combines patient demographics, clinical symptoms, medical and family history, exposure risks, occupation, travel history, and lifestyle factors to assess infection likelihood. Probability scores derived from these parameters provide a structured aid for early diagnosis and case reporting. The study also highlighted the need to enhance regional data collection by raising awareness among vulnerable populations, healthcare providers, and paramedical staff through government initiatives.