Observing the Unobserved: A Newspaper Based Dengue Surveillance System for the Low-Income Regions of Bangladesh

Dengue is one of the emerging diseases of this century, which established itself as both endemic and epidemic—particularly in the tropical and subtropicalregions. Because of its high morbidity and mortality rates, Dengue is a significant economic and health burden for middle to lower-income countries. The lack of a stable, cost-effective, and suitable surveillance system has made the identification of dengue zones and designing potential control programs very challenging. As a result, it is not feasible to assess the effect of the intervention actions properly. Therefore, most of the prevention and mitigation efforts by the associated health officials are failing. In this work, we chose Bangladesh, a developing country from the South-East Asia region with its occasional history of dengue outbreaks and with a high out-of-pocket medical expenditure, as a use case. We use some well known data-mining techniques on the local newspapers, written in Bengali, to unearth valuable insights and develop a dengue news surveillance system. We categorize dengue-news and detect the spatiotemporal trends among crucial variables. Our technique provides an f-score of 91.45% and very closely follows the ground truth of reported cases. Additionally, we identify the under-reported regions of the country effectively while establishing a meaningful relationship between complex socio-economic factors and reporting of dengue.