Exploring the Scope and Potential of Local Newspaper-based Dengue Surveillance in Bangladesh

TL;DR

This paper investigates the use of local newspaper content and data-mining techniques to develop a cost-effective, timely dengue surveillance system in Bangladesh, achieving high classification accuracy and identifying high-risk regions.

Contribution

It introduces a novel newspaper-based dengue surveillance framework with a human-in-the-loop database creation process and demonstrates its effectiveness in Bangladesh.

Findings

Classification f-score of 91.45%

Close match with reported dengue cases

Identification of high-risk regions for intervention

Abstract

Dengue fever has been considered to be one of the global public health problems of the twenty-first century, especially in tropical and subtropical countries of the global south. The high morbidity and mortality rates of Dengue fever impose a huge economic and health burden for middle and low-income countries. It is so prevalent in such regions that enforcing a granular level of surveillance is quite impossible. Therefore, it is crucial to explore an alternative cost-effective solution that can provide updates of the ongoing situation in a timely manner. In this paper, we explore the scope and potential of a local newspaper-based dengue surveillance system, using well-known data-mining techniques, in Bangladesh from the analysis of the news contents written in the native language. In addition, we explain the working procedure of developing a novel database, using human-in-the-loop technique, for further analysis, and classification of dengue and its intervention-related news. Our classification method has an f-score of 91.45%, and matches the ground truth of reported cases quite closely. Based on the dengue and intervention-related news, we identified the regions where more intervention efforts are needed to reduce the rate of dengue infection. A demo of this project can be accessed at: http://erdos.dsm.fordham.edu:3009/