Analysis of Infectious Disease Outbreaks in EPIWATCH® Before and After Including Nepali Language Search Terms

Abstract

Purpose:

This study aimed to evaluate the impact of integrating Nepali language search terms into EPIWATCH®, an AI-based open-source epidemic surveillance system. The data was then compared to the Early Warning and Reporting System (EWARS) Nepal and HealthMap.

Methods:

EPIWATCH® can detect early signals of disease outbreaks in 51 languages, including English. In August 2021, Nepali language search terms were added to EPIWATCH®, and by October 2021, the system began scanning local Nepali news articles. The study analysed EPIWATCH® outbreaks reports from Nepal before and after adding the Nepali search terms.

Results:

Of the 517 reports EPIWATCH® identified between January 1, 2018, and June 30, 2023, 43.1% (223) came from Nepali-language articles, mostly detected after adding Nepali search terms. EPIWATCH® identified three times more outbreak reports from Nepal than HealthMap in the year after adding Nepali search terms (271 from October 2021 to September 2022, compared to 42 from October 2020 to September 2021). EPIWATCH® sometimes reported more infectious cases than EWARS Nepal, especially for Dengue (>28,000 vs 8,923) and Cholera (961 vs 58) from October 2021 to September 2022.

Conclusion:

The study demonstrated the increased efficiency of EPIWATCH® following the integration of Nepali language search terms, which enhanced its ability to scan local news articles. This research underscores the importance of language-specific Open-source intelligence (OSINT) in detecting infectious disease outbreaks and highlights the need for incorporating local languages into surveillance systems for more effective grassroots-level monitoring.