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<p> This study aimed to integrate Nigerian Pidgin English (NPE) into the Artificial Intelligence system EPIWATCH<sup>®</sup>, with the aim of providing early detection and enhanced surveillance of infectious disease outbreaks in Nigeria. The widespread use of NPE as a lingua franca, spoken by over 75&#xa0;million people in Africa, improves interaction and communication within communities across Nigeria. Key search terms were translated into NPE and incorporated into EPIWATCH<sup>®</sup> for surveillance. Using publicly available outbreak reports collected by EPIWATCH<sup>®</sup> from Nigeria between 2018 and 2023, we conducted a descriptive analysis to compare outbreak detection patterns before and after the integration of NPE into the system. We observed a 315% increase in EPIWATCH<sup>®</sup> outbreak reports in March 2024 compared to the March median across 2018 to 2023, following NPE language integration. This study demonstrates the potential of leveraging technology and linguistic diversity to improve disease surveillance using open source intelligence (OSINT) and response efforts in Nigeria. Findings should be interpreted in light of the short prospective time frame, scope, contextual factors, and public source constraints, which may limit generalisability and nuance in assessing the impact of NPE integration.&#xa0;</p>

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Early Detection and Surveillance of Infectious Disease Outbreaks in Nigeria 

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月13日

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  • Omolara Kolawole,
  • Ashley Quigley,
  • Abrar A. Chughtai,
  • Chandini R. MacIntyre

摘要

This study aimed to integrate Nigerian Pidgin English (NPE) into the Artificial Intelligence system EPIWATCH®, with the aim of providing early detection and enhanced surveillance of infectious disease outbreaks in Nigeria. The widespread use of NPE as a lingua franca, spoken by over 75 million people in Africa, improves interaction and communication within communities across Nigeria. Key search terms were translated into NPE and incorporated into EPIWATCH® for surveillance. Using publicly available outbreak reports collected by EPIWATCH® from Nigeria between 2018 and 2023, we conducted a descriptive analysis to compare outbreak detection patterns before and after the integration of NPE into the system. We observed a 315% increase in EPIWATCH® outbreak reports in March 2024 compared to the March median across 2018 to 2023, following NPE language integration. This study demonstrates the potential of leveraging technology and linguistic diversity to improve disease surveillance using open source intelligence (OSINT) and response efforts in Nigeria. Findings should be interpreted in light of the short prospective time frame, scope, contextual factors, and public source constraints, which may limit generalisability and nuance in assessing the impact of NPE integration.