Previously submitted to: JMIR Public Health and Surveillance (no longer under consideration since Oct 20, 2025)
Date Submitted: Jun 7, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Feasibility of Implementing AI-Driven Disease Surveillance and Early Warning Systems at International Airports in Sub-Saharan Africa: A case study of Julius Nyerere International Airport (JNIA), Tanzania: A systematic review
ABSTRACT
Background:
The increasing volume of international air travel has significantly contributed to the spread of infectious diseases, making airports critical points for disease surveillance. However, many airports in Sub-Saharan Africa (SSA), such as Julius Nyerere International Airport (JNIA) in Tanzania, still rely on basic methods like temperature screening due to limited resources. This systematic review evaluates the feasibility of implementing AI-driven disease surveillance systems at SSA airports, particularly JNIA, and identifies barriers to adoption.
Objective:
To evaluate the feasibility and potential impact of implementing AI-driven disease surveillance systems at airports in SSA, with a specific focus on Julius Nyerere International Airport (JNIA) in Tanzania, and provide strategies to overcome the challenges hindering the implementation of AI-driven systems at airports in resource-limited settings.
Methods:
A systematic review following PRISMA guidelines was conducted. Databases such as PubMed, ScienceDirect, and Web of Science were searched for studies published from 2019 to 2025 on AI applications in disease surveillance at airports. Risk of bias was assessed using ROBINS-I, AMSTAR 2, and CASP tools. Studies were analyzed narratively to identify key themes and challenges.
Results:
A total of 45 studies and seven policy frameworks were included. AI systems demonstrated potential for improving disease detection accuracy and response time. Barriers identified include inadequate infrastructure, limited workforce training, and concerns about data privacy and algorithmic bias. Key recommendations include investing in infrastructure, workforce development, and tailored ethical guidelines.
Conclusions:
AI implementation at SSA airports is feasible but requires overcoming barriers related to infrastructure, training, and regulatory frameworks. International collaboration and policy development are essential for successfully adopting AI in public health surveillance.
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Copyright
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