Generative AI-Driven Decision-Making for Disease Control and Pandemic Preparedness Model 4.0 in Rural Communities of Bangladesh: Management Informatics Approach

• URL: http://arxiv.org/abs/2508.01142v1
• Date: Sat, 02 Aug 2025 01:54:16 GMT
• Title: Generative AI-Driven Decision-Making for Disease Control and Pandemic Preparedness Model 4.0 in Rural Communities of Bangladesh: Management Informatics Approach
• Authors: Mohammad Saddam Hosen, MD Shahidul Islam Fakir, Shamal Chandra Hawlader, Farzana Rahman, Tasmim Karim, Muhammed Habil Uddin,
• Abstract summary: Rural Bangladesh is confronted with substantial healthcare obstacles.<n>These obstacles impede effective disease control and pandemic preparedness.<n>The study concludes that the health resilience and pandemic preparedness of marginalized rural populations can be improved through AI-driven, localized disease control strategies.
• Score: 0.7067443325368975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rural Bangladesh is confronted with substantial healthcare obstacles, such as inadequate infrastructure, inadequate information systems, and restricted access to medical personnel. These obstacles impede effective disease control and pandemic preparedness. This investigation employs a structured methodology to develop and analyze numerous plausible scenarios systematically. A purposive sampling strategy was implemented, which involved the administration of a questionnaire survey to 264 rural residents in the Rangamati district of Bangladesh and the completion of a distinct questionnaire by 103 healthcare and medical personnel. The impact and effectiveness of the study are assessed through logistic regression analysis and a pre-post comparison that employs the Wilcoxon Signed-Rank test and Kendall's coefficient for non-parametric paired and categorical variables. This analysis evaluates the evolution of disease control and preparedness prior to and subsequent to the implementation of the Generative AI-Based Model 4.0. The results indicate that trust in AI (\b{eta} = 1.20, p = 0.020) and confidence in sharing health data (\b{eta} = 9.049, p = 0.020) are the most significant predictors of AI adoption. At the same time, infrastructure limitations and digital access constraints continue to be significant constraints. The study concludes that the health resilience and pandemic preparedness of marginalized rural populations can be improved through AI-driven, localized disease control strategies. The integration of Generative AI into rural healthcare systems offers a transformative opportunity, but it is contingent upon active community engagement, enhanced digital literacy, and strong government involvement.

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