References

IJMABHR

International Journal of Medical and All Body Health Research

IJMABHR

2582-8940

Anfo Publication House

430

10.54660/IJMBHR.2023.4.4.66-74

Integrating Artificial Intelligence into Epidemiological Intelligence Systems to Strengthen Disease Control, Biosecurity, and Pandemic Response

Olalere

Sarafa Olumide

Uzu-Okoh

Joy Ejenavi

1Department of Preventive and Social Medicine, University of Port Harcourt, Port Harcourt, Rivers State, Nigeria 2Department of Nursing, University of Benin, Benin City, Edo State, Nigeria

2023

4 4 66 74 13112023 10122023

CC BY-NC-SA 4.0

The incorporation of Artificial Intelligence (AI) into epidemiological intelligence systems offers a revolutionary chance to improve pandemic response, biosecurity, and disease control. Due to its heavy reliance on human data collection, reporting, and analysis, traditional epidemiological surveillance frequently has delays, underreporting, and poor prediction ability. Real-time data processing from diverse sources, including electronic health records, social media, environmental sensors, and genomic surveillance, is made possible by AI-driven techniques including machine learning, natural language processing, and predictive analytics. By facilitating early epidemic detection, dynamic risk modeling, and focused intervention tactics, this integration enhances the promptness and precision of public health responses. This study explores integrating Artificial intelligence into epidemiological intelligence system to strengthen disease control, biosecurity and pandemic response adopting PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. The review systematically collated evidence on AI-enabled epidemiological intelligence systems, highlighting their contribution to early warning, outbreak management, and cross-sectoral coordination. Findings reveal that the use of AI improves biosecurity frameworks and enables proactive mitigation. Therefore, adopting AI-enhanced epidemiological knowledge strategically can improve pandemic preparedness, bolster national and international health security, and lessen the social and economic effects of infectious disease epidemics. This approach represents a critical evolution in public health practice, combining advanced computational tools with traditional epidemiological expertise to create resilient, adaptive, and proactive health intelligence systems.

Artificial IntelligenceEpidemiological IntelligenceDisease ControlBiosecurityPandemic Response

References 1

Abdel-Rahman SM, Gill H, Carpenter SL, Gueye P, Wicklund B, Breitkreutz M, et al. Design and usability of an electronic health record-integrated, point-of-care, clinical decision support tool for modeling and simulation of Antihemophilic factors. Appl Clin Inform. 2020;11(2):253-64.

Adebowale AM, Akinnagbe OB. Leveraging AI-driven data integration for predictive risk assessment in decentralized financial markets. Int J Eng Technol Res Manag. 2021;5(12):295.

Adenuga T, Ayobami AT, Okolo FC. AI-Driven International Journal of Medical and All Body Health Research www.allmedicaljournal.com Workforce Forecasting for Peak Planning and Disruption Resilience in Global Logistics and Supply Networks. Int J Multidiscip Res Growth Eval. 2020;2(2):71-87.

Agbehadji IE, Awuzie BO, Ngowi AB, Millham RC. Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing. Int J Environ Res Public Health. 2020;17(15):5330.

Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020;2020:baaa010.

Albalawi U, Mustafa M. Current artificial intelligence (AI) techniques, challenges, and approaches in controlling and fighting COVID-19: a review. Int J Environ Res Public Health. 2022;19(10):5901.

Alfred R, Obit JH. The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review. Heliyon. 2021;7(6):e07220.

Asokan GV, Mohammed MY. Harnessing big data to strengthen evidence-informed precise public health response. In: Big data in psychiatry & neurology. London: Academic Press; 2021. p. 325-37.

Auld G, Casovan A, Clarke A, Faveri B. Governing AI through ethical standards: Learning from the experiences of other private governance initiatives. J Eur Public Policy. 2022;29(11):1822-44.

Balahur A, Jenet A, Hupont IT, Charisi V, Ganesh A, Griesinger CB, et al. Data quality requirements for inclusive, non-biased and trustworthy AI. Luxembourg: Publications Office of the European Union; 2022.

Bauskar SR, Madhavaram CR, Galla EP, Sunkara JR, Gollangi HK. Predicting disease outbreaks using AI and Big Data: A new frontier in healthcare analytics. Eur Chem Bull. 2022;11(12):17745.

Bhaskaran SV. Integrating data quality services (dqs) in big data ecosystems: Challenges, best practices, and opportunities for decision-making. J Appl Big Data Anal Decis Mak Predict Model Syst. 2020;4(11):1-12.

Capobianco R, Kompella V, Ault J, Sharon G, Jong S, Fox S, et al. Agent-based Markov modeling for improved COVID-19 mitigation policies. J Artif Intell Res. 2021;71:953-92.

Chakilam C. Integrating Machine Learning and Big Data Analytics to Transform Patient Outcomes in Chronic Disease Management. J Surv Fish Sci. 2022;9(3):118-30.

Chianumba EC, Ikhalea NUR, Mustapha AY, Forkuo AY, Osamika DAMILOLA. A conceptual framework for leveraging big data and AI in enhancing healthcare delivery and public health policy. IRE Journals. 2021;5(6):303-10.

Clemmensen LH, Kjærsgaard RD. Data representativity for machine learning and AI systems. arXiv [Preprint]. 2022 [cited 2025 Jan 10]. Available from: https://arxiv.org/abs/2203.04706.

Cole B. Essential components of a public health tuberculosis prevention, control, and elimination program: recommendations of the Advisory Council for the Elimination of Tuberculosis and the National Tuberculosis Controllers Association. MMWR Recomm Rep. 2020;69(7):1-27.

De Mello BH, Rigo SJ, Da Costa CA, da Rosa Righi R, Donida B, Bez MR, et al. Semantic interoperability in health records standards: a systematic literature review. Health Technol (Berl). 2022;12(2):255-72.

Erondu NA, Rahman-Shepherd A, Khan MS, Abate E, Agogo E, Belfroid E, et al. Improving National Intelligence for Public Health Preparedness: a methodological approach to finding local multi-sector indicators for health security. BMJ Glob Health. 2021;6(1):e004227.

Escobar CA, McGovern ME, Morales-Menendez R. Quality 4.0: a review of big data challenges in manufacturing. J Intell Manuf. 2021;32(8):2319-34.

Gao A, Murphy RR, Chen W, Dagnino G, Fischer P, Gutierrez MG, et al. Progress in robotics for combating infectious diseases. Sci Robot. 2021;6(52):eabf1462.

Gruel G, Diouf MB, Abadie C, Chilin-Charles Y, Etter EMC, Geffroy M, et al. Critical evaluation of cross-sectoral collaborations to inform the implementation of the “one health” approach in Guadeloupe. Front Public Health. 2021;9:652079.

Hinton R, Armstrong C, Asri E, Baesel K, Barnett S, Blauvelt C, et al. Specific considerations for research on the effectiveness of multisectoral collaboration: methods and lessons from 12 country case studies. Global Health. 2021;17(1):18.

Huck A, Monstadt J, Driessen PP, Rudolph-Cleff A. Towards Resilient Rotterdam? Key conditions for a networked approach to managing urban infrastructure risks. J Contingencies Crisis Manag. 2021;29(1):12-22.

Ibeneme S, Okeibunor J, Muneene D, Husain I, Bento P, Gaju C, et al. Data revolution, health status transformation and the role of artificial intelligence for health and pandemic preparedness in the African context. BMC Proc. 2021;15(Suppl 15):22.

Ikhalea N, Chianumba EC, Mustapha AY, Forkuo AY. A Conceptual Framework for AI-Driven Early Detection of Chronic Diseases Using Predictive Analytics. 2022. [Note: Journal/source details incomplete in original; formatted as available.]

Jabarulla MY, Lee HN. A blockchain and artificial intelligence-based, patient-centric healthcare system for combating the COVID-19 pandemic: Opportunities and applications. Healthcare (Basel). 2021;9(8):1019.

Jimmy F. Emerging threats: The latest cybersecurity risks and the role of artificial intelligence in enhancing cybersecurity defenses. Valley Int J Digit Libr. 2021;1:564-74.

Kaur I, Behl T, Aleya L, Rahman H, Kumar A, Arora S, et al. Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic. Environ Sci Pollut Res Int. 2021;28(30):40515-32.

Kavanagh C. New Tech, New Threats, and New Governance Challenges: An Opportunity to Craft Smarter Responses? Washington, DC: Carnegie Endowment for International Peace; 2022.

Kerry VB, Ahaisibwe B, Malewezi B, Ngoma D, Daoust P, Stuart-Shor E, et al. Partnering to build human resources for health capacity in Africa: a descriptive review of the global health service partnership’s innovative model for health professional education and training from 2013-2018. Int J Health Policy Manag. 2020;11(7):919-28.

Komi LS, Chianumba EC, Yeboah A, Forkuo DO, International Journal of Medical and All Body Health Research www.allmedicaljournal.com Mustapha AY. A conceptual framework for telehealth integration in conflict zones and post-disaster public health responses. Iconic Res Eng J. 2021;5(6):342-59.

Kyrkou C, Kolios P, Theocharides T, Polycarpou M. Machine learning for emergency management: A survey and future outlook. Proc IEEE. 2022;111(1):19-41.

Lal A, Ashworth HC, Dada S, Hoemeke L, Tambo E. Optimizing pandemic preparedness and response through health information systems: lessons learned from Ebola to COVID-19. Disaster Med Public Health Prep. 2022;16(1):333-40.

Lefèvre T, Colineaux H, Morgand C, Tournois L, Delpierre C. AI, Epidemiology and Public Health in the Covid Pandemic. In: Artificial Intelligence in Covid-19. Cham: Springer International Publishing; 2022. p. 279-338.

LoTempio J, Spencer D, Yarvitz R, Vilain AD, Vilain E, Délot E. We can do better: lessons learned on data sharing in COVID-19 pandemic can inform future outbreak preparedness and response. Sci Diplom. 2020;9(2).

Mazzucato M, Schaake M, Krier S, Entsminger J. Governing artificial intelligence in the public interest. 2022. [Note: Publisher/source details incomplete; formatted as available.]

Meckawy R, Stuckler D, Mehta A, Al-Ahdal T, Doebbeling BN. Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review. BMC Public Health. 2022;22(1):2216.

Morin O, Vallières M, Braunstein S, Ginart JB, Upadhaya T, Woodruff HC, et al. An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. Nat Cancer. 2021;2(7):709-22.

Mremi IR, George J, Rumisha SF, Sindato C, Kimera SI, Mboera LE. Twenty years of integrated disease surveillance and response in Sub-Saharan Africa: challenges and opportunities for effective management of infectious disease epidemics. One Health Outlook. 2021;3(1):22.

Muggah R, Whitlock M. Reflections on the evolution of conflict early warning. Stab Int J Secur Dev. 2022;10(1).

Noorazar H, Srivastava A, Pannala S, Sadanandan KS. Data-driven operation of the resilient electric grid: A case of COVID-19. J Eng. 2021;2021(11):665-84.

Pastor-Escuredo D, Treleaven P, Vinuesa R. An ethical framework for artificial intelligence and sustainable cities. AI. 2022;3(4):961-74.

Penberthy LT, Rivera DR, Lund JL, Bruno MA, Meyer AM. An overview of real-world data sources for oncology and considerations for research. CA Cancer J Clin. 2022;72(3):287-300.

Pokhrel P, Hu C, Mao H. Detecting the coronavirus (COVID-19). ACS Sens. 2020;5(8):2283-96.

Putra CD. Brief Introduction to Hospital Information System. In: Indonesia Post-Pandemic Outlook: Environment and Technology Role for Indonesia Development. 2022. p. 297.

Ravichandran P, Machireddy JR, Rachakatla SK. AI-Enhanced data analytics for real-time business intelligence: Applications and challenges. J AI Healthc Med. 2022;2(2):168-95.

Richardson N. Emergency Response Planning: Leveraging Machine Learning for Real-Time Decision-Making. Emergency. 2021;4:14.

Riswantini D, Nugraheni E. Machine learning in handling disease outbreaks: a comprehensive review. Bull Electr Eng Inform. 2022;11(4):2169-86.

Rolnick D, Donti PL, Kaack LH, Kochanski K, Lacoste A, Sankaran K, et al. Tackling climate change with machine learning. ACM Comput Surv. 2022;55(2):1-96.

Schlebusch L, Chambers NJ, Dawson-Squibb JJ, Harty M, Franz L, de Vries PJ. Challenges and opportunities of implementing early interventions for autism spectrum disorders in resource-limited settings: A South African example. In: Starting at the Beginning. 2020. p. 99-132.

Song X, Zhang H, Akerkar R, Huang H, Guo S, Zhong L, et al. Big data and emergency management: concepts, methodologies, and applications. IEEE Trans Big Data. 2020;8(2):397-419.

Sundaramurthy SK, Ravichandran N, Inaganti AC, Muppalaneni R. AI-powered operational resilience: Building secure, scalable, and intelligent enterprises. Artif Intell Mach Learn Rev. 2022;3(1):1-10.

Taimoor N, Rehman S. Reliable and resilient AI and IoT-based personalised healthcare services: A survey. IEEE Access. 2021;10:535-63.

Traversi D, Pulliero A, Izzotti A, Franchitti E, Iacoviello L, Gianfagna F, et al. Precision medicine and public health: new challenges for effective and sustainable health. J Pers Med. 2021;11(2):135.

Uddin MI, Ali Shah SA, Al-Khasawneh MA, Alarood AA, Alsolami E. Optimal policy learning for COVID-19 prevention using reinforcement learning. J Inf Sci. 2022;48(3):336-48.

Uddoh J, Ajiga D, Okare BP, Aduloju TD. Next-Generation Business Intelligence Systems for Streamlining Decision Cycles in Government Health Infrastructure. J Front Multidiscip Res. 2021;2(1):303-11.

Yang H, Zhang S, Liu R, Krall A, Wang Y, Ventura M, et al. Epidemic informatics and control: A holistic approach from system informatics to epidemic response and risk management in public health. In: AI and Analytics for Public Health - Proceedings of the 2020 INFORMS International Conference on Service Science. Berlin/Heidelberg: Springer; 2021. p. 1-46.

Yeh KB, Parekh FK, Tabynov K, Hewson R, Fair JM, Essbauer S, et al. Operationalizing cooperative research for infectious disease surveillance: lessons learned and ways forward. Front Public Health. 2021;9:659695.

Yigitcanlar T, Desouza KC, Butler L, Roozkhosh F. Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies. 2020;13(6):1473.