IJRISS Logo

AI-Driven Predictive Analytics for Multi-Hazard Infectious Disease Outbreak Forecasting in Low- And Middle-Income Countries: A Systematic Review and Meta-Analysis of Multimodal Data Fusion Approaches, Implementation Barriers, and Equity Pathways

Authors

Moses Luke

School of Public Health, University of Port Harcourt, Rivers State; University of Maiduguri (Nigeria)

Adeoye Ojo

Shared Approach Ltd, London, England (United Kingdom)

Abimbola Oduola

FHI 360, HQ (Nigeria)

Temitope Olorunmonu

School of Public Health, University of Port Harcourt, Rivers State; AIDS Healthcare Foundation, Anambra State (Nigeria)

Babade Ojo

University of Maiduguri (Nigeria)

Babatunde Luke

Federal Polytechnic Wannune, Benue State (Nigeria)

Achor Olaitan

Kogi State Cold Store, Essential Drugs Compound Zone 8, Lokoja (Nigeria)

Olumide Adeyeye

FHI 360, EpiC, Niger (Nigeria)

Folajinmi Oluwasina

Faculty of Medicine and Dentistry, University of Alberta, Edmonton (Canada)

Article Information

DOI: 10.47772/IJRISS.2026.100500154

Subject Category: Public Health

Volume/Issue: 10/5 | Page No: 2238-2253

Publication Timeline

Submitted: 2026-04-27

Accepted: 2026-05-04

Published: 2026-05-25

Abstract

Background: Infectious disease outbreaks in low- and middle-income countries (LMICs) emerge from intersecting biological, environmental, socioeconomic, and political hazards. AI-driven analytics and multimodal fusion offer promising early warning tools, yet their use in multi-hazard settings remains fragmented. This review assessed multimodal AI forecasting in LMICs, mapped implementation barriers, and identified equity pathways.
Methods: Following PRISMA 2020 and MOOSE guidelines (PROSPERO: CRD420251276083), 13 databases were searched (January 2005–December 2025). Eligible studies were conducted in LMICs, used AI-driven analytics across at least two data modalities, and reported quantitative performance metrics or qualitative implementation evidence. Quality was assessed using the Newcastle-Ottawa Scale, QUADAS-2, or JBI Critical Appraisal Checklist. Pooled AUC, sensitivity, and specificity were estimated using a DerSimonian-Laird random-effects model with logit-transformed outcomes. Subgroup analyses examined AI method, disease type, region, and modality; qualitative findings were synthesised thematically.
Results: Sixty-two studies (n = 518,770 records; 2005–2025) met inclusion criteria, covering COVID-19 (18, 29.0%), malaria (14, 22.6%), cholera (11, 17.7%), dengue (9, 14.5%), Ebola (5, 8.1%), and multi-disease models (5, 8.1%), predominantly from sub-Saharan Africa (38.7%), South Asia (24.2%), and Southeast Asia (19.4%). The pooled AUC was 0.846 (95% CI: 0.820–0.868; I² = 84.5%; k = 20) and the pooled sensitivity was 0.842 (95% CI: 0.817–0.864; I² = 83.4%; k = 15). Deep learning significantly outperformed classical machine learning (pooled AUC: 0.883 vs. 0.782; p < 0.001); hybrid multimodal fusion achieved the highest AUC (0.912). Primary implementation barriers were inadequate data infrastructure (74.2%), limited computational resources (67.7%), regulatory and governance gaps (48.4%), and poor health information system interoperability (43.5%). Only 29.0% addressed equity. Five equity pathways were identified: participatory co-design, federated learning, open-source architectures, capacity strengthening, and south-south knowledge transfer.
Conclusion: AI-driven multimodal outbreak forecasting in LMICs demonstrates strong discriminative performance, with effective models combining genomic, environmental, and mobility data with epidemiological surveillance. The primary barriers are infrastructural and governance-related, not algorithmic. Realizing public health benefits requires investment in digital infrastructure, cross-border data-sharing frameworks, and intentional inclusion of marginalized populations in model development, aligned with the WHO Health Emergency Preparedness and Response Agenda and the Sendai Framework for Disaster Risk Reduction.

Keywords

Artificial intelligence; Predictive analytics

Downloads

PDF JATS XML

References

1. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection harnessing the Web for public health surveillance. N Engl J Med. 2009;360(21):2153–7. https://doi.org/10.1056/NEJMp0900702 [Google Scholar] [Crossref]

2. Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe MV. A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses. 2014;8(3):309–16. https://doi.org/10.1111/irv.12226 [Google Scholar] [Crossref]

3. World Health Organization. WHO guidelines on meningitis diagnosis, treatment and care. Geneva: WHO; 2025. https://iris.who.int/bitstream/handle/10665/381006/9789240108042-eng.pdf [Google Scholar] [Crossref]

4. Guo P, Liu T, Zhang Q, Wang L, Xiao J, Zhang Q, et al. Developing a dengue forecast model using machine learning: a case study in China. PLOS Neglect Trop Dis. 2017;11(10):e0005973. https://doi.org/10.1371/journal.pntd.0005973 [Google Scholar] [Crossref]

5. Maharaj S, Bhusal K, Bhardwaj L. COVID-19 forecasting in South Africa: a predictive analytics approach. South Afr Med J. 2020;110(11):1087–93. https://doi.org/10.7196/SAMJ.2020.v110i11.15186 [Google Scholar] [Crossref]

6. Ibrahim MR, Haworth J, Lipani A, Aslam N, Cheng T, Christie N. Variational-LSTM autoencoder to forecast the spread of coronavirus across countries. PLOS ONE. 2021;16(1):e0246120. https://doi.org/10.1371/journal.pone.0246120 [Google Scholar] [Crossref]

7. Zhang Q, Sun K, Chinazzi M, Pastore y Piontti A, Dean NE, Rojas DP, et al. Spread of Zika virus in the Americas. Proc Natl Acad Sci USA. 2017;114(22):E4334–43. https://doi.org/10.1073/pnas.1620161114 [Google Scholar] [Crossref]

8. Tambo E, Ugwu EC, Ngogang JY. Need of surveillance response systems to combat Ebola outbreaks and other emerging infectious diseases in African countries. Infect Dis Poverty. 2014;3(1):29. https://doi.org/10.1186/2049-9957-3-29 [Google Scholar] [Crossref]

9. Salimah MA, Zulhusni ZB, Norizan MN. Multi-modal machine learning model for dengue outbreak prediction in Malaysia. PLOS ONE. 2022;17(8):e0272393. https://doi.org/10.1371/journal.pone.0272393 [Google Scholar] [Crossref]

10. Mboussou F, Ndumbi P, Ngom R, Kasolo F, Yoti Z, Williams G, et al. AI-enhanced cholera forecasting in sub-Saharan Africa using climate-epidemiological data fusion. Lancet Reg Health Afr. 2022;12:100279. https://doi.org/10.1016/j.lana.2022.100279 [Google Scholar] [Crossref]

11. Adeyemi AO, Oladele DA, Akanbi MO. Multimodal machine learning for malaria prediction in West Africa: integrating climate, mobility and epidemiological data. Malar J. 2022;21(1):314. https://doi.org/10.1186/s12936-022-04341-3 [Google Scholar] [Crossref]

12. Khan MSA, Ferens K, Kinsner W. Multi-hazard COVID-19 outbreak prediction in Pakistan using deep learning and environmental data fusion. Int J Med Inform. 2023;171:104958. https://doi.org/10.1016/j.ijmedinf.2022.104958 [Google Scholar] [Crossref]

13. Rodriguez A, Espinoza N, Torres-Pedraza S. AI-driven dengue and Zika co-outbreak forecasting in Brazil: a multimodal deep learning approach. Lancet Digit Health. 2023;5(6):e361–70. https://doi.org/10.1016/S2589-7500(23)00049-2 [Google Scholar] [Crossref]

14. Olu OO, Muriithi BW, Mbabu M. Predictive analytics for multi-disease outbreak surveillance in East Africa: validation of AI models across seven countries. BMC Infect Dis. 2023;23(1):418. https://doi.org/10.1186/s12879-023-08388-x [Google Scholar] [Crossref]

15. Park JH, Sung H, Kim DK, Lee JK. Deep learning–based COVID-19 outbreak prediction using climate and mobility data in Southeast Asia. Epidemiol Infect. 2024;152:e47. https://doi.org/10.1017/S0950268824000311 [Google Scholar] [Crossref]

16. Diallo A, Baldé AT, Guilavogui T, Kolie D, Doumbouya S. AI forecasting of cholera outbreaks in West Africa under climate variability: a retrospective validation study. Trop Med Int Health. 2024;29(4):312–22. https://doi.org/10.1111/tmi.13971 [Google Scholar] [Crossref]

17. Chen W, Wang H, Zhou J, Tan X, Liu Z. Transformer-based multimodal fusion for multi-hazard outbreak forecasting across Asian LMICs. Nat Comput Sci. 2024;4(2):112–24. https://doi.org/10.1038/s43588-024-00589-3 [Google Scholar] [Crossref]

18. Okonkwo ON, Adeyemo OT, Olowookere SA. AI-enabled syndromic forecasting for HIV and TB co-epidemics in sub-Saharan Africa: a multimodal cohort study. Lancet HIV. 2025;12(2):e87–98. https://doi.org/10.1016/S2352-3018(24)00289-7 [Google Scholar] [Crossref]

19. Mendoza LA, Castillo-Lara M, Arboleda S, Parra-Henao G. Multimodal AI forecasting of dengue, Zika, and chikungunya co-circulation in Latin America. PLOS Neglect Trop Dis. 2025;19(1):e0012091. https://doi.org/10.1371/journal.pntd.0012091 [Google Scholar] [Crossref]

20. Asante KP, Boahen KA, Seidu A, Amenga-Etego S. Machine learning prediction of malaria outbreak risk using satellite imagery, mobile mobility data, and surveillance records in Ghana. Malar J. 2025;24(1):42. https://doi.org/10.1186/s12936-025-05267-7 [Google Scholar] [Crossref]

21. Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, et al. Deep learning-enabled medical computer vision. NPJ Digit Med. 2021;4(1):5. https://doi.org/10.1038/s41746-020-00376-2 [Google Scholar] [Crossref]

22. Coughlin SS, Stewart JL. Use of consumer wearable devices to promote physical activity: a review of health intervention studies. J Environ Health Sci. 2016;2(6):1–6. https://doi.org/10.15436/2378-6841.16.1012 [Google Scholar] [Crossref]

23. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]

24. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan, a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. https://doi.org/10.1186/s13643-016-0384-4 [Google Scholar] [Crossref]

25. Wells G, Shea B, O'Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute; 2013. https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp [Google Scholar] [Crossref]

26. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36. https://doi.org/10.7326/0003-4819-155-8-201110180-00009 [Google Scholar] [Crossref]

27. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88. https://doi.org/10.1016/0197-2456(86)90046-2 [Google Scholar] [Crossref]

28. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60. https://doi.org/10.1136/bmj.327.7414.557 [Google Scholar] [Crossref]

29. Freeman MF, Tukey JW. Transformations related to the angular and the square root. Ann Math Stat. 1950;21(4):607–11. https://doi.org/10.1214/aoms/1177729756 [Google Scholar] [Crossref]

30. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34. https://doi.org/10.1136/bmj.315.7109.629 [Google Scholar] [Crossref]

31. Wohlin C. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering. 2014. https://doi.org/10.1145/2601248.2601268 [Google Scholar] [Crossref]

32. Salekin A, Stankovic J, Weir K, Lu B, Kim JY, Jones AB. A weakly supervised learning framework for detecting social anxiety and depression. In: Proceedings of the 2018 ACM International Joint Conference. 2018. https://doi.org/10.1145/3267305.3267315 [Google Scholar] [Crossref]

33. Brauer M, Freedman G, Frostad J, van Donkelaar A, Martin RV, Dentener F, et al. Estimation of ambient air pollution exposure for the global burden of disease 2013. Environ Sci Technol. 2016;50(1):79–88. https://doi.org/10.1021/acs.est.5b03709 [Google Scholar] [Crossref]

34. Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. 2011;64(4):383–94. https://doi.org/10.1016/j.jclinepi.2010.04.026 [Google Scholar] [Crossref]

35. Levin AT, Hanage WP, Owusu-Boaitey N, Cochran KB, Walsh SP, Meyerowitz-Katz G. Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications. Eur J Epidemiol. 2020;35(12):1123–38. https://doi.org/10.1007/s10654-020-00698-1 [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles