Rahman, M.S., Shiddik, M.A.B. Unraveling global malaria incidence and mortality using machine learning and artificial intelligence–driven spatial analysis. Sci Rep 15, 28334 (2025). https://doi.org/10.1038/s41598-025-12872-0.

Masuda Begum Sampa, Nor Hidayati Abdul Aziz, Md. Siddikur Rahman, Mehdi Hasan & Nor Azlina Ab. Aziz (2025) Factors influencing the adoption and acceptance of eHealth in Malaysia: a systematic review, Critical Public Health, 35:1, 2519780, DOI:10.1080/09581596.2025.2519780

M.S. Rahman and M.A.B. Shiddik (2025), Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers, Global Epidemiology https://doi.org/10.1016/j.gloepi.2025.100210.

Zafar, S., Rocklöv, J., Paul, R.E. M.S. Rahman, U. Haque et al. (2025), Landscape and climatic drivers of dengue fever in Lao People’s Democratic Republic and Thailand: a retrospective analysis during 2002–2019. Landscape Ecology, 40, 102. https://doi.org/10.1007/s10980-025-02102-3.

Rahman, M.S., Amrin, M. and Bokkor Shiddik, M.A. (2025), Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree-Based Machine Learning Model. Health Science Reports, 8: e70726. https://doi.org/10.1002/hsr2.70726.

Rahman, M.S., Amrin, M. and Bokkor Shiddik, M.A. (2025), Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree-Based Machine Learning Model. Health Science Reports, 8: e70726. https://doi.org/10.1002/hsr2.70726.

Rahman, M.S., Shiddik, A.B. (2025) Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally. Scientific Reports 15, 13619 (2025). https://doi.org/10.1038/s41598-025-96973-w.

Rahman, Md. S., Anika, A. A., Raka, R. A. & Muratovic, A. K. (2025) Impact of Climate Change on Emerging Infectious Diseases and Human Physical and Mental Health in Bangladesh. Health Care Science doi:10.1002/HCS2.129.

Chowdhury AH, Rahman MS (2025) Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh. PLoS Negl Trop Dis 19(1): e0012800. https://doi.org/10.1371/journal.pntd.0012800.

Chowdhury AH, Rahman MS. Spatio‐temporal pattern and associate meteorological factors of airborne diseases in Bangladesh using geospatial mapping and spatial regression model. Health Sci Rep. 2024;7:e2176. doi:10.1002/hsr2.2176.

Chowdhury AH, Rad D, Rahman MS. Predicting anxiety, depression, and insomnia among Bangladeshi university students using tree‐based machine learning models. Health Sci Rep. 2024;7:e2037.doi:10.1002/hsr2.2037.

Uwanbamrung C, Srinam B, Promkool P, Suwannakarn W, Siripanich S, Rahman MS, et al. (2024) Uptake of COVID-19 vaccine among high-risk urban populations in Southern Thailand using the COM-B model. PLoS ONE 19(3): e0300509.https://doi.org/10.1371/journal.pone.0300509.

MF, Chowdhury MH, Rahman MS. A quantile regression approach to identify risk factors for high blood glucose levels among Bangladeshi individuals. Health Science Reports. 2023;6:e1772. doi:10.1002/hsr2.1772.

Sampa MB, Biswas T, Rahman MS, Aziz NHBA, Hossain MN, Aziz NAA. A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study. JMIR Diabetes 2023;8:e49113. doi: 10.2196/49113.PMID: 37999944PMCID: 10709789.

Iñiguez-Gallardo, V., Campbell, D., Castellanos, E.J., Haryanto, B., Tume, S.J.P., Rahman, M.S., Weber, E., Borghi, J., Garcia-Dorado, S.C. and Anton, B., 2023. Place-based capacity building to enhance resilience in tropical countries. One Earth, 6(8),pp.935-938.

Abdulla F, Hossain MM, Rahman MM, Rahman MS and Rahman A. Risk factors of cesarean deliveries in urban–rural areas of Bangladesh. Frontiers in Reproductive Health. 2023;  5:1101400. doi: 10.3389/frph.2023.1101400.

C. Kaewchandee, U. Hnuthong, S. Thinkan, S. Rahman, C. Suwanbamrung, The experiences of district public health officers during the COVID-19 crisis and its management in the upper southern region of Thailand: A mixed-methods approaches, HELIYON, https://doi.org/10.1016/ j.heliyon.2022.e12558. 

Zafar, S., Overgaard, H. J., Pongvongsa, T., Vannavong, N., Phommachanh, S., Shipin, O., Rocklöv, J., Paul, R. E., Rahman, M. S., & Mayxay, M. (2022). Epidemiological profile of dengue in Champasak and Savannakhet provinces, Lao People’s Democratic Republic, 2003–2020: Dengue epidemiological profile in southern Lao PDR. Western Pacific Surveillance and Response, 13(4), 13. https://doi.org/10.5365/wpsar.2022.13.4.932.

Nontapet O, Maneerattanasak S, Jaroenpool J, Phumee A, Kracha W, Napet P, Rahman MS, Suwanbamrung C, et al. Understanding dengue solution and larval indices surveillance system among village health volunteers in high- and low-risk dengue villages in southern Thailand. One Health. 2022:100440. doi: https://doi.org/10.1016/j.onehlt.2022.100440.

Rahman MS, Chowdhury AH (2022) A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers. PLoS ONE 17(9): e0273319. https://doi.org/10.1371/journal.pone.0273319 

Rahman MS, Safa NT, Sultana S, Salam S, Karamehic-Muratovic A, Overgaard HJ. Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh. Parasite Epidemiol Control. 2022;18:e00266; doi: 10.1016/j.parepi.2022.e00266 

Rahman, M. S., A. H. Chowdhury and M. Amrin (2022). "Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh." PLOS Global Public Health 2(5): e0000495.https://doi.org/10.1371/journal.pgph.0000495

Rahman MS, Pientong C, Zafar S, Ekalaksananan T, Paul RE, Haque U, et al. Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach. One health (Amsterdam, Netherlands). 2021;13:100358; doi: 10.1016/j.onehlt.2021.100358. https://www.sciencedirect.com/science/article/pii/S2352771421001488

Zafar, S.; Shipin, O.; Paul, R.E.; Rocklöv, J.; Haque, U.; Rahman, M.; Mayxay, M.; Pientong, C.; Aromseree, S.; Poolphol, P. Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand. International journal of environmental research and public health 2021, 18, 9421. https://doi.org/10.3390/ijerph18179421 .https://www.mdpi.com/1660-4601/18/17/9421

Jony, S.S.R.; Haque, U.; Webb, N.J.; Spence, E.; Rahman, M.; Aghamohammadi, N.; Lie, Y.; Angulo-Molina, A.; Ananth, S.; Ren, X. Analyzing Predictors of Control Measures and Psychosocial Problems Associated with COVID-19 Pandemic: Evidence from Eight Countries. Behavioral Sciences 2021, 11, 106. https://pubmed.ncbi.nlm.nih.gov/34436096/

Rahman, M.; Ekalaksananan, T.; Zafar, S.; Poolphol, P.; Shipin, O.; Haque, U.; Paul, R.; Rocklöv, J.; Pientong, C.; Overgaard, H.J. Ecological, Social, and Other Environmental Determinants of Dengue Vector Abundance in Urban and Rural Areas of Northeastern Thailand. International Journal of Environmental Research and Public Health 2021, 18, 5971. https://www.mdpi.com/1660-4601/18/11/5971

Rahman MS, Karamehic-Muratovic A, Amrin M, Chowdhury AH, Mondol MS, Haque U, et al. COVID-19 Epidemic in Bangladesh among Rural and Urban Residents: An Online Cross-Sectional Survey of Knowledge, Attitudes, and Practices. Epidemiologia. 2021;2(1):1-13. PubMed PMID: doi:10.3390/epidemiologia2010001. https://www.mdpi.com/2673-3986/2/1/1

Doum, D.; Overgaard, H.J.; Mayxay, M.; Suttiprapa, S.; Saichua, P.; Ekalaksananan, T.; Tongchai, P.; Rahman, M.S.; Haque, U.; Phommachanh, S.; Pongvongsa, T.; Rocklöv, J.; Paul, R.; Pientong, C. Dengue Seroprevalence and Seroconversion in Urban and Rural Populations in Northeastern Thailand and Southern Laos. Int. J. Environ. Res. Public Health 2020, 17, 9134. doi: 10.3390/ijerph17239134

Md. Siddikur Rahman, Hans J. Overgaard, Chamsai Pientong, Mayfong Mayxay, Tipaya Ekalaksananan, Sirinart Aromseree, Supranee Phanthanawiboon, Sumaira Zafar, Oleg Shipin, Richard E. Paul, Sysavanh Phommachanh, Tiengkham Pongvongsa, Nanthasane Vannavong, Ubydul Haque, Knowledge, Attitudes, and Practices on Climate Change and Dengue in Lao People’s Democratic Republic and Thailand, Environmental Research, 2020, 110509, ISSN 0013-9351, https://doi.org/10.1016/j.envres.2020.110509. https://pubmed.ncbi.nlm.nih.gov/33297445/

Md Siddikur Rahman, Ajlina Karamehic-Muratovic, Mahdi Baghbanzadeh, Miftahuzzannat Amrin, Sumaira Zafar, Nadia Nahrin Rahman, Sharifa Umma Shirina, Ubydul Haque, Climate change and dengue fever knowledge, attitudes and practices in Bangladesh: a social media–based cross-sectional survey, Transactions of The Royal Society of Tropical Medicine and Hygiene, traa093, https://doi.org/10.1093/trstmh/traa093 

Noah C Peeri, Nistha Shrestha, Md Siddikur Rahman, Rafdzah Zaki, Zhengqi Tan, Saana Bibi, Mahdi Baghbanzadeh, Nasrin Aghamohammadi, Wenyi Zhang, Ubydul Haque, The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? International Journal of Epidemiology, Volume 49, Issue 3, June 2020, Pages 717–726, https://doi.org/10.1093/ije/dyaa033

Rahman MS, Peeri NC, Shrestha N, Zaki R, Haque U, Hamid SHA. Defending against the Novel Coronavirus (COVID-19) outbreak: How can the Internet of Things (IoT) help to save the world? Health Policy Technol. 2020 Jun;9(2):136-138. doi: 10.1016/j.hlpt.2020.04.005. Epub 2020 Apr 22. PMID: 32322475; PMCID: PMC7175864.https://pubmed.ncbi.nlm.nih.gov/32322475/