Exploring Co-occurrence Patterns in Infectious Diseases using Association Rule Mining: A Hospital-based Study from the Cities of Nepal
DOI:
https://doi.org/10.63682/jns.v14i30S.9414Keywords:
Apriori Algorithm, Frequent mining, Dengue, Scrub typhusAbstract
The co-occurrence of patient characteristics with infectious diseases provides essential insights into disease distribution, supporting improved preparedness and response strategies in both endemic and epidemic contexts. While Association Rule Mining (ARM) is widely applied in computer science for pattern discovery, its use in healthcare, particularly for understanding demographic and geographic clustering of infectious diseases, remains limited. This study applied the Apriori algorithm, a commonly used ARM technique, to retrospective inpatient data from three tertiary hospitals in Nepal—Kathmandu, Bharatpur, and Pokhara—totaling 1,019 records. Rules were evaluated based on support, confidence, and lift to identify strong, non-redundant associations. The findings showed that Dengue consistently clustered among younger urban populations, with gender and caste (particularly males and Brahmins) emerging as key relationships, and showed distinct geographical concentrations in Kathmandu and Pokhara. In contrast, Scrub typhus was strongly associated with older adults, females, and rural residents, with exceptionally high lift values observed in Chitwan, indicating a substantially elevated risk compared to the general inpatient population. No significant rules emerged for Kala-azar due to its low case frequency. These findings underscore the heterogeneity of vector-borne disease distribution across ecological and demographic contexts. The study concludes that ARM is a valuable analytic tool for uncovering non-random, epidemiologically meaningful patterns, offering practical guidance for targeted interventions, resource allocation, and the development of context-specific public health strategies in Nepal and similar regions
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References
Agrawal, R., Imieliński, T., Swami, A., 1993. Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22, 207–216. https://doi.org/10.1145/170036.170072
Baker, R.E., Mahmud, A.S., Miller, I.F., Rajeev, M., Rasambainarivo, F., Rice, B.L., Takahashi, S., Tatem, A.J., Wagner, C.E., Wang, L.-F., Wesolowski, A., Metcalf, C.J.E., 2022. Infectious disease in an era of global change. Nat. Rev. Microbiol. 20, 193–205. https://doi.org/10.1038/s41579-021-00639-z
Blane, D.B., 1996. Collecting retrospective data: Development of a reliable method and a pilot study of its use. Soc. Sci. Med. 42, 751–757. https://doi.org/10.1016/0277-9536(95)00340-1
Chinedozie, G.C., 2023. Emerging patterns of tuberculosis drug resistance by data mining with association rules (Apriori): using the R-library tbdr19prediction. Federal University of Rio de Janeiro.
EDCD, 2023. Early Warning and Reporting System (EWARS) annual report 2022 & 2023. Epidemiology and Disease Control Division.
Gautam, A., Upadhayay, P., Ghimre, D., Khanal, A., Gaire, A., Kaphle, K., 2021. Prioritised Zoonotic Diseases of Nepal: A Review. Int. J. Appl. Sci. Biotechnol. 9, 1–15. https://doi.org/10.3126/ijasbt.v9i1.34967
Gautam, R., Parajuli, K., Sherchand, J.B., 2019. Epidemiology, Risk Factors and Seasonal Variation of Scrub Typhus Fever in Central Nepal. Trop. Med. Infect. Dis. 4, 27. https://doi.org/10.3390/tropicalmed4010027
Gómez-Pulido, J.A., Romero-Muelas, J.M., Gómez-Pulido, J.M., Castillo Sequera, J.L., Sanz Moreno, J., Polo-Luque, M.-L., Garcés-Jiménez, A., 2020. Predicting Infectious Diseases by Using Machine Learning Classifiers, in: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (Eds.), Bioinformatics and Biomedical Engineering, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 590–599. https://doi.org/10.1007/978-3-030-45385-5_53
Han, J.J., Song, H.A., Pierson, S.L., Shen-Gunther, J., Xia, Q., 2023. Emerging Infectious Diseases Are Virulent Viruses—Are We Prepared? An Overview. Microorganisms 11, 2618. https://doi.org/10.3390/microorganisms11112618
Jahangir, I., Hannan, A., Javed, S., 2018. Prediction of dengue disease through data mining by using modified apriori algorithm., in: Proceedings of the 4th ACM International Conference of Computing for Engineering and Science. pp. 1–4.
Jose, P., Rajan, N., Kommu, P.P.K., Krishnan, L., 2022. Dengue and scrub typhus co-infection in children: Experience of a teaching hospital in an endemic area. Indian J. Public Health 66, 292–294. https://doi.org/10.4103/ijph.ijph_2052_21
Linsuwanon, P., Auysawasdi, N., Chao, C.-C., Rodkvamtook, W., Shrestha, B., Bajracharya, S., Shrestha, J., Wongwairot, S., Limsuwan, C., Lindroth, E., Mann, A., Davidson, S., Wanja, E., Shrestha, S.K., 2024. Estimating the Seroprevalence of Scrub Typhus in Nepal. Pathogens 13, 736. https://doi.org/10.3390/pathogens13090736
Mishra, S.R., Ghimire, K., Khanal, V., Aryal, D., Shrestha, B., Khanal, P., Yadav, S., Sharma, V., Khatri, R., Schwarz, D., Adhikari, B., 2025. Transforming health in Nepal: a historical and contemporary review on disease burden, health system challenges, and innovations. Health Res. Policy Syst. 23, 61. https://doi.org/10.1186/s12961-025-01321-z
Rai, V.K., Chakraborty, Santonab, Chakraborty, Shankar, 2023. Association rule mining for prediction of COVID-19. Decis. Mak. Appl. Manag. Eng. 6, 365–378. https://doi.org/10.31181/dmame0317102022r
Shakil, K.A., Anis, S., Alam, M., 2015. Dengue disease prediction using weka data mining tool. https://doi.org/10.48550/ARXIV.1502.05167
Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R., Jr, K.C.L., 2018. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.
Singh, A., Singh, D., Upreti, K., Sharma, V., Rathore, B.S., Raikwal, J., 2022. Investigating New Patterns in Symptoms of COVID-19 Patients by Association Rule Mining (ARM). J. Mob. Multimed. 19, 1–28. https://doi.org/10.13052/jmm1550-4646.1911
WHO, 2024. Monitoring health for the SDGs. World Health Organization..
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