{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:12:39Z","timestamp":1789737159430,"version":"4.0.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003789","name":"Helwan University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003789","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Infectious diseases significantly impact both public health and economic stability, underscoring the critical need for precise outbreak predictions to effictively mitigate their impact. This study applies advanced machine learning techniques to forecast outbreaks of Dengue, Chikungunya, and Zika, utilizing a comprehensive dataset comprising climate and socioeconomic data. Spanning the years 2007 to 2017, the dataset includes 1716 instances characterized by 27 distinct features. The researchers adopt the Analytic Hierarchy Process (AHP) for feature selection and integrated transfer learning to boost the accuracy of the study\u2019s predictions. The researchers\u2019 approach involves the deployment of several machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, and an ensemble of these methods. The result reveals that the ensemble model is particularly effective, achieving the highest accuracy rate of 96.80% and an AUC of 0.9197 for predicting Zika outbreaks. Furthermore, it exhibts consistent performance across various metrics. Notably, in the context of Chikungunya, this model achieves an optimal balance between precision and recall, with an accuracy of 93.31%, a precision of 57%, and a recall of 63%, highlighting its reliability for effective outbreak prediction.<\/jats:p>","DOI":"10.1038\/s41598-024-81367-1","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T13:43:30Z","timestamp":1735652610000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Leveraging AHP and transfer learning in machine learning for improved prediction of infectious disease outbreaks"],"prefix":"10.1038","volume":"14","author":[{"given":"Reham","family":"Abdallah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sayed","family":"Abdelgaber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanan Ali","family":"Sayed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"81367_CR1","unstructured":"World Health Organization. Global Report on Infection Prevention and Control. World Health Organization: Geneva, Switzerland. Licence: CC BY-NC-SA 3.0 IGO. (2022)."},{"key":"81367_CR2","doi-asserted-by":"crossref","unstructured":"R Abdallah, SA Abdel Gaber, HA Sayed. Disease Outbreak \/Epidemic in Public Health Sector. In 2024 6th International Conference on Computing and Informatics (ICCI), New Cairo\u2013Cairo, Egypt, 203\u2013216. https:\/\/ieeexplore.ieee.org\/document\/10485007 (2024).","DOI":"10.1109\/ICCI61671.2024.10485007"},{"key":"81367_CR3","doi-asserted-by":"publisher","first-page":"175","DOI":"10.3390\/make5010013","volume":"5","author":"OE Santangelo","year":"2023","unstructured":"Santangelo, O. E. et al. Machine learning and prediction of infectious diseases: a systematic review. Mach. Learn. Knowl. Extr. 5, 175\u2013198 (2023).","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"81367_CR4","first-page":"49","volume":"10","author":"NCD Adhikari","year":"2018","unstructured":"Adhikari, N. C. D. et al. Epidemic outbreak prediction using artificial intelligence. AIRCC Int. J. Comput. Sci. Inf. Technol. 10, 49\u201364 (2018).","journal-title":"AIRCC Int. J. Comput. Sci. Inf. Technol."},{"key":"81367_CR5","unstructured":"Pramod A, Abhishek JS. Epidemic outbreak prediction using machine learning models. arXiv preprint arXiv:2310.19760. (2023)."},{"issue":"1","key":"81367_CR6","doi-asserted-by":"publisher","first-page":"012009","DOI":"10.1088\/1742-6596\/1803\/1\/012009","volume":"1803","author":"A Fariza","year":"2021","unstructured":"Fariza, A., Basofi, A. & Aryani, M. D. Spatial mapping of diphtheria vulnerability level in East Java, Indonesia, using analytical hierarchy process\u2013natural break classification. J. Phys. Conf. Ser. 1803(1), 012009 (2021).","journal-title":"J. Phys. Conf. Ser."},{"issue":"3","key":"81367_CR7","first-page":"4223","volume":"48","author":"R Mahato","year":"2020","unstructured":"Mahato, R., Nimasow, G., Nimasow, O. D. & Bushi, D. Analytic hierarchy process based potential risk zonation of COVID-19 in India. J. Soc. Sci. 48(3), 4223\u20134238 (2020).","journal-title":"J. Soc. Sci."},{"issue":"3","key":"81367_CR8","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s41324-019-00242-8","volume":"27","author":"SA Ali","year":"2019","unstructured":"Ali, S. A. & Ahmad, A. Mapping of mosquito-borne diseases in Kolkata municipal corporation using GIS and AHP based decision making approach. Spat. Inf. Res. 27(3), 351\u2013372 (2019).","journal-title":"Spat. Inf. Res."},{"key":"81367_CR9","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1007\/s40808-021-01244-y","volume":"8","author":"Z Gao","year":"2022","unstructured":"Gao, Z. et al. An AHP-based regional COVID-19 vulnerability model and its application in China. Model. Earth Syst. Environ. 8, 2525\u20132538 (2022).","journal-title":"Model. Earth Syst. Environ."},{"issue":"10","key":"81367_CR10","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S. J. & Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009).","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"81367_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T. M. & Wang, D. A survey of transfer learning. J. Big Data 3, 1\u201340 (2016).","journal-title":"J. Big Data"},{"issue":"3","key":"81367_CR12","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1007\/s10708-022-10757-7","volume":"88","author":"G Harsha","year":"2023","unstructured":"Harsha, G. et al. Dengue risk zone mapping of Thiruvananthapuram district, India: A comparison of the AHP and F-AHP methods. GeoJournal 88(3), 2449\u20132470 (2023).","journal-title":"GeoJournal"},{"key":"81367_CR13","doi-asserted-by":"crossref","unstructured":"Badillo-Rivera E, Fow-Esteves A, Alata-L\u00f3pez F, Vir\u00fa-V\u00e1squez P, ,Medina-Acu\u00f1a M. Environmental and social analysis as risk factors for the spread of the novel coronavirus (SARS-CoV-2) using remote sensing, GIS and analytical hierarchy process (AHP): Case of Peru. MedRxiv, 2020\u201305. (2020).","DOI":"10.1101\/2020.05.31.20118653"},{"issue":"4","key":"81367_CR14","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s41324-018-0187-x","volume":"26","author":"SA Ali","year":"2018","unstructured":"Ali, S. A. & Ahmad, A. Using analytic hierarchy process with GIS for dengue risk mapping in Kolkata municipal corporation, West Bengal, India. Spat. Inf. Res. 26(4), 449\u2013469 (2018).","journal-title":"Spat. Inf. Res."},{"issue":"9","key":"81367_CR15","doi-asserted-by":"publisher","first-page":"e074385","DOI":"10.1136\/bmjopen-2023-074385","volume":"13","author":"JP Romero-Leiton","year":"2023","unstructured":"Romero-Leiton, J. P., Acharya, K. R., Parmley, J. E., Arino, J. & Nasri, B. Modelling the transmission of dengue, zika and chikungunya: a scoping review protocol. BMJ Open 13(9), e074385 (2023).","journal-title":"BMJ Open"},{"key":"81367_CR16","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/978-981-97-4982-9_21","volume-title":"Asian Conference on Intelligent Information and Database Systems","author":"D Sharma","year":"2024","unstructured":"Sharma, D., Khetarpaul, S., Tiwari, S., Aakash, L. & Gupta, A. Predicting epidemic outbreak using climatic factors. In Asian Conference on Intelligent Information and Database Systems 264\u2013275 (Springer, 2024)."},{"key":"81367_CR17","first-page":"15","volume":"78","author":"Z Hussain","year":"2023","unstructured":"Hussain, Z., Khan, I. A. & Hassan, M. Machine learning approaches for dengue prediction: A review of algorithms and applications. Pak. Geogr. Rev. 78, 15\u201336 (2023).","journal-title":"Pak. Geogr. Rev."},{"issue":"6","key":"81367_CR18","doi-asserted-by":"publisher","first-page":"e16727","DOI":"10.1016\/j.heliyon.2023.e16727","volume":"9","author":"MA Esmaeili","year":"2023","unstructured":"Esmaeili, M. A., Ravandi, M. R. G. & Zare, S. Assessing the impact of COVID-19 pandemic on the performance indicators of safety management using the analytic hierarchy process (AHP) in an electricity industry. Heliyon 9(6), e16727 (2023).","journal-title":"Heliyon"},{"issue":"Suppl 9","key":"81367_CR19","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1186\/s12911-022-01749-1","volume":"21","author":"W Tan","year":"2021","unstructured":"Tan, W. et al. Disease risk analysis for schizophrenia patients by an automatic AHP framework. BMC Med. Inform. Decis. Mak. 21(Suppl 9), 375 (2021).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"81367_CR20","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman, M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Am. Stat. Assoc. 32, 675\u2013701 (1937).","journal-title":"Am. Stat. Assoc."},{"issue":"3","key":"81367_CR21","doi-asserted-by":"publisher","first-page":"e0009259","DOI":"10.1371\/journal.pntd.0009259","volume":"15","author":"J Morgan","year":"2021","unstructured":"Morgan, J., Strode, C. & Salcedo-Sora, J. E. Climatic and socio-economic factors supporting the co-circulation of dengue, Zika and chikungunya in three different ecosystems in Colombia. PLoS Negl. Trop. Dis. 15(3), e0009259 (2021).","journal-title":"PLoS Negl. Trop. Dis."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-81367-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-81367-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-81367-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T14:13:53Z","timestamp":1735654433000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-81367-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,31]]},"references-count":21,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["81367"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-81367-1","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,31]]},"assertion":[{"value":"10 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"32163"}}