{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T19:28:00Z","timestamp":1769974080918,"version":"3.49.0"},"reference-count":105,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The outbreak of epidemiological diseases creates a major impact on humanity as well as on the world\u2019s economy. The consequence of such infectious diseases affects the survival of mankind. The government has to stand up to the negative influence of these epidemiological diseases and facilitate society with medical resources and economical support. In recent times, COVID-19 has been one of the epidemiological diseases that created lethal effects and a greater slump in the economy. Therefore, the prediction of outbreaks is essential for epidemiological diseases. It may be either frequent or sudden infections in society. The unexpected raise in the application of prediction models in recent years is outstanding. A study on these epidemiological prediction models and their usage from the year 2018 onwards is highlighted in this article. The popularity of various prediction approaches is emphasized and summarized in this article.<\/jats:p>","DOI":"10.3390\/info15110719","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T03:53:14Z","timestamp":1731383594000},"page":"719","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["From Data to Diagnosis: Machine Learning Revolutionizes Epidemiological Predictions"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4967-6223","authenticated-orcid":false,"given":"Abdul Aziz","family":"Abdul Rahman","sequence":"first","affiliation":[{"name":"Finance and Accounting Department, College of Business Administration, Kingdom University, Riffa P.O. Box 40434, Bahrain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8131-2844","authenticated-orcid":false,"given":"Gowri","family":"Rajasekaran","sequence":"additional","affiliation":[{"name":"Department of Computer Science, AVS College of Arts and Science (Autonomous), Salem 636106, Tamil Nadu, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3970-262X","authenticated-orcid":false,"given":"Rathipriya","family":"Ramalingam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Periyar University, Salem 636011, Tamil Nadu, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2697-1935","authenticated-orcid":false,"given":"Abdelrhman","family":"Meero","sequence":"additional","affiliation":[{"name":"Finance and Accounting Department, College of Business Administration, Kingdom University, Riffa P.O. Box 40434, Bahrain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8239-1276","authenticated-orcid":false,"given":"Dhamodharavadhani","family":"Seetharaman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Periyar University, Salem 636011, Tamil Nadu, India"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16732","DOI":"10.1073\/pnas.2006520117","article-title":"The challenges of modeling and forecasting the spread of COVID-19","volume":"117","author":"Bertozzi","year":"2020","journal-title":"Proc. Natl. Acad. Sci. 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