{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:49:26Z","timestamp":1771026566019,"version":"3.50.1"},"reference-count":38,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Clinical Practice"],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Backgrounds<\/jats:title><jats:p>SARS\u2010CoV\u20102 is affecting different countries all over the world, with significant variation in infection\u2010rate and death\u2010ratio. We have previously shown a presence of a possible relationship between different variables including the Bacillus Calmette\u2013Gu\u00e9rin (BCG) vaccine, average age, gender, and malaria treatment, and the rate of spread, severity and mortality of COVID\u201019 disease. This paper focuses on developing machine learning models for this relationship.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We have used real\u2010datasets collected from the Johns Hopkins University Center for Systems Science and Engineering and the European Centre for Disease Prevention and Control to develop a model from China data as the baseline country. From this model, we predicted and forecasted different countries' daily confirmed\u2010cases and daily death\u2010cases and examined if there was any possible effect of the variables mentioned above.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The model was trained based on China data as a baseline model for daily confirmed\u2010cases and daily death\u2010cases. This machine learning application succeeded in modelling and forecasting daily confirmed\u2010cases and daily death\u2010cases. The modelling and forecasting of viral spread resulted in four different regions; these regions were dependent on the malarial treatments, BCG vaccination, weather conditions, and average age. However, the lack of social distancing resulted in variation in the effect of these factors, for example, double\u2010humped spread and mortality cases curves and sudden increases in the spread and mortality cases in different countries.<\/jats:p><jats:p>The process of machine learning for time\u2010series prediction and forecasting, especially in the pandemic COVID\u201019 domain, proved usefulness in modelling and forecasting the end status of the virus spreading based on specific regional and health support variables.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>From the experimental results, we confirm that COVID\u201019 has a very low spread in the African countries with all the four variables (average young age, hot weather, BCG vaccine and malaria treatment); a very high spread in European countries and the USA with no variable (old people, cold weather, no BCG vaccine and no malaria). The effect of the variables could be on the spread or the severity to the extent that the infected subject might not have symptoms or the case is mild and can be missed as a confirmed\u2010case. Social distancing decreases the effect of these factors.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ijcp.14116","type":"journal-article","created":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T18:56:03Z","timestamp":1614452163000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A study of the possible factors affecting COVID\u201019 spread, severity and mortality and the effect of social distancing on these factors: Machine learning forecasting model"],"prefix":"10.1155","volume":"75","author":[{"given":"Hossam M.","family":"Zawbaa","sequence":"first","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence Beni\u2010Suef University Beni\u2010Suef Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"El\u2010Gendy","sequence":"additional","affiliation":[{"name":"Microbiology and Immunology Department Faculty of Pharmacy Beni\u2010Suef University Beni\u2010Suef Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2040-9466","authenticated-orcid":false,"given":"Haitham","family":"Saeed","sequence":"additional","affiliation":[{"name":"Clinical Pharmacy Department Faculty of Pharmacy Beni\u2010Suef University Beni\u2010Suef Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hasnaa","family":"Osama","sequence":"additional","affiliation":[{"name":"Clinical Pharmacy Department Faculty of Pharmacy Beni\u2010Suef University Beni\u2010Suef Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed M. 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