Prediction of Coronavirus Outbreak Based on Cuisines and Temperature Using Machine Learning Algorithms

Aman Dubey, R. Krishna, S. Aravind, Sanika Mohagaonkar, Ankur Saxena
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引用次数: 5

Abstract

In the current pandemic scenario, every possibility to find the cure is a contribution towards welfare of mankind. As of 15th May 2020, there are 4,543,060 confirmed cases and 1,712,895 patients have been recovered. The first COVID-19 case was reported in Wuhan, China. Till March the highest number of cases as well as deaths were reported by Italy. The type of food people eats, and its nutritional value decides the health or immunity of people whereas temperature plays an important role for the activity of corona. With the help of machine learning tools such as decision tree and heatmap we have shown the correlation of temperature, number of cases and Kcal per capita per day consumption in India and Italy. This correlation forms the basis of our research and it was found to be one of the important factors for the spread of this disease. Through our study we have found that temperature and calory intake of people play an important role in deciding the rise of number of cases in any country.
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基于菜肴和温度的机器学习算法预测冠状病毒爆发
在目前大流行的情况下,找到治疗方法的每一种可能性都是对人类福祉的贡献。截至2020年5月15日,确诊病例4543060例,治愈病例1712895例。中国武汉报告了首例新冠肺炎病例。截至3月,意大利报告的病例和死亡人数最多。人们所吃的食物种类及其营养价值决定了人们的健康或免疫力,而温度对冠状病毒的活性起着重要作用。在决策树和热图等机器学习工具的帮助下,我们展示了印度和意大利的温度、病例数和人均每天消耗的千卡之间的相关性。这种相关性构成了我们研究的基础,并被发现是这种疾病传播的重要因素之一。通过我们的研究,我们发现人们的体温和卡路里摄入量在决定任何国家病例数量的上升方面都起着重要作用。
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