Rath Smita, Tripathy Alakananda, Tripathy Alok Ranjan
Department of Computer Science and Engineering, Siksha 'O' Anusandhan Deemed to be University, Odisha, India.
Department of Computer Science,Ravenshaw University, Cuttack, India.
Diabetes Metab Syndr. 2020 Sep-Oct;14(5):1467-1474. doi: 10.1016/j.dsx.2020.07.045. Epub 2020 Aug 1.
The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world. India is one of the countries which is affected by the disease and thousands of people on daily basis are getting infected. In this paper, an analysis of daily statistics of people affected by the disease are taken into account to predict the next days trend in the active cases in Odisha as well as India.
A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases.
Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model R CONCLUSION: These models acquired remarkable accuracy in COVID-19 recognition. A strong correlation factor determines the relationship among the dependent (active) with the independent variables (positive, deceased, recovered).
源自中国武汉市的新冠疫情对世界各国的健康、社会经济和金融事务都产生了重大影响。印度是受该疾病影响的国家之一，每天都有成千上万人感染。本文通过对受该疾病影响人群的每日统计数据进行分析，以预测印度奥里萨邦以及印度全国未来几日的活跃病例趋势。
从世界卫生组织的每日统计数据中收集了有效的全球数据集，并阐述了确诊总数、活跃病例、死亡病例、阳性病例之间的相关性。将线性回归和多元线性回归等回归模型应用于该数据集，以直观呈现受影响病例的趋势。
本文对线性回归模型和多元线性回归模型进行了比较，给出了模型R的得分 结论：这些模型在新冠疫情识别方面具有显著的准确性。一个强相关因子决定了因变量（活跃病例）与自变量（阳性病例、死亡病例、康复病例）之间的关系。
