Nowadays, a significant number of infectious diseases such as human coronavirus disease (COVID-19) are threatening the world by spreading at an alarming rate. Some of the literatures pointed out that the pandemic is exhibiting seasonal patterns in its spread, incidence and nature of the distribution. In connection to the spread and distribution of the infection, scientific analysis that answers the questions whether the next summer can save people from COVID-19 is required. Many researchers have been exclusively asked whether high temperature during summer can slow down the spread of the COVID-19 as it has with other seasonal flues. Since there are a lot of questions that are unanswered right now, and many mysteries aspects about the COVID-19 that is still unknown to us, in-depth study and analysis of associated weather features are required. Moreover, understanding the nature of COVID-19 and forecasting the spread of COVID-19 request more investigation of the real effect of weather variables on the transmission of the COVID-19 among people. In this work, various regressor machine learning models are proposed to extract the relationship between different factors and the spreading rate of COVID-19. The machine learning algorithms employed in this work estimate the impact of weather variables such as temperature and humidity on the transmission of COVID-19 by extracting the relationship between the number of confirmed cases and the weather variables on certain regions. To validate the proposed method, we have collected the required datasets related to weather and census features and necessary prepossessing is carried out. From the experimental results, it is shown that the weather variables are more relevant in predicting the mortality rate when compared to the other census variables such as population, age, and urbanization. Thus, from this result, we can conclude that temperature and humidity are important features for predicting COVID-19 mortality rate. Moreover, it is indicated that the higher the value of temperature the lower number of infection cases.

如今，诸如人类冠状病毒病（COVID-19）等大量传染病正以惊人的速度传播，威胁着世界。一些文献指出，大流行在其传播，发生和分布的性质上表现出季节性模式。关于感染的传播和分布，需要科学的分析来回答第二年夏天是否可以拯救人们脱离COVID-19的问题。许多研究人员被专门问到，夏季高温是否会像其他季节性烟道一样延缓COVID-19的传播。由于目前尚有许多问题尚待解决，而且关于COVID-19的许多谜团我们仍然不知道，因此需要对相关天气特征进行深入研究和分析。此外，了解COVID-19的性质并预测COVID-19的传播要求对气候变量对人间COVID-19传播的实际影响进行更多调查。在这项工作中，提出了各种回归机器学习模型，以提取不同因素与COVID-19传播率之间的关系。通过提取确诊病例数与某些地区的天气变量之间的关系，这项工作中采用的机器学习算法估算了温度和湿度等天气变量对COVID-19传播的影响。为了验证所提出的方法，我们收集了与天气和人口普查特征相关的所需数据集，并进行了必要的假设。从实验结果来看 结果表明，与其他人口普查变量（例如人口，年龄和城市化程度）相比，天气变量在预测死亡率方面更为相关。因此，从该结果可以得出结论，温度和湿度是预测COVID-19死亡率的重要特征。而且，表明温度值越高，感染病例的数量越少。