Department of Statistics, Government Graduate College for Women, Sargodha, Punjab, Pakistan.
Department of Economics, School of Social Sciences and Humanities, National University of Sciences and Technology, Islamabad, H-12, Pakistan.
Environ Sci Pollut Res Int. 2022 Mar;29(15):21811-21825. doi: 10.1007/s11356-021-17268-x. Epub 2021 Nov 12.
The COVID-19 pandemic affected the world through its ability to cause widespread infection. The Middle East including the Kingdom of Saudi Arabia (KSA) has also been hit by the COVID-19 pandemic like the rest of the world. This study aims to examine the relationships between meteorological factors and COVID-19 case counts in three cities of the KSA. The distribution of the COVID-19 case counts was observed for all three cities followed by cross-correlation analysis which was carried out to estimate the lag effects of meteorological factors on COVID-19 case counts. Moreover, the Poisson model and negative binomial (NB) model with their zero-inflated versions (i.e., ZIP and ZINB) were fitted to estimate city-specific impacts of weather variables on confirmed case counts, and the best model is evaluated by comparative analysis for each city. We found significant associations between meteorological factors and COVID-19 case counts in three cities of KSA. We also perceived that the ZINB model was the best fitted for COVID-19 case counts. In this case study, temperature, humidity, and wind speed were the factors that affected COVID-19 case counts. The results can be used to make policies to overcome this pandemic situation in the future such as deploying more resources through testing and tracking in such areas where we observe significantly higher wind speed or higher humidity. Moreover, the selected models can be used for predicting the probability of COVID-19 incidence across various regions.
COVID-19 大流行通过其造成广泛感染的能力影响了世界。包括沙特阿拉伯王国（KSA）在内的中东地区也像世界其他地区一样受到了 COVID-19 大流行的打击。本研究旨在检验气象因素与 KSA 三个城市 COVID-19 病例数之间的关系。观察了所有三个城市的 COVID-19 病例数分布，然后进行了互相关分析，以估计气象因素对 COVID-19 病例数的滞后影响。此外，还拟合了泊松模型和负二项式（NB）模型及其零膨胀版本（即 ZIP 和 ZINB），以估计天气变量对确诊病例数的特定城市影响，并通过比较分析评估每个城市的最佳模型。我们发现 KSA 三个城市的气象因素与 COVID-19 病例数之间存在显著关联。我们还发现，ZINB 模型最适合 COVID-19 病例数。在本案例研究中，温度、湿度和风速是影响 COVID-19 病例数的因素。研究结果可用于制定未来克服这种大流行情况的政策，例如在风速较高或湿度较高的地区部署更多资源进行测试和跟踪。此外，所选模型可用于预测各个地区 COVID-19 发病率的概率。
