Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

中文翻译：

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基于人工智能应用优先分析气候和城市参数在 COVID-19 确诊病例中的作用。

如今，传染病的爆发被认为是可持续发展进程中最具破坏性的影响之一。新型冠状病毒（COVID-19）作为一种传染病的爆发表明，它具有不良的社会、环境和经济影响，并带来严重的挑战和威胁。此外，研究优先参数对于减少这场全球危机的负面影响至关重要。因此，本研究的主要目的是优先考虑并分析某些环境参数的作用。为此，选择了意大利的四个城市作为案例研究，并将一些值得注意的气候参数（例如日平均温度、相对湿度、风速）和城市参数（人口密度）作为输入数据集，并有确诊病例COVID-19 是输出数据集。本文采用两种人工智能技术，包括基于粒子群优化（PSO）算法的人工神经网络（ANN）和差分进化（DE）算法，对气候和城市参数进行优先级排序。该分析基于特征选择过程，然后与所提出的模型获得的结果进行比较，以选择最佳模型。最后，两个模型的性能之间的成本函数差异约为 0.0001，因此，两种方法在成本函数上没有差异，但是发现 ANN-PSO 更好，因为它达到了所需的精度水平迭代次数比 ANN-DE 少。此外，城市参数和相对湿度这两个变量在预测 COVID-19 确诊病例方面的优先级最高。