COVID-19 pandemic has become a concern of every nation, and it is crucial to apply an estimation model with a favorably-high accuracy to provide an accurate perspective of the situation. In this study, three explicit mathematical prediction models were applied to forecast the COVID-19 outbreak in Iran and Turkey. These models include a recursive-based method, Boltzmann Function-based model and Beesham’s prediction model. These models were exploited to analyze the confirmed and death cases of the first 106 and 87 days of the COVID-19 outbreak in Iran and Turkey, respectively. This application indicates that the three models fail to predict the first 10 to 20 days of data, depending on the prediction model. On the other hand, the results obtained for the rest of the data demonstrate that the three prediction models achieve high values for the determination coefficient, whereas they yielded to different average absolute relative errors. Based on the comparison, the recursive-based model performs the best, while it estimated the COVID-19 outbreak in Iran better than that of in Turkey. Impacts of applying or relaxing control measurements like curfew in Turkey and reopening the low-risk businesses in Iran were investigated through the recursive-based model. Finally, the results demonstrate the merit of the recursive-based model in analyzing various scenarios, which may provide suitable information for health politicians and public health decision-makers.

中文翻译：

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对预测伊朗和土耳其 COVID-19 爆发的三种数学预测模型的评估


COVID-19大流行已成为每个国家都关心的问题，应用具有较高准确度的估计模型来提供准确的情况预测至关重要。在这项研究中，应用了三个明确的数学预测模型来预测伊朗和土耳其的 COVID-19 爆发。这些模型包括基于递归的方法、基于玻尔兹曼函数的模型和Beesham 预测模型。这些模型分别用于分析伊朗和土耳其 COVID-19 爆发前 106 天和前 87 天的确诊病例和死亡病例。此应用程序表明，这三个模型无法预测前 10 到 20 天的数据，具体取决于预测模型。另一方面，其余数据获得的结果表明，这三个预测模型的确定系数达到了很高的值，但它们产生了不同的平均绝对相对误差。根据比较，基于递归的模型表现最好，同时它对伊朗的 COVID-19 疫情的估计优于土耳其。通过基于递归的模型研究了应用或放松控制措施（例如土耳其宵禁和重新开放伊朗低风险企业）的影响。最后，结果证明了基于递归的模型在分析各种场景方面的优点，这可以为卫生政治家和公共卫生决策者提供合适的信息。