论文标题
COVID-19数据分析和预测：阿尔及利亚与世界
COVID-19 Data Analysis and Forecasting: Algeria and the World
论文作者
论文摘要
2019年的新型冠状病毒疾病19 Covid-19一直带领世界陷入杰出的危机。截至2020年5月19日，该病毒已蔓延到215个国家，其中超过4,622,001例已确认病例，311,916例报告了全球死亡，其中包括阿尔及利亚，有7201例案件和555例死亡。分析和预测Covid-19案件和死亡增长在许多方面可能是有用的，政府可以估算医疗设备并采取适当的政策反应，专家可以近似疾病的高峰和终结。在这项工作中，我们首先培训了一个时间序列的先知模型，以根据先前报道的数字分析和预测阿尔及利亚的Covid-19案件和死亡人数。然后，为了更好地了解Covid-19的传播和特性，我们包括可能有助于加速/减慢病毒传播，从可靠来源构建数据集并进行大规模数据分析的外部因素，并考虑了全球82个国家/地区的大规模数据分析。评估结果表明，时间序列的先知模型准确地预测了阿尔及利亚的病例和死亡人数分别为218.87和4.79，而预测表明，预计在接下来的几周内，病例和死亡的总数将增加。此外，全球数据驱动的分析揭示了病例数量和死亡人数的增加/减少与可能有助于加速/减慢病毒传播（例如地理，气候，健康，经济和人口统计学因素）的相关性。
The novel coronavirus disease 2019 COVID-19 has been leading the world into a prominent crisis. As of May 19, 2020, the virus had spread to 215 countries with more than 4,622,001 confirmed cases and 311,916 reported deaths worldwide, including Algeria with 7201 cases and 555 deaths. Analyze and forecast COVID-19 cases and deaths growth could be useful in many ways, governments could estimate medical equipment and take appropriate policy responses, and experts could approximate the peak and the end of the disease. In this work, we first train a time series Prophet model to analyze and forecast the number of COVID-19 cases and deaths in Algeria based on the previously reported numbers. Then, to better understand the spread and the properties of the COVID-19, we include external factors that may contribute to accelerate/slow the spread of the virus, construct a dataset from reliable sources, and conduct a large-scale data analysis considering 82 countries worldwide. The evaluation results show that the time series Prophet model accurately predicts the number of cases and deaths in Algeria with low RMSE scores of 218.87 and 4.79 respectively, while the forecast suggests that the total number of cases and deaths are expected to increase in the coming weeks. Moreover, the worldwide data-driven analysis reveals several correlations between the increase/decrease in the number of cases and deaths and external factors that may contribute to accelerate/slow the spread of the virus such as geographic, climatic, health, economic, and demographic factors.
