论文标题
新型冠状病毒（Covid-19）案例的实时预测和风险评估：数据驱动分析
Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis
论文作者
论文摘要
2019年冠状病毒疾病（Covid-19）已成为影响全球201个国家和地区的国际公共卫生紧急情况。截至2020年4月4日，这引起了大流行爆发，超过11,1643次确认感染，全世界有59,170多人死亡。本文的主要重点是两个方面：（a）对多个国家的未来Covid-19案件进行短期（实时）预测； （b）通过发现国家的各种重要人口特征以及某些疾病特征，对一些深远影响国家的新型Covid-19的风险评估（就病例死亡率而言）。为了解决第一个问题，我们提出了一种基于自回归综合运动平均模型和基于小波的预测模型的混合方法，该模型可以产生短期（提前十天）的预测，以预测加拿大，法国，印度，韩国，韩国和英国的日常确认案件数量。对不同国家的未来爆发的预测将有助于有效分配医疗保健资源，并将充当政府决策者的早期制定系统。在第二个问题中，我们应用了一种最佳回归树算法来找到基本的因果变量，从而显着影响不同国家的病例死亡率。该数据驱动的分析必然会为50个受影响的国家的早期风险评估研究提供深入的见解。
The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting 201 countries and territories around the globe. As of April 4, 2020, it has caused a pandemic outbreak with more than 11,16,643 confirmed infections and more than 59,170 reported deaths worldwide. The main focus of this paper is two-fold: (a) generating short term (real-time) forecasts of the future COVID-19 cases for multiple countries; (b) risk assessment (in terms of case fatality rate) of the novel COVID-19 for some profoundly affected countries by finding various important demographic characteristics of the countries along with some disease characteristics. To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. The predictions of the future outbreak for different countries will be useful for the effective allocation of health care resources and will act as an early-warning system for government policymakers. In the second problem, we applied an optimal regression tree algorithm to find essential causal variables that significantly affect the case fatality rates for different countries. This data-driven analysis will necessarily provide deep insights into the study of early risk assessments for 50 immensely affected countries.
