论文标题
通过数据驱动方法估算八个国家的Covid-19的感染范围
Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach
论文作者
论文摘要
COVID-19大流行已经影响了世界上所有国家，造成了大量死亡，伴随着社会，财务和教育组织的重大破坏。中国实施的严格纪律措施非常有效，因此大多数世界国家都在各种程度上采用。感染持续时间和受感染者的数量对于与大流行的战斗至关重要。我们使用在中国传播的疾病的定量景观作为基准，并利用来自八个国家的感染数据来估计每个国家中感染的完全演变。该分析预测，每个国家的预期每日感染的预期次数都成功，也许更重要的是，每个国家的流行持续时间。我们的定量方法是基于高斯扩散假设，该假设在简单的动态感染模型中被施加的措施而导致出现。一旦现象结束，这可能会产生后果并散发出策略效率。
The COVID-19 pandemic has affected all countries of the world producing a substantial number of fatalities accompanied by a major disruption in their social, financial, and educational organization. The strict disciplinary measures implemented by China were very effective and thus were subsequently adopted by most world countries to various degrees. The infection duration and number of infected persons are of critical importance for the battle against the pandemic. We use the quantitative landscape of the disease spreading in China as a benchmark and utilize infection data from eight countries to estimate the complete evolution of the infection in each of these countries. The analysis predicts successfully both the expected number of daily infections per country and, perhaps more importantly, the duration of the epidemic in each country. Our quantitative approach is based on a Gaussian spreading hypothesis that is shown to arise as a result of imposed measures in a simple dynamical infection model. This may have consequences and shed light in the efficiency of policies once the phenomenon is over.
