论文标题
使用数据驱动的SEIRD模型预测印度Covid-19在印度的传播-19
Forecasting the transmission of Covid-19 in India using a data driven SEIRD model
论文作者
论文摘要
使用确定性易感性暴露感染的已恢复（SEIRD）隔室模型，已经研究了针对印度特异性病例的SARS-COV-2病毒引起的感染和死亡。最重要的流行病学参数之一，即感染的有效繁殖数量是从报告感染的每日生长速率数据中提取的，并包括在具有时间变化的模型中。我们评估了到目前为止实施的控制干预措施的影响，并估算了这些限制性措施避免的感染和死亡的病例数。我们进一步提供了印度未来Covid-19的传播范围的预测，并预测了各种潜在情况下感染和死亡的可能数量。
The infections and fatalities due to SARS-CoV-2 virus for cases specific to India have been studied using a deterministic susceptible-exposed-infected-recovered-dead (SEIRD) compartmental model. One of the most significant epidemiological parameter, namely the effective reproduction number of the infection is extracted from the daily growth rate data of reported infections and it is included in the model with a time variation. We evaluate the effect of control interventions implemented till now and estimate the case numbers for infections and deaths averted by these restrictive measures. We further provide a forecast on the extent of the future Covid-19 transmission in India and predict the probable numbers of infections and fatalities under various potential scenarios.
