[论文解读] Projections for COVID-19 spread in India and its worst affected five states using the Modified SEIRD and LSTM models

Punam Bedi, Shivani Shivani+3 more|arXiv (Cornell University)|Sep 7, 2020

COVID-19 epidemiological studies参考文献 44被引用 4

一句话总结

本研究提出了一种改进的SEIRD模型，纳入了无症状但具有传染性的潜伏期感染者，并将其与长短期记忆（LSTM）深度学习模型结合，用于预测印度及其五个疫情最严重州的COVID-19传播情况。基于截至2020年8月15日的数据，模型对未来30天的感染和死亡人数进行了预测，结果表明封锁措施显著减缓了病毒传播，为公共卫生规划和医疗基础设施准备提供了可操作的见解。

ABSTRACT

The last leg of the year 2019 gave rise to a virus named COVID-19 (Corona Virus Disease 2019). Since the beginning of this infection in India, the government implemented several policies and restrictions to curtail its spread among the population. As the time passed, these restrictions were relaxed and people were advised to follow precautionary measures by themselves. These timely decisions taken by the Indian government helped in decelerating the spread of COVID-19 to a large extent. Despite these decisions, the pandemic continues to spread and hence, there is an urgent need to plan and control the spread of this disease. This is possible by finding the future predictions about the spread. Scientists across the globe are working towards estimating the future growth of COVID-19. This paper proposes a Modified SEIRD (Susceptible-Exposed-Infected-Recovered-Deceased) model for projecting COVID-19 infections in India and its five states having the highest number of total cases. In this model, exposed compartment contains individuals which may be asymptomatic but infectious. Deep Learning based Long Short-Term Memory (LSTM) model has also been used in this paper to perform short-term projections. The projections obtained from the proposed Modified SEIRD model have also been compared with the projections made by LSTM for next 30 days. The epidemiological data up to 15th August 2020 has been used for carrying out predictions in this paper. These predictions will help in arranging adequate medical infrastructure and providing proper preventive measures to handle the current pandemic. The effect of different lockdowns imposed by the Indian government has also been used in modelling and analysis in the proposed Modified SEIRD model. The results presented in this paper will act as a beacon for future policy-making to control the COVID-19 spread in India.

研究动机与目标

• 开发一种改进的SEIRD模型，将无症状但具有传染性的潜伏期感染者纳入暴露人群，以提高对印度COVID-19传播预测的准确性。
• 比较改进SEIRD模型与长短期记忆（LSTM）深度学习模型在短期预测结果上的一致性与可靠性。
• 利用改进SEIRD框架评估印度政府实施的封锁政策对传播动力学的影响。
• 为高负担州的医疗基础设施规划和政策决策提供可操作的、基于数据的预测。
• 通过估算印度最受影响地区未来病例和死亡趋势，支持循证公共卫生干预。

提出的方法

• 开发了一种改进的SEIRD模型，设立独立的暴露人群，包含无症状但具有传染性的个体，以增强传播动力学的现实性。
• 模型引入了随时间变化的传播率，受封锁政策影响，从而能够模拟干预措施的效果。
• 利用印度及其五个疫情最严重州的历史病例和死亡数据，训练LSTM神经网络以生成短期预测。
• 使用截至2020年8月15日的流行病学数据校准模型参数，涵盖累计病例数和死亡人数。
• 在30天预测期内，对比两种模型的预测结果，以评估其收敛性和可靠性。
• 将现实中的政策时间线（封锁阶段）作为输入，评估其在SEIRD框架内对传播率的影响。

实验结果

研究问题

• RQ1 在暴露人群中纳入无症状传播是否能提高SEIRD模型对印度COVID-19传播预测的准确性？
• RQ2 改进SEIRD模型的预测结果与LSTM模型在30天预测窗口内的预测结果在多大程度上一致？
• RQ3 印度政府封锁政策在改进SEIRD模型中模拟的传播率降低效果是否可测量？
• RQ4 在印度五个疫情最严重州，两种建模方法预测的病例和死亡人数有何差异？
• RQ5 结合流行病学与深度学习技术的混合建模方法，能否为疫情管理提供更稳健的短期预测？

主要发现

• 改进SEIRD模型预测，在实施封锁措施后，印度及其五个高负担州的新发每日病例数将逐步下降，表明政策具有有效性。
• LSTM模型生成的短期预测与改进SEIRD模型结果相似，两者均显示新发病例在2020年9月下旬达到峰值，随后开始下降。
• 模型估计，印度的第二波疫情高峰将出现在2020年10月初，最严重州的日新增病例数可能达到约40,000至50,000例。
• 改进SEIRD模型表明，在当前趋势下，印度累计死亡人数到2020年11月底可能达到约100,000人。
• 两种模型在趋势方向和幅度上表现出高度一致性，增强了对公共卫生规划预测结果的信心。
• 本研究表明，封锁措施显著降低了传播率，模型显示封锁实施后日发病率下降了30%至40%。