Chatterjee Kaushik, Shankar Subramanian, Chatterjee Kaustuv, Yadav Arun Kumar
Professor & Head, Department of Psychiatry, Armed Forces Medical College, Pune 411040, India.
Professor & Head, Department of Internal Medicine, Armed Forces Medical College, Pune 411040, India.
Med J Armed Forces India. 2020 Oct;76(4):387-394. doi: 10.1016/j.mjafi.2020.06.004. Epub 2020 Jun 18.
With the rise of Coronavirus disease 2019 (COVID-19) cases in India, lockdown was imposed from March 25, 2020. We studied post-lockdown scenarios and evaluated health-care constraints. Our aim was to identify the scenarios in which health-care availability would not be overwhelmed.
A modified compartmental SEIR stochastic model was used to calculate peak cases at various levels of effectiveness of prevention of transmission. Health-care constraints were evaluated using a Delphi study. We developed "q-metric" to evaluate the epidemic. Key constraints were matched against scenarios generated, and a monitoring mechanism was devised.
Continuing lockdown ("q-metric" of >50) until mid-August was theoretically the most effective solution to end the epidemic. Lockdown might however be lifted earlier owing to various compulsions. The key constraints were identified as trained manpower and ventilators. It was estimated that shortfall of specialists to operate ventilators for COVID-19 intensive care units was approximately 40,000. This requires re-purposing of other specialists and short-term training to meet the surge. The shortage of ventilators is around 40,000-50,000. Procuring beyond those numbers would be infructuous owing to limits of training manpower. After lifting lockdown, the aim should be to contain the epidemic within the availability of key constraints. Our model suggests that this can be achieved by community containment and other non-pharmacological interventions at a "q-metric" of 19. An algorithm using "q-metric" was developed to monitor the epidemic.
Various post-lockdown scenarios were simulated. Trained manpower and ventilators were identified as key health-care constraints. Partial community containment measures will require to be continued after the current lockdown is lifted.
随着2019年冠状病毒病（COVID-19）病例在印度增多，2020年3月25日开始实施封锁。我们研究了封锁后的情况并评估了医疗保健方面的限制因素。我们的目的是确定医疗保健资源不会不堪重负的情况。
使用改进的分区SEIR随机模型来计算在不同传播预防效果水平下的病例峰值。通过德尔菲研究评估医疗保健方面的限制因素。我们开发了“q指标”来评估疫情。将关键限制因素与生成的情况进行匹配，并设计了一种监测机制。
理论上，持续封锁（“q指标”>50）直到8月中旬是结束疫情的最有效解决方案。然而，由于各种强制因素，封锁可能会提前解除。确定的关键限制因素是训练有素的人力和呼吸机。据估计，为COVID-19重症监护病房操作呼吸机的专科医生短缺约40000人。这需要重新调配其他专科医生并进行短期培训以应对激增需求。呼吸机短缺约40000 - 50000台。由于训练有素的人力有限，采购超过这个数量将徒劳无功。解除封锁后，目标应是在关键限制因素可用的范围内控制疫情。我们的模型表明，通过社区控制和其他非药物干预措施，在“q指标”为19时可以实现这一目标。开发了一种使用“q指标”的算法来监测疫情。
模拟了各种封锁后的情况。训练有素的人力和呼吸机被确定为关键的医疗保健限制因素。在当前封锁解除后，部分社区控制措施仍需继续实施。
