Purpose

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

Design/methodology/approach

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

Findings

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

Practical implications

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

Originality/value

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

中文翻译：

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一项预测 COVID-19 大流行后医疗保健能力动态的研究

目的

本研究旨在评估医疗资源容量扩张与疾病传播之间的动态。此外，该研究还估计了适当应对大流行所需的资源。

设计/方法论/途径

本研究采用系统动力学模拟和情景分析的方法，对易感暴露感染和康复（SEIR）模型进行了修改实验。这些实验评估诊断能力扩展，以确定合适的扩展计划和时间表。随后，使用人工神经网络（ANN）和自回归积分移动平均（ARIMA）这两种常用的预测工具来估计感染数据可用期间的床位需求。

发现

研究结果表明，积极的检测、隔离和检疫整合可以成为预防疾病爆发的有效策略。研究结果表明，决策者必须在疫情爆发的前两周迅速扩大诊断能力，以支持积极的检测和隔离。此外，结果证实，如果没有这些策略，德里的医疗资源短缺至少两个月。此外，研究结果通过模拟疫情爆发早期各种参数未知的一系列接触率和疾病传染性，强调了产能扩张时间表的重要性。此外，人们还发现，在获得大流行数据的情况下，预测工具可以有效地估计医疗资源需求。

实际影响

政策制定者可以利用本研究中开发的模型来适当设计应对计划。关于需要多少诊断能力以及何时扩大能力以最大限度地减少感染传播的决定已经在德里市得到证实。此外，该研究提出了决策支持系统（DSS），以协助决策者在疾病爆发期间进行短期和长期规划。

原创性/价值

该研究估计了采用积极测试策略所需的资源。进行了多次实验，成功验证了仿真模型的稳健性。尝试对 SEIR 模型进行诊断能力增量、隔离和测试块的修改，为测试策略提供独特的视角。已经系统地解决了疫情的预防问题。