New Delhi: The SARS-CoV-2 (COVID-19) pandemic has resulted in severely stressed healthcare systems across the world, and in many countries, the capacity of existing infrastructure has proven to be inadequate for managing the COVID-19 caseload. To meet this demand-capacity imbalance, makeshift facilities for COVID-19 have been set up around the world—for example, Nightingale hospitals in the UK and COVID-19 Care Centres (CCCs) in India.

In this context, there is a need for analytical tools capable of providing quantitative assessments of the extent to which the existing healthcare infrastructure, which is often composed of multiple tiers, in a given region can respond adequately to pandemic caseloads. This is important for planning the capacity of makeshift COVID-19 care facilities.

Such a tool must also be able to handle the substantial uncertainty associated with these situations, including the uncertainty around the care provision process, the caseload, patient progression, and patient flow across the network.

A team of researchers, including Prof. Varun Ramamohan, Department of Mechanical Engineering, IIT Delhi; Dr. Karan Madan, Additional Professor of Pulmonology, Critical Care, and Sleep Medicine, AIIMS New Delhi; Prof. Navonil Mustafee, University of Exeter Business School, United Kingdom; and Mohd Shoaib, Research Scholar, IIT Delhi, developed a probabilistic discrete-event simulation of the operations of a public healthcare network in a north Indian district and demonstrated its use for planning the capacity of the network for a single wave of the COVID-19 pandemic.

The public healthcare network that they modelled was based on the COVID-19 caseload during the first wave in one of the districts in North India, having 10 primary health centres (PHCs), 3 community health centres (CHCs), and a district hospital. Simulation models of the operations of each of these facilities were created and then linked together to build the public health network simulation.

The researchers formulated a pandemic response strategy in collaboration with AIIMS New Delhi and the University of Exeter, United Kingdom, that was incorporated into the simulation.

"The simulation outcomes indicated that the existing capacity of the public health network—in particular in terms of the number of general ward beds, ICU beds with supplemental oxygen, and ICUs with ventilator beds—needs to be enhanced to handle the wave and that a makeshift COVID-19 care centre would be required. However, interestingly, the capacity of the medical personnel—doctors and nurses—was found to be sufficient in terms of medical care provision (not considering administration and paperwork) during the simulated wave. The simulation tool was then used to estimate the capacity for the makeshift COVID-19 care centre so that patient wait times to receive appropriate care are reduced", Prof. Varun Ramamohan said.

Overall, while the simulation tool was demonstrated using COVID-19 as a case study, it can be used to plan the operational response of the public health network in a given region to any health crisis, such as a dengue or cholera outbreak. It can even be used to evaluate changes in operational policy during routine care conditions, such as when a new referral policy from lower-tier to higher-tier facilities is introduced.

The study titled "Leveraging multi-tier healthcare facility network simulations for capacity planning in a pandemic" is published in Socio-Economic Planning Sciences (SEPS): https://www.sciencedirect.com/science/article/pii/S0038012123001726

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