key: cord-1046778-7buiiy9h
authors: Chatterjee, Kaushik; Subramanian, Shankar; Chatterjee, Kaustuv; Yadav, Arun Kumar
title: Covid-19 in India: Post-Lockdown scenarios and provisioning for healthcare
date: 2020-06-18
journal: Med J Armed Forces India
DOI: 10.1016/j.mjafi.2020.06.004
sha: ed9b8765ab3b51f5409a70907d0844324ad6caa2
doc_id: 1046778
cord_uid: 7buiiy9h

Background 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. Methods 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. Results 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. Conclusion 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.

With the numbers of COVID-19 cases rising steadily in India, the central government announced a Lockdown from 25 Mar 2020 till 14 April 2020, that was subsequently extended to 3rd May 2020 and beyond. 1 A stochastic mathematical model to study the impact of Non-Pharmacological Interventions (NPIs) on the growth of the epidemic was published earlier. 2 The study showed that the peak number of infectious cases decreased rapidly, as greater percentage were quarantined or isolated. 2 Successful isolation of more than 40-50% of infectious cases was shown to be associated with reduction in the peak of cases, to numbers that could be managed within existing healthcare resources.

Steady state of the epidemic shall eventually be reached when herd immunity is achieved either through natural transmission or development of an effective vaccine. 3 Till then, it is imperative to protect those who are vulnerable, like the elderly or those with specific co-morbidities. 4 It is equally important to build capacity to handle the expected surge in cases. All NPIs, including Lockdown, lower and delay the peak of cases, making available precious time required for capacity-building. Lockdown also comes with considerable economic, social and cultural costs. 5 There is a need to identify optimal strategies to be implemented after lifting of Lockdown in India. Our study modeled various post-Lockdown scenarios and evaluated key healthcare constraints. Our aim was to identify the scenarios where healthcare availability would not be overwhelmed by the epidemic.

The impact of NPIs, including community containment, on growth of the pandemic in India and selected nations was mapped, to identify possible post-Lockdown trajectories, using data available in public domain. 6 The effect of increasing fraction of isolation/ quarantine of infectious cases was studied using a stochastic mathematical model. 2 A SEIR compartment model was modified to account for infectious cases that were isolated, preventing spread to the susceptible population (Table 1 & Suppl Table 1 ). The death rate was stratified across age with a calculated mean infection fatality rate of 0.498% (Suppl Table 2 ). [7] [8] [9] The number of individuals quarantined (Q 0 ) was assumed to be the sum of the number of patients under active treatment and/or quarantined/isolated. The initial number of infectious patients (I 0 ) was estimated from Q 0 assuming that Q 0 is q% of total infectious cases (I 0 + Q 0 ). (Suppl Table 3 ) Initial numbers of Quarantined, Recovered and Dead were taken from data published by MOHFW, GOI, on 07 Apr 20.

To measure the effectiveness of prevention of transmission, a 'q-metric' was created. The q-metric represents the percentage of infectious individuals in the population who were successfully contained (not allowed to transmit the infection to others). This was generated across various proportions from 1% to 90%.

Various hypothetical post-Lockdown scenarios were modeled for India. These ranged from total lifting of lockdown (permitting unrestricted mixing of population), to extension of enforced lockdown (till current natural transmission subsides), and various possibilities in between.

Constraints were listed regarding delivery of effective healthcare for COVID-19 epidemic in India and their criticality was evaluated using Delphi method. (Suppl Table   4 ) Criticality was assessed under the broad categories of infrastructure (Creating ICUs, facilities for isolation of large numbers of cases), medical equipment (Ventilators, other ICU equipment, Personnel Protective Equipment (PPE) for healthcare providers, test kits), personnel requirements (up-scaling of ICU care personnel, including training) and logistic processes (agility of response). 10 (Table 2) Capacity-building constraints were matched against modeled scenarios. Feasible scenarios thus generated yielded planning inputs in terms of likely numbers of cases, hospitalizations and ICU admissions (Suppl Table 2 ). 7-9 These scenarios were stochastically simulated to generate Confidence Intervals. (Suppl Table 5 ) Monitoring mechanism to assess the effectiveness of prevention of transmission was evaluated using the 'q-metric'. Death due to COVID-19 on a given day was taken as the input, that can be indirectly extrapolated to the number of active cases in the population around two weeks earlier (Suppl Table 6 ). 7, 11, 12 An algorithm was developed to estimate trajectory using death rate and progression of epidemic. The number of actual dead on a given day yielded the likely cases 14 days earlier 11 . Using the doubling rate on that day, the likely number of cases on the initial day was now calculated. All mathematical models were developed using MATLAB/SIMULINK and SimVoi add in for Microsoft Excel software.

Plotting the growth of cases shows that NPIs used in the population including Lockdown, appear to have an effect in reducing the rate of growth. (Fig 1a, 1b) This is also further reflected in reduction in death rates (Fig 1c, 1d )

Scenarios of different countries that had imposed some variation of community containment were compared. (Suppl Fig 1) China was the only country that had imposed complete community containment in affected areas and removed restrictions later. 13 On 09 Mar 20 complete lockdown was imposed all over Italy, which was to be in place till 13 April and has now been extended upto 03 May. 14, 15 Spain was placed in lockdown on 14 Mar for 4 weeks. 16 Countries like South Korea have followed a different path of early detection of infected patients through intensive contact-tracing and testing. By successfully isolating people who have come in contact with cases, they have managed to keep their numbers low, without lockdown. 17 USA has adopted some NPIs like social distancing.

Though USA has the highest number of cases of COVID-19, it has not yet proposed lockdown. 4

The post-Lockdown scenario with minimally restricted mixing of population, utilizing 'q-metric' of 1 yielded a curve with the epidemic peaking by end-June 2020, with a total of 1,086 million cases (Fig 2) . This scenario represents isolation of only those detected to be COVID positive and hospitalized. At its peak, there would be 102 million active cases translating to peak critical care and ventilator requirements of approximately 800,000 ICU beds. (Suppl Table 5 ) This represents a 20-25 fold increase over current availability.

It was shown that effective containment of >50% of infected cases was required to drastically reduce case numbers 2 . We next modeled continuing stringent lockdown till the transmission to fresh cases had ceased and the number of new cases reached zero. This would be achieved by isolation of 50-90% of infectious persons, representing a 'q-metric' of 50-90 (Fig 3) . This implies continued lockdown till mid-August when India would reach less than 100 active cases. This scenario, though mathematically elegant and possibly successful like in China, would come at significant economic and social costs, making it unfeasible. 18 Permitting international travel during or after, might result in re-seeding, as had happened in Heilongijang province of China 19 .

Multiple possibilities in-between the two extreme scenarios described above were modeled, reflecting intermediate levels of 'q-metric' of 10-40 (Suppl Fig 2 and Suppl Table 5 ). On ground, these partial containment measures could be targeted at specified geographical areas or Hotspots (e.g. a city or its part), specified at-risk populations (e.g. Elderly beyond 65 years) or to specified events (e.g. social, religious, sports, educational, entertainment or political related mass-gatherings). Table   7 ) Good governance is the third arm which ensures resolute implementation, together with adequate and agile supply-chains. This enables the other two to be effective.

India has 30,000 -50,000 ICU beds with ventilators, translating to 2.18 -3.64 per 100,000 population (Suppl Fig 3) . 24 A large proportion of these beds are already occupied by patients with other ailments and will continue to be so. Thus, capacity inevitably has to be expanded to meet the additional demands. Expansion with a modular plan of a 10-bed ICU set up, in clusters, appears workable from a logistics and where the nutritional, hygiene and other needs of the positive cases was met, while they were medically monitored and evacuated to healthcare facilities in case of symptoms worsening. 23 It is important to ensure that the asymptomatic or mildly symptomatic cases do not burden the already stretched healthcare facilities. 21 Currently, ICU teams are usually led by Intensivists, Anaesthesiologists or Physicians. It is roughly estimated that 60-80,000 such specialists (Team leaders) exist in India who can operate complex ventilators effectively, which would be required for critical COVID-19 cases (Suppl table 8 ). Of these, maybe 25% can be spared for COVID19 duties immediately without compromising rest of health care delivery. A 10 bedded COVID ICU (with 5-7 ventilators) translates to 6000-8000 such modular units, each catering to approx 2 lakh population. Each ICU will have to run 6 hourly shifts, each requiring a Team-leader, who will work 1 week every fortnight. This translates to 48-64,000 Team-leaders of which around 20,000 are already available. Expansion of manpower will require evolving a rapid training program conducted in functioning ICUs, in the duration before numbers rise exponentially.

Personal Protective Equipment (PPE) are protective gear designed for safety of healthcare workers by minimizing exposure to a biological agent (Suppl Table 9 ). 25 PPE are required at multiple locations including screening clinics, sample collection areas, isolation wards and ICUs. The estimated requirements of PPE are elaborated in Supplementary Table 9 . As the epidemic spreads, the requirement for testing shall increase exponentially too, quickly overwhelming available capacity. There is not only a need to enhance the testing capacity, but also to constantly evolve novel algorithms for diagnosis, incorporating cheaper and easier antibody testing. 26 Agility of organizational response is imperative. While catering for ventilators, it is expected that hotspots will appear at random locations. This has to be met with a reserve pool of personnel and equipment maintained centrally and at state levels, available for re-location at short notice.

Shortage of trained manpower to operate ventilators was identified as the key constraint. The maximum number of Ventilators that can be operated will be limited by the availability of Team-leaders, i.e specialists capable of operating Ventilators comprising Intensivists, Anesthesiologists and Physicians. As the number of cases rise, more of these specialists can be pressed to duty and augmented by Pulmonologists, Pediatricians, etc, in the expansion phase without significant additional training. In the surge phase, more numbers will be required. Therefore short course training of suitable personnel (Physicians and other clinical specialties) needs to be planned and undertaken immediately to prepare for surge when it happens.

Shortage of ventilators was identified as the other key constraint. It was estimated that India can realistically scale up ventilator numbers by 2 times, to 40,000 additional units. This would require an expanded production capacity of 5000 ventilators a month together with successful import of 10-12,000 ventilators. Thus, we can expect 80-85,000 total ventilators by October 2020, of which not more than 40-50,000 can be dedicated for COVID-19 ICUs. This number might marginally increase if some of the low-cost innovations in ventilators are used for non-COVID cases, permitting redistribution of existing ventilators. Of the 50,000 COVID ventilators, 5-10% should be held as reserve (between center and states) for rapid deployment to hotspots as required.

The other important constraint was PPE. While most of PPE can be sourced from manufacturers of Hazmat (Biological Hazardous Material) suits and other niche producers, the requirement for large numbers of N95 masks will have to be specifically addressed. Also, the logistics of delivering complete sets, not component items, to hospitals needs to be ensured, to reduce procurement woes. It was estimated that 10,460 PPE units were required for 1000 diagnosed cases (Suppl Table 10 ). One simple innovation, the Korean sample collection kiosk might reduce the requirement of PPE partially, and requires exploration. 27 However, collection of sample using kiosk may impact sensitivity due to practical considerations and needs further study.

How long to extend the Lockdown, then becomes a critical question. For the epidemic to die down, effective Lockdown with 'q-metric 'of 50 or more is required to be continued at least till mid-August as mentioned earlier.

In case that is not feasible and the Lockdown is to be lifted before that, there is no option but to follow it up with partial community containment. In order to keep the number of COVID-19 patients requiring utilization of these healthcare resources to below the possibly expanded capacity, it would be imperative to keep the peak number of infectious cases below 6 million. At a 'q-metric' of 19, the peak ventilator requirement Table 3 ) This implies that if we manage to prevent 19% or more of infected cases from transmitting the disease, then India will stay within the projected healthcare constraints. (Fig 4) .

There is a need to establish a monitoring system which should be able to track if the measures of containment fall below the expected trajectory of q-metric of 19.

Universal testing is impractical given the size of our population. There is no ready surrogate measure available for the number of infectious cases in the population, to track the effectiveness of containment measures. An objective measure available is the deaths due to COVID-19 on a given day, which can be indirectly extrapolated to the number of active cases in the population around two weeks earlier (Suppl Table 6 ). 7 A simple monitoring system devised. Using the actual numbers of deaths, the model estimated the total cases in the population. The ratio between the detected to predicted cases was plotted against containment bands to estimate the effectiveness of NPIs ( Fig   5) . This reveals the current q-metric achieved.

This would aid in decision-making about containment measures. It is envisaged that there would be areas of high concentration or high growth rates (local hotspots) that would continue to keep appearing across the country at various times. These hotspots may require a localized intensive response like the Bhilwara model. 28 The state of Kerala has shown the excellent containment among Indian states. This has been achieved by vigorous contact tracing, effective surveillance, efficient isolation/ quarantine and specialized COVID Care wards. 29 However, replication of models might be difficult because of varying local conditions like demographics, administrative capability etc and some models may be unscalable.

This model has similar limitations as other predictive mathematical models. 2 For ease of modeling we have assumed homogenous distribution of the Indian population that does not capture variations in population density or the urban-rural variations. We have also assumed equal susceptibility to COVID-19, which does not cater for variation in mixing, with stratification for age, occupation and travel. We have not modeled for pre-existing co-morbidities, which alters outcomes. Lastly the calculated infection fatality rates might be an underestimation, as it is pegged to western figures, who have better available healthcare infrastructure.

For Delphi technique only a small number of doctors were included. The modeling was not done for economic, social and cultural costs. The present model is based on early data available on public platforms till 07 April 2020. Future models may include more data leading to better predictions.

Our model revealed that 'q-metric' appeared to be a promising way to evaluate the spread and to monitor the epidemic. Mathematically, continuing Lockdown, reflected in a 'q-metric' of 50 or more till mid-August is the most effective solution for the epidemic to die down. As that might not be feasible due to economic and social compulsions, the Lockdown is likely to be lifted earlier. As such, model indicates achievement of q-metric of 10 during Lockdown. The key constraints were identified as trained personnel to lead ICUs and ventilator requirement. Our healthcare resources can at best be expanded to cater for the peak of epidemic reflected at a 'q-metric' of 19.

To achieve this, the healthcare sector is required to continue aggressive contacttracing, expanded testing and isolation of cases. The population would have to settle down to a collective 'new normal' of physical distancing, universal hand-washing, wearing masks, protection of at-risk individuals and reduction of mass-gatherings.

Efficient governance would enable healthcare and population measures to be effective.

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