Discussions on a lockdown exit strategy have started earnestly as we approach the last week of the 21-day national lockdown. Politicians, public health experts and administrators are busy discussing various issues like whether to extend the lockdown, continue in ‘hotspot’ areas or give breaks between lockdowns; how to lift the lockdown in a staggered manner; and how to decrease the impact on the economy. It’s quite clear that there are no easy solutions. What might help now: intent, grit and core common-sense public health principles.
We need to let science and data guide our actions instead of political expediency. The lockdown serves the same purpose as net practice in cricket—it provides a protected environment to enforce social distancing, enhance testing, enforce quarantine and isolation, and ramp up health systems preparedness.
It does not make sense to lift lockdown when testing is not yet available widely because we cannot control the epidemic without testing. It should also not be lifted if the case count keeps increasing daily, and our health systems preparation is inadequate. However, it’s likely that there would be pressure due to political and economic imperative for lifting or loosening the lockdown either in masse or progressively in phases. This might open us to the possibility for a rapid rebound spread of infection, triggering future lockdown decisions. In this light, we feel that future lockdowns should be decentralized, graduated, data-driven and be guided by “Start where you are, Use what you have, Do what you can” triumvirate.
Decentralization is key because it does not make sense for a whole state or a large part of the state to go into lockdown when cases are only coming from one district. Future lockdown decisions should be taken by a district-level empowered committee using a transparent criterion. Lockdowns should not be ‘ALL or NONE’ affairs. It needs to be graduated -schools, colleges, malls, restaurants, bus stands, markets, function halls could be closed in a staggered manner or have restricted entry and use norms according to need. This decision should also be driven by evidence and data collation in real-time.
Start where you are
A lot of COVID-19 prediction models have been floating around. Beginning with population, factoring in growth rate, and other assumptions, the model then provides an estimated number of ICU beds or ventilators needed. Instead, we could flip the equation, begin with how many ICU beds and (functional) ventilators are there in a district and then extrapolate what is the number of serious COVID-19 cases the district could handle at any point of time. The district’s response capacity should be part of the calculation: testing kits, contact tracers, PPE stockpiles, isolations beds, ICU beds, ventilators and then determine the daily caseload the district can handle.
Use what you can
If testing is not available widely or if the results have a long turnaround time, then it might be wise to come up with a case definition for syndromic surveillance. Include both private and public hospitals in active surveillance to give real time data using whatsapp, sms or phone calls. Do intensive contact tracing. If specialists are not available, then see if you can train doctors through crash courses or use telemedicine with specialists. Arrange for reserve capacity in neighbouring districts. The available legal measures should be used to help in control activities.
Do what you can
Use data on daily new cases identified to build a simple mathematical tool with basic assumptions to project cases in future. The administration should fix trigger points for different levels of lockdown keeping in mind the data lag of 14 days i.e. once a lockdown is initiated, the remaining bed capacity should be enough for next 14 days, if not more, at current growth rate.
Periodically tweak the model based on experience and data. From the ICMR, MOHFW, and state health authorities, try to get data on presenting complaints; serial intervals; incubation periods; symptom onset duration; length of stay in isolation, hospital or ICU; treatment; positive cases from outside the district that were present in the district during their infectivity period. Keep trying to increase capacity in the meantime for the long run.
Lockdown causes severe economic and social disruption because of uncertainty and abruptness. Reduce uncertainty by fixing the lockdown duration beforehand and provide a lead period as was done in Singapore, but with adequate precautions, so that people can stock up and plan appropriately.
Abruptness can be reduced when people can anticipate an impending lockdown by understanding the “rules of the game” i.e. trigger points and if they are close to one. Communicate transparently by creating a dashboard with key indictors in an easy to understand manner— Current run rate (Daily new cases/serious cases), Required run rate (District capacity for daily new cases/serious cases), Wickets in Hand (No of empty hospital/ICU beds).
Publish this dashboard daily across multiple channels- SMS, WhatsApp, Social media, website, billboards. This dashboard might among other strategies ensure greater community participation in terms of personal hygiene and social distancing and avoid complacency setting in. When numbers begin to tick up, social peer pressure might set in. When a person gets symptoms, they will not delay seeking care if they know only 3 ICU beds are left.
COVID-19 will probably last till we get an effective drug treatment or vaccine or herd immunity. We need to smoulder the epidemic without it getting out of control till we reach one of this. The way forward is a bit like flying a kite, you need to give a bit of slack and then pull hard when required without the thread getting cut. There is a Kannada adage ‘Hasige idastu kalu chachu’ which translates to ‘Stretch your leg to the size of bed’ i.e. live within your means; for COVID-19, we might adapt it to stretch the epidemic to match the health systems capacity and use lockdowns strategically.
Dr Manjunath Shankar is a public health specialist. He participated in the US CDC Emergency Response (Modelling Task Force) to the West African Ebola outbreak in 2014-15.
Dr Anant Bhan is a researcher in global health, bioethics and health policy.