April 27, 2020
Modelling Projections Based On Initial 21-day Lockdown: Is This How It Will Unravel In India?
On 25th March 2020, India announced a nationwide 21-day lockdown till 14th April 2020. A study, conducted by researchers at the Center For Disease Dynamics, Economics & Policy (CDDEP), Johns Hopkins University, and Princeton University, has modeled the impact of the 21-day lockdown on the spread of COVID-19 in India before, during, and after the lockdown is lifted. Since then, the lockdown has been extended till 3rd May 2020, with guidelines from the Government of India regarding tightened and relaxed restrictions.
The analysis is based on four scenarios:
- Baseline, in which the disease continues to spread with no lockdown, social distancing, or other intervention and no change in transmission rate. R0* = 2.66 (in line with estimates from other country contexts)
- Moderate Lockdown, in which transmission is reduced to R0 of 2 during lockdown period, then transmission resumes at R0 of 2.4.
- Hard Lockdown, in which transmission is reduced to R0 of 1.5 during lockdown period, then transmission resumes at R0 of 2.4.
- Hard Lockdown and Continued Social Distancing/Isolating Cases, in which transmission is reduced to R0 of 1.5 during lockdown period, then, through social distancing regulations and isolation of symptomatic individuals, resumes at R0 of 2.
*The basic reproductive number or R0 of an infectious agent (in this case, the Covid-19 virus) is an epidemiological term to describe the average number of infections caused by one infected individual in a completely susceptible population.
In the model, confirmed cases represent symptomatic cases. There may be a more drastic increase in confirmed cases in the near future than predicted as testing capacity is increased and contact tracing is continued. Moreover, the model does not yet include seasonal effects or mutations in the virus. These factors may reduce transmission without any human involvement.
The study shows that existing lockdown measures can effectively slow the spread of Covid-19 hospitalizations and moderate infections compared to a lack of interventions. A greater decrease in the long-term transmission rate should significantly reduce peak infections and hospitalizations, thus lessening the load on the healthcare system and mortality risk among high-risk patients. Although this study modeled only the reduction of contacts in the symptomatic population (confirmed cases), reducing contacts and maintaining physical distancing in the general population can reduce the peak further. Some of the policies that help to reduce the transmission of the virus include immediate isolation of all individuals with symptoms of influenza-like illness and severe acute respiratory illness, physical distancing and universal masking, restrictions on large gatherings and events, improved sanitation and hygiene, and increased testing availability. A combination of these policies should remain in effect in order to achieve long-term impact.
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