Minimizing the Effect of COVID-19 Pandemic Through an Adaptive Staggered Approach to Lock-Downs

Abstract

COVID-19 has been declared as a pandemic by WHO. To control the spread of infection, many states have implemented some form of lock-down, ranging from partial to full lock-downs on basis of geographical boundaries. Lock-downs are intended to break social contact, which determines the rate of transmission. In the SIR model, the general population is broken down into Susceptible, Infected and Recovered. Here, we propose a partitioning technique to reduce the transmission rate further, reducing the rate of growth of active cases. This allows for providing better medical care to the patients, reducing cumulative deaths. We are not arguing the effectiveness of complete lock down, but proposing a mechanism for the pre and post complete lock down. We will be proving that the partitioning approach reduces the probability of a person being affected by COVID-19 compared to the scenario of post complete lock-down of areas with high population density. Complete lock-downs also result in problems of food scarcity, predatory pricing, law and order problems, and business losses. A partitioning based approach mitigates quite a few of these problems. Implementing a partial lock down might appear as resource exhausting on the state, but the economy does not come to a stand still. This is an adaptive approach where the populace is partitioned based on infected density, allowing a specific partition of the population freedom to continue their day to day work.