Sustaining the economy under partial lockdown: A pandemic centric approach

TL;DR

This paper proposes an optimized partial lockdown strategy for COVID-19 that balances infection control with economic activity, using a SEIR model and NSGA-II algorithm to identify effective policies.

Contribution

It introduces a novel approach combining SEIR modeling with social behavior influence and multi-objective optimization to optimize lockdown duration and economic impact.

Findings

Optimal lockdown policies for ten weeks identified

Model balances infection control with economic activity

Social distancing influenced by infection awareness modeled

Abstract

As the world fights to contain and control the spread of the Novel Coronavirus, countries are imposing severe measures from restrictions on travel and social gatherings to complete lockdowns. Lockdowns, though effective in controlling the virus spread, leaves a massive economic impact. In a country like India with 21.9 % of its population below the poverty line, lockdowns have a direct impact on the livelihood of a large part of the population. Our approach conforms to healthcare and state practices of reducing human to human contact, by optimizing the lockdown strategy. We propose resuming economic activities while keeping healthcare facilities from being overwhelmed. We model the coronavirus pandemic as SEIR dynamic model for a set of states as nodes with certain population and analyze the model output before and after complete lockdown. Social distancing that people would willingly follow, in the no lockdown situation is modeled as being influenced with the knowledge of the current number of infection by imitating Granovetter threshold model. We then provide optimal lockdown policy solutions for the duration of ten weeks using NSGA-II optimization algorithm. While there are many studies that focus on modelling the transmission of COVID-19, ours is one of the few attempts to strike a balance between number of infections and economic operations.