A Networks Based ABM to Investigate the Efficacy of Lockdowns and other Mitigation Strategies 

The COVID-19 pandemic is one that has left the world reeling at how it has managed to affect the entire globe and killed hundreds of thousands. The intensity with which the virus struck called for an extreme reaction to keep it from overwhelming the healthcare system. To prevent deaths from this highly contagious virus we saw countries go into lockdowns. Businesses shut down; the roads were traffic-less; entire cities became ghost-towns. As lockdowns extended, the trade-off between staying at home and being safe from the virus and going out to work to earn enough to not succumb to economic hardships became apparent; a number of lockdown strategies were proposed to keep the costs (in terms of lives lost) to a minimum. Other, significantly less drastic, measures were proposed too, with masks heading the list. This project aims to develop an agent-based-model that allows us to simulate the real-world situation and investigate what kind of lockdown strategy is optimal, specifically in Pakistan, where a significant proportion of its population cannot adapt to a “work from home” climate, so copy-pasting strategies from more developed countries may not work. We aim to create a platform to plot the different possible courses the situation can take, and the impact of different policies in each situation. Since the situation is fluid and doesn’t seem to be ending any time soon, with fears of multiple waves, we believe this will be an important contribution to the debate surrounding policy related to Corona.