Network model and analysis of the spread of Covid-19 with social distancing

TL;DR

This paper develops a network-based model of human interactions to simulate Covid-19 spread and evaluate mitigation strategies like social distancing and lockdown, providing insights into potential second waves.

Contribution

It introduces a detailed network model incorporating various social interaction types and simulates disease spread under different mitigation scenarios, validated with real Covid-19 data.

Findings

Lockdowns reduce disease transmission in the model.

Gradual lifting of restrictions can lead to second waves.

Model aligns with real reproduction number data.

Abstract

The first mitigation response to the Covid-19 pandemic was to limit person-to-person interaction as much as possible. This was implemented by the temporary closing of many workplaces and people were required to follow social distancing. Networks are a great way to represent interactions among people and the temporary severing of these interactions. Here, we present a network model of human-human interactions that could be mediators of disease spread. The nodes of this network are individuals and different types of edges denote family cliques, workplace interactions, interactions arising from essential needs, and social interactions. Each individual can be in one of four states: susceptible, infected, immune, and dead. The network and the disease parameters are informed by the existing literature on Covid-19. Using this model, we simulate the spread of an infectious disease in the presence of various mitigation scenarios. For example, lockdown is implemented by deleting edges that denote non-essential interactions. We validate the simulation results with the real data by matching the basic and effective reproduction numbers during different phases of the spread. We also simulate different possibilities of the slow lifting of the lockdown by varying the transmission rate as facilities are slowly opened but people follow prevention measures like wearing masks etc. We make predictions on the probability and intensity of a second wave of infection in each of these scenarios.