COVID-19 vaccination strategies depend on the underlying network of social interactions

Abstract

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, different mitigation and management strategies limiting economic and social activities have been implemented across many countries. Despite these strategies, the virus continues to spread and mutate. As a result, vaccinations are now administered to suppress the pandemic. Current COVID-19 epidemic models need to be expanded to account for the change in behaviour of new strains, such as an increased virulence and higher transmission rate. Furthermore, models need to account for an increasingly vaccinated population. We present a network model of COVID-19 transmission accounting for different immunity and vaccination scenarios. We conduct a parameter sensitivity analysis and find the average immunity length after an infection to be one of the most critical parameters that define the spread of the disease. Furthermore, we simulate different vaccination strategies and show that vaccinating highly connected individuals first is the quickest strategy for controlling the disease.

Public Policy Measures to Increase Anti-SARS-CoV-2 Vaccination Rate in Russia

“…Persons with antivaccination sentiments are more active than supporters of vaccination, including activity in social networks [ 88 , 89 , 90 ]. Therefore, the percentage of vaccination antagonists in our research is likely to be higher than in the entire Russian society.…”

A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks

“…Network science plays a fundamental role in epidemiology, and many recent pieces of research modeled disease spreading using network models 4 – 10 . A spreading disease can be described as a network where nodes represent the individuals and links (edges) represent the social contacts between them 9 , 11 – 13 . The two classes of interventions that can be implemented to reduce the size of an outbreak can be divided into pharmacological interventions, PI, such as vaccinations, and non-pharmacological interventions, NPI, such as social distancing, washing hands, and lockdowns.…”

A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks

“…Network science plays a fundamental role in epidemiology, and many recent pieces of research modeled disease spreading using network models [4][5][6][7][8][9][10] . A spreading disease can be described as a network where nodes represent the individuals and links (edges) represent the social contacts between them 9,[11][12][13] . The two classes of interventions that can be implemented to reduce the size of an outbreak can be divided into pharmacological interventions, PI, such as vaccinations, and nonpharmacological interventions, NPI, such as social distancing, washing hands, and lockdowns.…”

“…NPIs are a set of changes in social behavior that aim to reduce the social interactions (links or connections) among individuals, like during lockdowns 14,15 , or by reducing the risk that the disease is transmitted for each social interaction like for wearing masks 16 . One of the fundamental problems in network epidemiology is finding the best vaccination strategy to halt epidemics 6,7,13,17 . Node removal (attack) on complex social networks is an optimal framework to model vaccination strategies over a population where diseases can spread 13,[17][18][19][20][21][22] .…”

“…One of the fundamental problems in network epidemiology is finding the best vaccination strategy to halt epidemics 6,7,13,17 . Node removal (attack) on complex social networks is an optimal framework to model vaccination strategies over a population where diseases can spread 13,[17][18][19][20][21][22] . As a first approach, the problem can be mapped to the classic percolation problem in graph theory, where nodes are immunized (removed) from the network to induce the fastest network dismantling 6,7,17 .…”