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.
Highlights
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
There is a high level of uncertainty on the level and duration of effective immunity to COVID-19 in populations and on how these parameters will affect the number of vaccinations required to suppress the disease
Many of the models currently used to predict the COVID-19 pandemic do not take into consideration the structure of the underlying human interaction network
Summary
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. The exact network topology can be varied in different simulations, and even with a fixed network topology repeated simulations may still lead to varying results given the stochastic nature of the process, which provides a broader coverage of the range of observed outcomes in different locations These models better account for observed effects such as superspreading, which can be linked to the scale-free structure of social contact networks. Number of days for which an individual that has contracted the disease can pass it on to others
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