Network-based simulations of re-emergence and spread of COVID-19 in Aotearoa New Zealand

semanticscholar(2020)

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摘要
We simulate the late July/early August re-emergence and spread of COVID-19 in Aotearoa New Zealand. We use a stochastic, individual-based network model of all ≈5 million individuals in Aotearoa, and run simulations for a period of 30 days. Based on these simulations, we calculate: the expected time to detection of the first case after initial seed cases; the number of cases at the time of detection; the time until detection of a first case outside of Auckland; and how the overall number of cases increases without intervention. Our model includes interaction pathways, referred to as ‘contexts’ in the network, broken down into network ‘layers’ representing home, work, school, and community structure. Each simulation starts from initial (seed) cases corresponding to the first detected re-emergence cases in August 2020. We run 50 realisations of each simulation for 30 days — each simulation scenario corresponding to one of three different levels of transmission rate. To model the behaviour of individuals in the weeks prior to the August 11th re-emergence, we assume a moderate rate of people getting tested if mildly symptomatic. No contact tracing or intervention is present in this scenario, other than cases that test positive being isolated to their dwelling.
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