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密歇根州 COVID-19 传播的经数据验证的临时免疫模型。

A data-validated temporary immunity model of COVID-19 spread in Michigan.

机构信息

Department of Mathematics & Computer Science, Lawrence Technological University, 21000 W 10 Mile Rd, Southfield, MI 48075, USA.

出版信息

Math Biosci Eng. 2022 Jul 18;19(10):10122-10142. doi: 10.3934/mbe.2022474.

Abstract

We introduce a distributed-delay differential equation disease spread model for COVID-19 spread. The model explicitly incorporates the population's time-dependent vaccine uptake and incorporates a gamma-distributed temporary immunity period for both vaccination and previous infection. We validate the model on COVID-19 cases and deaths data from the state of Michigan and use the calibrated model to forecast the spread and impact of the disease under a variety of realistic booster vaccine strategies. The model suggests that the mean immunity duration for individuals after vaccination is 350 days and after a prior infection is 242 days. Simulations suggest that both high population-wide adherence to vaccination mandates and a more-than-annually frequency of booster doses will be required to contain outbreaks in the future.

摘要

我们引入了一个用于 COVID-19 传播的分布式时滞微分方程疾病传播模型。该模型明确纳入了人口随时间变化的疫苗接种率,并为接种疫苗和先前感染都纳入了一个伽马分布的临时免疫期。我们在密歇根州的 COVID-19 病例和死亡数据上对模型进行了验证,并使用校准后的模型预测了在各种现实的加强针疫苗接种策略下疾病的传播和影响。该模型表明,个体接种疫苗后的平均免疫持续时间为 350 天,而感染后的平均免疫持续时间为 242 天。模拟结果表明,未来需要高度普及疫苗接种要求和每年多次加强针接种频率,才能控制疫情爆发。

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