SREP-20-02757A

This dataset corresponds to paper titled "A Mathematical Model for COVID-19 Considering Waning Immunity, Vaccination and Control Measures". In this work we define a modified SEIR model that accounts for the spread of infection during the latent period, infections from asymptomatic or pauci-symptomatic infected individuals, potential loss of acquired immunity, people’s increasing awareness of social distancing and the use of vaccination as well as non-pharmaceutical interventions like social confinement. We estimate model parameters in three different scenarios - in Italy, where there is a growing number of cases and re-emergence of the epidemic, in India, where there are significant number of cases post confinement period and in Victoria, Australia where a re-emergence has been controlled with severe social confinement program. Our result shows the benefit of long term confinement of 50% or above population and extensive testing. With respect to loss of acquired immunity, our model suggests higher impact for Italy. We also show that a reasonably effective vaccine with mass vaccination program can be successful in significantly controlling the size of infected population. We show that for India, a reduction in contact rate by 50% compared to a reduction of 10% in the current stage can reduce death from 0.0268% to 0.0141% of population. Similarly, for Italy we show that reducing contact rate by half can reduce a potential peak infection of 15% population to less than 1.5% of population, and potential deaths from 0.48% to 0.04%. With respect to vaccination, we show that even a 75% efficient vaccine administered to 50% population can reduce the peak number of infected population by nearly 50% in Italy. Similarly, for India, a 0.056% of population would die without vaccination, while 93.75% efficient vaccine given to 30\% population would bring this down to 0.036% of population, and 93.75% efficient vaccine given to 70% population would bring this down to 0.034%.

本数据集对应于一篇题为《考虑衰减免疫力、疫苗接种和控制措施的新型冠状病毒肺炎数学模型》的论文。在该研究中，我们定义了一种改进的SEIR模型，该模型能够描述潜伏期内的感染传播、无症状或症状轻微的感染者的感染情况、获得性免疫的潜在丧失、公众对保持社交距离的日益重视以及疫苗接种及非药物干预措施（如社会隔离）的应用。我们针对三种不同场景估计了模型参数：在意大利，病例数量持续增长且疫情复燃；在印度，隔离期结束后病例数量显著；在澳大利亚维多利亚州，通过严格的社交隔离措施成功控制了疫情的复燃。我们的结果表明，长期实施50%以上人口隔离和大规模检测具有显著效益。针对获得性免疫的丧失，我们的模型指出意大利的影响更为显著。我们还表明，一种有效合理的疫苗，配合大规模疫苗接种计划，可以显著控制感染人群规模。我们进一步展示，对于印度而言，将接触率降低50%相比于当前阶段的10%降低，可以将死亡率从0.0268%降低至0.0141%的人口比例。同样，对于意大利，将接触率减半可以将潜在感染高峰从15%的人口比例降低至不到1.5%的人口比例，并将潜在死亡率从0.48%降低至0.04%。至于疫苗接种，我们展示，即使75%有效率的疫苗对50%的人口进行接种，也能将意大利感染高峰人数降低近50%。同样，对于印度，未接种疫苗的情况下，0.056%的人口将死亡，而给予30%人口的93.75%有效率疫苗将死亡人数降至0.036%的人口比例，给予70%人口的93.75%有效率疫苗则将死亡人数降至0.034%的人口比例。