COVID-19 Modeling Outcome versus Reality in Sweden.
COVID-19 Modeling Outcome versus Reality in Sweden.
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DOI：
10.3390/v14081840
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发表时间：
2022-08-22
期刊：
影响因子：
--
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文献类型：
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作者：
基于传染病传播的数学模型来预测新冠肺炎大流行的发展一直非常困难，而且由于主要的非药物干预(NPI)，目前仍不清楚这些模型在多大程度上符合实际情况。为了阐明这一问题，瑞典在2020年秋季至2021年春季这段时间内的情况特别令人感兴趣，因为国家生产总值指数相对较小，而且只有少量更新。我们发现，最先进的模型严重高估了价差，除非我们假设社交互动在整个时间框架内持续减少，这种方式与谷歌移动数据或NPI或公共假期的更新没有很好的相关性。这就引出了这样一个问题：现代Seir类型的数学模型是否不适合模拟SARS-CoV-2在人类群体中的传播，或者SARS-CoV-2的某些特定特征是否抑制了传播。我们证明，通过假设对SARS-CoV-2有一定程度的预免疫，我们得到了几乎完美的数据拟合，并讨论了在数学模型中哪些因素可能导致预免疫。在这种情况下，在给定的限制下，一种形式的群体免疫被达到了两次(第一次是针对武汉株，然后是对阿尔法株)，病例的最终下降是由于易感病例的耗尽，而不是疫苗接种活动。
It has been very difficult to predict the development of the COVID-19 pandemic based on mathematical models for the spread of infectious diseases, and due to major non-pharmacological interventions (NPIs), it is still unclear to what extent the models would have fit reality in a “do nothing” scenario. To shed light on this question, the case of Sweden during the time frame from autumn 2020 to spring 2021 is particularly interesting, since the NPIs were relatively minor and only marginally updated. We found that state of the art models are significantly overestimating the spread, unless we assume that social interactions significantly decrease continuously throughout the time frame, in a way that does not correlate well with Google-mobility data nor updates to the NPIs or public holidays. This leads to the question of whether modern SEIR-type mathematical models are unsuitable for modeling the spread of SARS-CoV-2 in the human population, or whether some particular feature of SARS-CoV-2 dampened the spread. We show that, by assuming a certain level of pre-immunity to SARS-CoV-2, we obtain an almost perfect data-fit, and discuss what factors could cause pre-immunity in the mathematical models. In this scenario, a form of herd-immunity under the given restrictions was reached twice (first against the Wuhan-strain and then against the alpha-strain), and the ultimate decline in cases was due to depletion of susceptibles rather than the vaccination campaign.
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