Department of Biology, Ecole Normale Superieure, University PSL, CNRS, Paris, France.
INSERM U1024, Paris, France.
PLoS One. 2023 Jan 17;18(1):e0278882. doi: 10.1371/journal.pone.0278882. eCollection 2023.
Before the availability of vaccines, many countries have resorted multiple times to drastic social restrictions to prevent saturation of their health care system, and to regain control over an otherwise exponentially increasing COVID-19 pandemic. With the advent of data-sharing, computational approaches are key to efficiently control a pandemic with non-pharmaceutical interventions (NPIs). Here we develop a data-driven computational framework based on a time discrete and age-stratified compartmental model to control a pandemic evolution inside and outside hospitals in a constantly changing environment with NPIs. Besides the calendrical time, we introduce a second time-scale for the infection history, which allows for non-exponential transition probabilities. We develop inference methods and feedback procedures to successively recalibrate model parameters as new data becomes available. As a showcase, we calibrate the framework to study the pandemic evolution inside and outside hospitals in France until February 2021. We combine national hospitalization statistics from governmental websites with clinical data from a single hospital to calibrate hospitalization parameters. We infer changes in social contact matrices as a function of NPIs from positive testing and new hospitalization data. We use simulations to infer hidden pandemic properties such as the fraction of infected population, the hospitalisation probability, or the infection fatality ratio. We show how reproduction numbers and herd immunity levels depend on the underlying social dynamics.
在疫苗问世之前，许多国家曾多次采取严厉的社会限制措施，以防止医疗体系饱和，并重新控制 otherwise exponentially increasing COVID-19 pandemic。随着数据共享的出现，计算方法是使用非药物干预措施 (NPIs) 有效控制大流行的关键。在这里，我们开发了一个基于时间离散和年龄分层的 compartmental 模型的数据驱动计算框架，以控制医院内外的大流行演变在不断变化的环境中与 NPIs。除了历元时间外，我们还引入了第二个感染史时间尺度，允许非指数转换概率。我们开发了推理方法和反馈程序，以便随着新数据的出现，逐步重新校准模型参数。作为一个展示，我们校准框架以研究 2021 年 2 月之前法国医院内外的大流行演变。我们将来自政府网站的全国住院统计数据与单个医院的临床数据相结合，以校准住院参数。我们推断出作为 NPIs 函数的社交接触矩阵的变化，从阳性检测和新的住院数据。我们使用模拟来推断隐藏的大流行特性，如感染人口的比例、住院概率或感染死亡率。我们展示了繁殖数和群体免疫水平如何取决于潜在的社会动态。
