Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy.
Front Public Health. 2021 Dec 16;9:724362. doi: 10.3389/fpubh.2021.724362. eCollection 2021.
The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as "dynamic causal modeling" (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.
新冠疫情大流行引发了一场激烈的辩论，探讨疫情动态背后的潜在因素。已经提出了几种计算模型来为有效的社会和医疗保健策略提供信息。至关重要的是，这些模型的预测有效性通常取决于对感染的行为和社会反应的纳入。在这些工具中，被称为“动态因果建模”（DCM）的分析框架已被应用于新冠疫情，为疫情动态背后的因素提供了新的视角。我们将 DCM 应用于来自意大利北部地区的数据，这些地区是欧洲首批应对疫情的地区，并分析了模型的预测有效性及其在突出控制疫情扩散的潜在因素方面的适用性。通过考虑疫情初期的数据，该模型可以准确地预测疫情在不同地区的扩散动态。DCM 似乎是一种可靠的工具，可以研究控制 SARS-CoV-2 传播的机制，以确定可以有效地用于对抗进一步感染浪潮的遏制和控制策略。
