12/31/2023 0 Comments Northern italy lockdownThis methodology allows one to model the temporal trajectory of several quantities related to the process under study and to estimate the impact of the underlying latent factors on observable data. The causes themselves are not directly observable (they are “latent”) but are probabilistically inferred by the model so as to explain the observable data in a Bayes-optimal fashion. In detail, DCM involves positing an architecture of coupled causes that interact in generating observable quantities. Whereas, DCM originates in the neuroimaging field to infer the nature of connectivity in brain networks, it is grounded on a generic theoretical–computational framework, which can be applied to a variety of non-linear dynamical systems where different causal sources interact in complex ways. DCM is a flexible statistical procedure, originally designed to infer the nature of connectivity in brain networks. Recently, the application of the dynamic causal modeling (DCM) ( 13) to the COVID-19 pandemic allowed to extract different interpretative perspectives on the factors driving the pandemic through different countries and phases ( 14– 17). The extension of SIR models with network linkages, in which the statistical dependencies between contacts are part of model structure, demonstrates a notable improvement in the estimation of transmission parameters ( 12). Compartmental models such as the SIR-based (susceptible–infected–recovered) or SEIR-based (susceptible–exposed–infectious–recovered) models have been used during the SARS-CoV-2 outbreak ( 6– 12)–which are generally based on differential equations accounting for the rate of transition of an individual between specific states or compartments. Relevant epidemiological data have been accumulated during the pandemic diffusion ( 3– 5), and predictive models have been developed to inform social and healthcare strategies. Nonetheless, northern Italian regions were more affected than others, with variable severity, raising questions that remain so far unanswered: (i) How did the tight lockdown impact the local dynamics of SARS-CoV-2 spread? (ii) Why were there substantial differences across regions despite the similar public health measures adopted? and (iii) Could have been possible to predict somehow the incoming of subsequent waves? Italy was the first country severely hit by the outbreak outside China, particularly in its northern part, and has promptly adopted a tight lockdown strategy and other specific public health measures ( 1, 2). The fight against the virus has in fact benefited from reliable models explaining the wealth of epidemiological data to the aim of informing the healthcare system. The COVID-19 pandemic has engendered a key debate about the generative factors governing its evolution, and a wide series of mathematical models have been developed to predict the evolution of the outbreak. 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. 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. 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. 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. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Several computational models have been proposed to inform effective social and healthcare strategies. The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. 6Brain Connectivity Center, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy.5Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.4Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States.2Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy.1Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.Daniela Gandolfi 1 * † Giuseppe Pagnoni 1,2 * † Tommaso Filippini 1 † Alessia Goffi 3 Marco Vinceti 1,4 Egidio D'Angelo 5,6 Jonathan Mapelli 1,2 *
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