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      Spatio-temporal Object-Oriented Bayesian Network modeling of the Covid-19 Italian outbreak data

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          Abstract

          The spatial epidemic dynamics of Covid-19 outbreak in Italy were modelled by means of an Object-Oriented Bayesian Network in order to explore the dependence relationships, in a static and a dynamic way, among the weekly incidence rate, the intensive care units occupancy rate and that of deaths. Following an autoregressive approach, both spatial and time components have been embedded in the model by means of spatial and time lagged variables. The model could be a valid instrument to support or validate policy makers’ decisions strategies.

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          Most cited references 23

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          Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

           Qun Li,  Xuhua Guan,  Peng Wu (2020)
          Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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            Learning Bayesian networks: The combination of knowledge and statistical data

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              Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems

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                Author and article information

                Journal
                Spat Stat
                Spat Stat
                Spatial Statistics
                Elsevier B.V.
                2211-6753
                14 July 2021
                14 July 2021
                Affiliations
                [a ]Department of Social and Ecnomic Sciences, Sapienza University of Rome, P.za Aldo Moro, 5, 00185 Rome, Italy
                [b ]Department of Political Sciences, LUISS University, Viale Romania, 32, 00197 Rome, Italy
                Author notes
                [* ]Corresponding author.
                Article
                S2211-6753(21)00039-7 100529
                10.1016/j.spasta.2021.100529
                8277433
                © 2021 Elsevier B.V. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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