论文标题
在意大利的冠状病毒疾病2019（Covid-19）建模和预测时空传播
Modelling and predicting the spatio-temporal spread of Coronavirus disease 2019 (COVID-19) in Italy
论文作者
论文摘要
官方可免费获得的数据有关在最佳水平的空间面积聚集（意大利省）中受感染的数量来对局部水平的COVID-19感染的时空分布进行建模。数据时间范围的范围从20020年2月26日起，这是意大利北部发现的第一个与中国直接相关的日期至2020年3月18日。以前的文献表明，这些类别的模型在时空提供了可靠的传染病预测。三个子组件表征了估计模型。第一个与疾病的演变有关。第二个特征是疾病在同一省的居民中传播。第三个评论是空间邻居的影响，并试图捕获附近地区的传染效应。专注于意大利每日计数的总时间序列，三个子组件中的任何一个的贡献并不占主导地位，我们的预测对整个国家来说都是极好的，与较晚的可用数据相比，每千的错误。相反，在地方一级，有趣的不同模式出现了。特别是，首先通过遏制措施关注的省份是那些不受空间邻居影响的影响。另一方面，对于各省，目前受到传染的强烈影响，占与周围地区空间相互作用的组件很普遍。此外，提出的模型在控制延迟报告的同时，提供了对地方一级感染数量的良好预测。
Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. Data time horizon ranges from 26 February 20020, which is the date when the first case not directly connected with China has been discovered in northern Italy, to 18 March 2020. An endemic-epidemic multivariate time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon. Previous literature has shown that these class of models provide reliable predictions of infectious diseases in time and space. Three subcomponents characterize the estimated model. The first is related to the evolution of the disease over time; the second is characterized by transmission of the illness among inhabitants of the same province; the third remarks the effects of spatial neighbourhood and try to capture the contagion effects of nearby areas. Focusing on the aggregated time-series of the daily counts in Italy, the contribution of any of the three subcomponents do not dominate on the others and our predictions are excellent for the whole country, with an error of 3 per thousand compared to the late available data. At local level, instead, interesting distinct patterns emerge. In particular, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, for the provinces the are currently strongly affected by contagions, the component accounting for the spatial interaction with surrounding areas is prevalent. Moreover, the proposed model provides good forecasts of the number of infections at local level while controlling for delayed reporting.
