Department of Economics and Management, University of Trento, Via V. Inama 5, Trento, 38122, Italy.
Department of Economics, University of Verona, Via Cantarane 24, Verona, 37129, Italy.
BMC Infect Dis. 2020 Sep 23;20(1):700. doi: 10.1186/s12879-020-05415-7.
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time.
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. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon.
Three subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting.
A strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished.
严重急性呼吸系统综合症冠状病毒 2 型(SARS-CoV-2)于 2019 年底在中国首次被发现,此后在短短几个月内便在全球范围内传播。意大利是最早面临卫生紧急情况的西方国家之一,也是受大流行影响最严重的国家之一。2019 年冠状病毒病(COVID-19)在意大利的传播呈现出一种特殊的空间模式,但科学界迄今为止的注意力几乎完全集中在随时间推移预测疾病的演变上。
使用关于感染人数的官方免费可用数据,并尽可能细化到空间区域聚合(意大利各省)的最高级别,对 COVID-19 感染在当地的时空分布进行建模。实现了一个用于区域疾病计数的地方性流行时间序列混合效应广义线性模型,以了解和预测疾病的时空扩散。
拟合模型有三个子成分。第一个描述了省内疾病的传播;第二个解释了邻近省份之间的传播;第三个与疾病随时间的演变有关。在地方层面上,首先受到遏制措施影响的省份是那些不受空间相邻地区影响的省份。另一方面,与周边地区的空间相互作用有关的成分在受感染严重的省份中占主导地位。此外,该模型在控制延迟报告的情况下,对当地感染人数的预测效果良好。
有强有力的证据表明,一些省份实施的严格控制措施有效地阻断了传染病的传播,并限制了其向周边地区的扩散。虽然遏制政策如果考虑到当地的特点来规划,可能会更有效,但需要在国家层面上有效和均匀地执行控制措施,以防止整个疾病控制被延迟或错失。这也可能适用于国际层面,例如,在像欧盟或美国这样的国家,国家之间的内部边界检查已经在很大程度上被取消。