Public Health & Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Australia; Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia.
Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia.
Travel Med Infect Dis. 2021 Mar-Apr;40:101988. doi: 10.1016/j.tmaid.2021.101988. Epub 2021 Feb 9.
The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide.
Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to examine the association and spatial autocorrelation between the number of COVID-19 cases and travel influx (and arrival time) from the source country.
We found significant negative association between disease arrival time and number of cases imported from Italy (r = -0.43, p = 0.004) and significant positive association between the number of COVID-19 cases and daily travel influx from Italy (r = 0.39, p = 0.011). Using bivariate Moran's Index analysis, we found evidence of spatial interaction between COVID-19 cases and travel influx (Moran's I = 0.340). Asia-Pacific region is at higher/extreme risk of disease importation from the Chinese epicentre, whereas the rest of Europe, South-America and Africa are more at risk from the Italian epicentre.
We showed that as the epicentre changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities.
最初在中国武汉市发现的严重急性呼吸系统综合症冠状病毒 2 型（SARS-CoV-2）的爆发现已蔓延到每个有人居住的大陆，但现在人们的注意力已从中国转移到其他中心。本研究探讨了早期评估意大利的空间接近度和旅行模式对 SARS-CoV-2 在全球进一步传播的影响。
我们使用有关 COVID-19 确诊病例数和国家间航空旅行数据，应用随机元种群模型来估计 COVID-19 的全球传播。我们使用皮尔逊相关系数、半变异函数和 Moran 指数来检验 COVID-19 病例数与来自源国的旅行输入（和到达时间）之间的关联和空间自相关。
我们发现，从意大利输入的疾病到达时间与输入病例数之间存在显著的负相关（r = -0.43，p = 0.004），而 COVID-19 病例数与来自意大利的每日旅行输入之间存在显著的正相关（r = 0.39，p = 0.011）。使用二元 Moran 指数分析，我们发现 COVID-19 病例与旅行输入之间存在空间相互作用的证据（Moran's I = 0.340）。亚太地区从中国中心输入疾病的风险更高/极高，而欧洲其他地区、南美洲和非洲则更容易受到意大利中心的影响。
我们表明，随着中心的变化，SARS-CoV-2 传播的动态变化反映了空间接近度。
