Spatiotemporal spread characteristics and influencing factors of COVID‐19 cases: Based on big data of population migration in China

Abstract

As a public health emergency, the COVID-19 pandemic has attracted widespread attention from scholars worldwide. Combining social network models, GIS analysis and spatial econometric models, we explored the characteristics of the Wuhan population outflow network and factors affecting the number of COVID-19 cases. The results show that the Wuhan population outflow network has strong temporal and spatial heterogeneity. Cities in Hubei Province, central cities such as Beijing and Shanghai, and cities rich in tourism resources were the main destinations of Wuhan's population inflow. The distribution of COVID-19 cases not only showed a strong spatial autocorrelation but also a hierarchical diffusion effect. The benchmark regression results showed that the population outflow from Wuhan determines the number of COVID-19 cases in other cities. Temperature was negatively correlated with the number of COVID-19 cases, while the PM2.5 concentration failed the significance test.Thus, the lower is the temperature, the greater are the survival and spread of the virus facilitated. Furthermore, cities with a higher population density and more employees in the middle reaches of the Yangtze River are more vulnerable to COVID-19. Finally, by replacing the weight matrix and setting instrumental variables, we proved the robustness of the above main conclusions.

Organizational resilience under COVID-19: the role of digital technology in R&D investment and performance

“…Thus, we consider that organizational learning ability and technical capabilities facilitate firms' integration of digital technology resources and equip a firm with the digital technologies best suited to its current environment. For example, we find that the rich R&D input activities of China Baosteel's Shanghai Baoshan base have given the firm experience in embedding digital technologies (Zhang et al, 2021), which has helped it operate 24 h a day during the epidemic without multiple employees. Based on the above, this research first focuses on the impact of the accumulation of prior R&D investment activities on digital transformation, as well as the strategic flexibility of firms with high digital technologies, which can help firms withstand COVID-19 and show great performance; to date, this has been neglected in the influencing factors discussed in the digital transformation literature.…”

“…Thus, we consider that organizational learning ability and technical capabilities facilitate firms' integration of digital technology resources and equip a firm with the digital technologies best suited to its current environment. For example, we find that the rich R&D input activities of China Baosteel's Shanghai Baoshan base have given the firm experience in embedding digital technologies (Zhang et al. , 2021), which has helped it operate 24 h a day during the epidemic without multiple employees.…”

Understanding the Geography of COVID-19 Case Fatality Rates in China: A Spatial Autoregressive Probit-Log Linear Hurdle Analysis

“…In addition, this study contributes to the existing literature by introducing a hurdle model into the spatial autoregressive model to deal with the zero-inflation problem, thus discovering two different processes influencing COVID-19 related deaths: whether there is a death from COVID-19 in a city and how high the non-zero value of CFR in a city is. There are relatively few studies on the determinants of COVID-19 confirmed cases and deaths in China employing spatial regression models, which focus more on COVID-19 transmission and the influencing factors on the number of COVID-19 cases (52)(53)(54), while this study considers the determinants on CFR with COVID-19 from different perspectives.…”

A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China

“…Understanding the spatiotemporal distribution of a population is vital for the prevention and control of COVID-19 [36][37][38][39]. COVID-19 cases exhibit multiscale characteristics, and the spread of infection shows a spatial association with population density [40,41].…”