Epidemics, Convergence, and Common Prosperity: Evidence from China

This article investigates the impact of previous epidemics on rural development and convergence, and identifies the impact's mechanism based on convergence tests. Using a balanced panel of 31 provinces, the empirical results from 2002 to 2019 show that epidemics decelerated convergence in rural per capita income. The mechanism analysis shows that the accelerated divergence in wages and the decelerated convergence in business income were the major drivers, which also led to decelerated convergence in rural per capita consumption. Although epidemics have not threatened rural food consumption and the Engel coefficient of rural households, these two indicators of basic living needs have failed to achieve convergence across regions. The overall impact of an epidemic on convergence in rural-urban income disparity has also been insignificant, indicating that epidemics have affected rural and urban development simultaneously. Finally, COVID-19 is likely to decelerate convergence in rural income, rural consumption, and urban income.

A graph-based approach towards risk alerting for covid-19 spread

With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic sensing paradigm to collect real-time contacts between both people and places. For example, we can rely on the Bluetooth signals that smartphones can both send out and receive to collect the real-time user contacts data. Based on the contacts data, in this paper, we investigate to propose an efficient approach to calculate the risk level of each person to have COVID-19. It can help pinpoint the people who need to be isolated. (1) We model the real-time contact data between people as a straming graph, which is a constantly growing sequence of edges. (2) We provide a risk alerting model to find the people who came in contact with someone having COVID-19. (3) In addition, we design efficient algorithms to calculate the risk level of each person and update the levels in real time. (4) Extensive experiments verify the effectiveness and efficiency of our approach. © 2020, Springer Nature Switzerland AG.