Anti-pandemic resilience assessment for countries along the Belt and Road route.

Background: The COVID-19 pandemic is sweeping the world, and countries along the Belt and Road (B&R) route have also been hit hard. However, the impact varied greatly from country to country, some severely and others mildly. What factors have led to such a wide variation? Method: In this paper, we considered institutional, infrastructural, economic, social, and technological resilience as components of overall anti-pandemic resilience, and constructed a set of indicators to evaluate this resilience for B&R countries in 2020. We evaluated the anti-pandemic resilience using the combined empowerment-VIKOR method, and classified the countries into different resilience levels by means of hierarchical clustering. The validity of the evaluation indicator system was verified by analyzing the consistency between the actual performance and the assessed resilience. Results: The ranking results showed that Israel and Bahrain were representative of countries that had the highest resilience, Hungary and Estonia represented countries with moderate resilience, and Laos and Cambodia represented countries with the lowest resilience. We also found that countries with high resilience had much better institutional and economic resilience than countries with moderate resilience, whereas countries with low resilience lagged behind in both infrastructural and social resilience. Based on these findings, policy recommendations were offered to help B&R countries respond to future pandemics.

Diverse spillover effects of COVID-19 control measures on air quality improvement: evidence from typical Chinese cities.

The COVID-19 prevention and control measures are taken by China's government, especially traffic restrictions and production suspension, had spillover effects on air quality improvement. These effects differed among cities, but these differences have not been adequately studied. To provide more knowledge, we studied the air quality index (AQI) and five air pollutants (PM2.5, PM10, SO2, NO2, and O3) before and after the COVID-19 outbreak in Shanghai, Wuhan, and Tangshan. The pollution data from two types of monitoring stations (traffic and non-traffic stations) were separately compared and evaluated. We used monitoring data from the traffic stations to study the emission reduction caused by traffic restrictions. Based on monitoring data from the non-traffic stations, we established a difference-in-difference model to study the emission reduction caused by production suspension. The COVID-19 control measures reduced AQI and the concentrations of all pollutants except O3 (which increased greatly), but the magnitude of the changes differed among the three cities. The control measures improved air quality most in Wuhan, followed by Shanghai and then Tangshan. We investigated the reasons for these differences and found that differences in the characteristics of these three types of cities could explain these differences in spillover effects. Understanding these differences could provide some guidance and support for formulating differentiated air pollution control measures in different cities. For example, whole-process emission reduction technology should be adopted in cities with the concentrated distribution of continuous process enterprises, whereas vehicles that use cleaner energy and public transport should be vigorously promoted in cities with high traffic development level.

A novel assessment framework for improving air quality monitoring network layout.

Redundant stations in the air quality monitoring network (AQMN), not only cause high maintenance and operation costs, but also affect the performance of air quality assessment. This study presents a novel framework for identifying the redundant stations and selecting the corresponding alternatives in AQMN. The framework composes three main steps. Firstly, we identify the redundant stations by correlation analysis and stepwise regression methods. Secondly, we determine the corresponding alternative stations by cluster analysis and correspondence analysis methods. Finally, the final optimization results are verified by the support vector regression. We perform empirical evaluations of the framework using Shanghai's AQMN. The results show that Xuhui, Zhangjiang, Shiwuchang, and Pudong New Area are four redundant pollution monitoring stations. Alternatives for each type of pollutant for these redundant stations are proposed and the adjusted layout of AQMN is verified with historical data. The framework proposed in this study can effectively improve the layout of AQMN, which could be applied to other cities or regions to improve the integrity of pollution information and reduce the monitoring costs.Implications: In this study, we set up a comprehensive framework. A case study proves that the framework we proposed can help countries identify redundant stations, so as to reduce the monitoring costs, improve the monitoring efficiency, and provide technical support for governments to implement accurate air quality control measures.Four particularly important aspects were highlighted in this work: (i) A new framework was constructed that combined regression and prediction for the first time to analyze and validate pollutant data; (ii) The framework used Stepwise Regression to improve previous methods for identifying redundant monitoring stations, effectively improving identification efficiency; (iii) The framework used Support Vector Regression to make predictions to verify the final results of the optimized layout, which was ignored in previous studies. (iv) This framework can be applied to any city or region, which has important practical significance for improving the comprehensiveness and accuracy of pollution monitoring in various cities.

Evaluation of health system resilience in 60 countries based on their responses to COVID-19.

Introduction: In 2020, the COVID-19 epidemic swept the world, and many national health systems faced serious challenges. To improve future public health responses, it's necessary to evaluate the performance of each country's health system. Methods: We developed a resilience evaluation system for national health systems based on their responses to COVID-19 using four resilience dimensions: government governance and prevention, health financing, health service provision, and health workers. We determined the weight of each index by combining the three-scale and entropy-weight methods. Then, based on data from 2020, we used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the health system resilience of 60 countries, and then used hierarchical clustering to classify countries into groups based on their resilience level. Finally, we analyzed the causes of differences among countries in their resilience based on the four resilience dimensions. Results: Switzerland, Japan, Germany, Australia, South Korea, Canada, New Zealand, Finland, the United States, and the United Kingdom had the highest health system resilience in 2020. Eritrea, Nigeria, Libya, Tanzania, Burundi, Mozambique, Republic of the Niger, Benin, Côte d'Ivoire, and Guinea had the lowest resilience. Discussion: Government governance and prevention of COVID-19 will greatly affect a country's success in fighting future epidemics, which will depend on a government's emergency preparedness, stringency (a measure of the number and rigor of the measures taken), and testing capability. Given the lack of vaccines or specific drug treatments during the early stages of the 2020 epidemic, social distancing and wearing masks were the main defenses against COVID-19. Cuts in health financing had direct and difficult to reverse effects on health systems. In terms of health service provision, the number of hospitals and intensive care unit beds played a key role in COVID-19 clinical care. Resilient health systems were able to cope more effectively with the impact of COVID-19, provide stronger protection for citizens, and mitigate the impacts of COVID-19. Our evaluation based on data from 60 countries around the world showed that increasing health system resilience will improve responses to future public health emergencies.

An adaptive heuristic clustering algorithm for influence maximization in complex networks.

Influence maximization research in the real world allows us to better understand, accelerate spreading processes for innovations and products, and effectively analyze, predict, and control the spread of diseases, rumors, and computer viruses. In this paper, we first put forward a new path-based node similarity measure, named the dynamic local similarity index, which can be dynamically adjusted to the optimal mode according to network topology characteristics. Compared to the Katz index with high complexity and an LP index with a limited application range, the proposed index achieves an excellent balance between complexity and precision. Second, combining the extended neighborhood coreness with the minimum distance, a novel strategy is presented for selecting initial centers of clusters, which is helpful for speeding up clustering convergence and avoiding local optimum, especially in non-connected networks. Subsequently, we present an adaptive heuristic clustering algorithm, which can find the seed set with maximum collective influence through clustering. The empirical results on four real datasets show the effectiveness and efficiency of the proposed algorithm, which compares favorably to several state-of-the-art algorithms.