Assessing a Fossil Fuels Externality with a New Neural Networks and Image Optimization Algorithm: The Case of Atmospheric Pollutants as Cofounders to COVID-19 Lethality

Risk factors associated with food consumption and food-handling habits for sporadic listeriosis: a case-control study in China from 2013 to 2022

The prevalence of listeriosis in China has been increasing in recent years. Listeriosis primarily spreads through contaminated food. However, the resilient causative organism, Listeria monocytogenes, and its extended incubation period pose challenges in identifying risk factors associated with food consumption and food-handling habits. This study aimed to identify the risk factors associated with food consumption and food-handling habits for listeriosis in China. A matched case-control study (1:1 ratio) was conducted, which enrolled all eligible cases of listeriosis between 1 January 2013 and 31 December 2022 in China. Basic information and possible risk factors associated with food consumption and food-handling habits were collected. Overall, 359 patients were enrolled, including 208 perinatal and 151 non-perinatal cases. Univariate and multivariable logistic analyzes were performed for the perinatal group. For the perinatal and non-perinatal groups, ice cream and Chinese cold dishes were the high-risk foods for listeriosis (odds ratio (OR) 2.09 95% confidence interval (CI): 1.23-3.55; OR 3.17 95% CI: 1.29-7.81), respectively; consumption of leftovers and pet ownership were the high-risk food-handling habits (OR 1.92 95% CI: 1.03-3.59; OR 3.00 95% CI: 1.11-8.11), respectively. In both groups, separation of raw and cooked foods was a protective factor (OR 0.27 95% CI: 0.14-0.51; OR 0.35 95% CI: 0.14-0.89), while refrigerator cleaning reduced the infection risk by 64.94-70.41% only in the perinatal group. The identification of high-risk foods and food-handling habits for listeriosis is important for improving food safety guidelines for vulnerable populations.

The intervention of local public authorities and the impact of the COVID-19 pandemic in Romania: a subnational analysis

The COVID-19 pandemic had a strong territorial dimension, with a highly asymmetric impact among Romanian counties, depending on pre-existing vulnerabilities, regions' economic structure, exposure to global value chains, specialization, and overall ability to shift a large share of employees to remote working. The aim of this paper is to assess the role of Romanian local authorities during this unprecedented global medical emergency by capturing the changes of public spending at the local level between 2010 and 2021 and amid the COVID-19 pandemic, and to identify clusters of Romanian counties that shared similar characteristics in this period, using a panel data quantitative model and hierarchical cluster analysis. Our empirical analysis shows that between 2010-2021, the impact of social assistance expenditures was higher than public investment (capital spending and EU funds) on the GDP per capita at county level. Additionally, based on various macroeconomic and structural indicators (health, labour market performance, economic development, entrepreneurship, and both local public revenues and several types of expenditures), we determined seven clusters of counties. The research contributes to the discussion regarding the increase of economic resilience but also to the evidence-based public policies implementation at local level.

Mapping the long-term associations between air pollutants and COVID-19 risks and the attributable burdens in the continental United States

Numerous studies have investigated the associations between COVID-19 risks and long-term exposure to air pollutants, revealing considerable heterogeneity and even contradictory regional results. Studying the spatial heterogeneity of the associations is essential for developing region-specific and cost-effective air-pollutant-related public health policies for the prevention and control of COVID-19. However, few studies have investigated this issue. Using the USA as an example, we constructed single/two-pollutant conditional autoregressions with random coefficients and random intercepts to map the associations between five air pollutants (PM_2.5, O₃, SO₂, NO₂, and CO) and two COVID-19 outcomes (incidence and mortality) at the state level. The attributed cases and deaths were then mapped at the county level. This study included 3108 counties from 49 states within the continental USA. The county-level air pollutant concentrations from 2017 to 2019 were used as long-term exposures, and the county-level cumulative COVID-19 cases and deaths through May 13, 2022, were used as outcomes. Results showed that considerably heterogeneous associations and attributable COVID-19 burdens were found in the USA. The COVID-19 outcomes in the western and northeastern states appeared to be unaffected by any of the five pollutants. The east of the USA bore the greatest COVID-19 burdens attributable to air pollution because of its high pollutant concentrations and significantly positive associations. PM_2.5 and CO were significantly positively associated with COVID-19 incidence in 49 states on average, whereas NO₂ and SO₂ were significantly positively associated with COVID-19 mortality. The remaining associations between air pollutants and COVID-19 outcomes were not statistically significant. Our study provided implications regarding where a major concern should be placed on a specific air pollutant for COVID-19 control and prevention, as well as where and how to conduct additional individual-based validation research in a cost-effective manner.

Towards a transactional medicine approach to combating global emerging pathogens: the case of COVID-19

When the COVID-19 pandemic struck and China reported the first case to the World Health Organization in December 2019, there was no evidence-based treatment to combat it. With the catastrophic situation that followed, materialised by a considerable number of deaths, researchers, doctors, traditional healers, and governments of all nations committed themselves to find therapeutic solutions, including preventive and curative. There are effective treatments offered both by modern medicine and traditional medicine for COVID-19 today. However, other therapeutic proposals have not been approved due to the lack of effectiveness and scientific rigour during their development process. Proponents of modern medicine prefer biomedical therapies while in some countries, traditional treatments are used regularly because of their availability, affordability and satisfaction they bring to the population. In this paper, we propose a transactional medicine approach where the interaction between traditional and modern medicine produces a change. With this approach, the promoters of traditional medicine and those of modern medicine will be able to acquire knowledge through the experience produced by their encounters. Transactional medicine aims to be a model for decolonising medicine and recognising the value of both traditional and modern medicine in the fight against COVID-19 and other global emerging pathogens.

Air pollution rebound and different recovery modes during the period of easing COVID-19 restrictions

Although COVID-19 lockdown policies have improved air quality in numerous countries, there is a lack of empirical evidence on the extent to which recovery has resulted in air pollution rebound, and the differences and similarities among regions' recovery modes during the period of easing COVID-19 restrictions. Here, we used daily air quality data and the recovery index constructed by a city-pair inflow index for 119 cities in China to quantify the impact of recovery on air pollution from March 2 to October 30, 2020. Findings show that recovery has significantly increased air pollution. When the recovery level increased by 10 %, the concentration of PM_2.5, SO₂, and NO₂ respectively deteriorated by 1.10, 0.33, 1.25 μg/m³, and the average growth rates of three air pollutants were about 3 %-6 %. Moreover, we used the counterfactual framework and time series clustering with wavelet transform to cluster the rebound trajectory of air pollution for 17 provinces into five recovery modes. Results show that COVID-19 has further intensified regional differentiations in economic development ability and green recovery trend. Three northwestern provinces dependent on their resource endowments belong to energy-intensive recovery mode, which have experienced a sharp rebound of air pollution for two months, thereby making green recovery more challenging to achieve. Three regions with a diversified industrial structure are in industrial-restructuring recovery mode, which has effectively returned to a normal level through adjusting industrial structure and technological innovation. Owing to local policies and the outbreak of COVID-19 in other countries, six provinces in policy-oriented and international trade-oriented recovery modes have not fully recovered to the level without COVID-19 until October 2020. The result highlights the importance of diversifying industrial structure, technological innovation, policy flexibility and industrial upgrading for different recovery modes to achieve long-term green recovery in the future.

A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality

The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic threat that is generating socio-economic and health issues in manifold countries. The principal goal of this study is to develop a machine learning experiment to assess the effects of vaccination on the fatality rate of the COVID-19 pandemic. Data from 192 countries are analysed to explain the phenomena under study. This new algorithm selected two targets: the number of deaths and the fatality rate. Results suggest that, based on the respective vaccination plan, the turnout in the participation in the vaccination campaign, and the doses administered, countries under study suddenly have a reduction in the fatality rate of COVID-19 precisely at the point where the cut effect is generated in the neural network. This result is significant for the international scientific community. It would demonstrate the effective impact of the vaccination campaign on the fatality rate of COVID-19, whatever the country considered. In fact, once the vaccination has started (for vaccines that require a booster, we refer to at least the first dose), the antibody response of people seems to prevent the probability of death related to COVID-19. In short, at a certain point, the fatality rate collapses with increasing doses administered. All these results here can help decisions of policymakers to prepare optimal strategies, based on effective vaccination plans, to lessen the negative effects of the COVID-19 pandemic crisis in socioeconomic and health systems.

Mathematical Modeling to Determine the Fifth Wave of COVID-19 in South Africa

The aim of this study is to predict the COVID-19 infection fifth wave in South Africa using the Gaussian mixture model for the available data of the early four waves for March 18, 2020-April 13, 2022. The quantification data is considered, and the time unit is used in days. We give the modeling of COVID-19 in South Africa and predict the future fifth wave in the country. Initially, we use the Gaussian mixture model to characterize the coronavirus infection to fit the early reported cases of four waves and then to predict the future wave. Actual data and the statistical analysis using the Gaussian mixture model are performed which give close agreement with each other, and one can able to predict the future wave. After that, we fit and predict the fifth wave in the country and it is predicted to be started in the last week of May 2022 and end in the last week of September 2022. It is predicted that the peak may occur on the third week of July 2022 with a high number of 19383 cases. The prediction of the fifth wave can be useful for the health authorities in order to prepare themselves for medical setup and other necessary measures. Further, we use the result obtained from the Gaussian mixture model in the new model formulated in terms of differential equations. The differential equations model is simulated for various values of the model parameters in order to determine the disease’s possible eliminations.

Convergence behaviours of energy series and GDP nexus hypothesis: A non-parametric Bayesian application

With the EU Green Deal initiatives, European members seek to launch the first climate neutral continent by 2050. This paper assesses the stochastic convergence of per capita energy consumption series for an illustrative sample of 15 EU countries with memberships prior to the 2004 enlargement, using data spanning the 1970–2018 period. Results from the confidence interval subsampling (asymmetric and equal-tailed) highlight that 11 out of the 15 EU series exhibit a long-run memory behaviour, while a diverging pattern was observed for the UK, Germany, Portugal, and Italy. Finally, per capita energy use series persist but fail to reveal one of the above dynamics for Ireland and Spain. Also, findings from the non-parametric Bayesian application (ANOVA/linear Dependent Dirichlet Process (DDP) mixture model) show how economic growth operates as a converging energy consumption-enabler over the long-run, from which the EU membership cannot be excluded. In particular, we highlight how the nature of energy-GDP hypotheses vary with the stochastic properties of energy use (converging behaviour with temporary shocks, converging pattern with permanent shocks, and diverging dynamic). Finally, our conclusions overcome the well-established development stage argument as we claim that countries with similar energy convergence patterns may need to adopt similar energy policies.

The Impact of China's Paired Assistance Policy on the COVID-19 Crisis-An Empirical Case Study of Hubei Province

To control the coronavirus pandemic (COVID-19), China implemented the Paired Assistance Policy (PAP). Local responders in 16 cities in Hubei Province were paired with expert teams from 19 provinces and municipalities. Fully supported by the country's top-down political system, PAP played a significant role in alleviating the COVID-19 pandemic in Hubei Province and China as a whole. In this study, we examined PAP using a two-way fixed effects model with the cumulative number of medical support personnel and cumulative duration as measurements. The results show personnel and material support played an active role in the nation's response to the COVID-19 public health crisis.

Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy

The Covid19 pandemic has significantly impacted on our lives, triggering a strong reaction resulting in vaccines, more effective diagnoses and therapies, policies to contain the pandemic outbreak, to name but a few. A significant contribution to their success comes from the computer science and information technology communities, both in support to other disciplines and as the primary driver of solutions for, e.g., diagnostics, social distancing, and contact tracing. In this work, we surveyed the Italian computer science and engineering community initiatives against the Covid19 pandemic. The 128 responses thus collected document the response of such a community during the first pandemic wave in Italy (February-May 2020), through several initiatives carried out by both single researchers and research groups able to promptly react to Covid19, even remotely. The data obtained by the survey are here reported, discussed and further investigated by Natural Language Processing techniques, to generate semantic clusters based on embedding representations of the surveyed activity descriptions. The resulting clusters have been then used to extend an existing Covid19 taxonomy with the classification of related research activities in computer science and information technology areas, summarizing this work contribution through a reproducible survey-to-taxonomy methodology.

What does the EKC theory leave behind? A state-of-the-art review and assessment of export diversification-augmented models

Over the past three decades, researchers have extensively examined the environmental Kuznets curve (EKC) hypothesis. Despite their early focus on the ecological impacts of anthropogenic development, associated conclusions differ and often conflict. In this study, we conducted a state-of-the-art review of this topic and shed light on the methodological challenges that the literature attempted to overcome so far. Since China is going through structural economic changes and environmental reforms, we relied on this illustrative case and developed an augmented-EKC framework to investigate whether this hypothesis holds between export product diversification and environmental pollution, stratifying by carbon energy content: renewable (Model 1) and fossil energy (Model 2). Quarterly data are collected over the most available and recent period (i.e., 1990Q1-2018Q4) and computed by applying the Quadratic Match-Sum Method (QMS) on annual series. Besides, per capita income and foreign direct investments are included as additional factors to the baseline models specifications. The empirical analysis comprises the Clemente-Montanes-Reyes unit root test with structural break and additive outlier, the autoregressive distributed lag (ARDL) bounds test for cointegration, the Granger causality test, and dynamic (DOLS) and fully modified OLS (FMOLS) estimators, followed by robustness checks confirming the stability of the coefficients exhibited in the two autoregressive settings. For both models, empirical results failed to support the existence of an inverted-U-shaped relationship among export product diversification and carbon release from fuel combustion in China. Also, as income grows, low-carbon resources seem improving export diversification and vice versa. Related findings are thought to bring robust inferences able to complement the existing literature and open a fruitful research direction.