MobCovid: Confirmed Cases Dynamics Driven Time Series Prediction of Crowd in Urban Hotspot.

Monitoring the crowd in urban hot spot has been an important research topic in the field of urban management and has high social impact. It can allow more flexible allocation of public resources such as public transportation schedule adjustment and arrangement of police force. After 2020, because of the epidemic of COVID-19 virus, the public mobility pattern is deeply affected by the situation of epidemic as the physical close contact is the dominant way of infection. In this study, we propose a confirmed case-driven time-series prediction of crowd in urban hot spot named MobCovid. The model is a deviation of Informer, a popular time-serial prediction model proposed in 2021. The model takes both the number of nighttime staying people in downtown and confirmed cases of COVID-19 as input and predicts both the targets. In the current period of COVID, many areas and countries have relaxed the lockdown measures on public mobility. The outdoor travel of public is based on individual decision. Report of large amount of confirmed cases would restrict the public visitation of crowded downtown. But, still, government would publish some policies to try to intervene in the public mobility and control the spread of virus. For example, in Japan, there are no compulsory measures to force people to stay at home, but measures to persuade people to stay away from downtown area. Therefore, we also merge the encoding of policies on measures of mobility restriction made by government in the model to improve the precision. We use historical data of nighttime staying people in crowded downtown and confirmed cases of Tokyo and Osaka area as study case. Multiple times of comparison with other baselines including the original Informer model prove the effectiveness of our proposed method. We believe our work can make contribution to the current knowledge on forecasting the number of crowd in urban downtown during the Covid epidemic.

Microbiology and immune mechanisms associated with male infertility.

Up to 50% of infertility is caused by the male side. Varicocele, orchitis, prostatitis, oligospermia, asthenospermia, and azoospermia are common causes of impaired male reproductive function and male infertility. In recent years, more and more studies have shown that microorganisms play an increasingly important role in the occurrence of these diseases. This review will discuss the microbiological changes associated with male infertility from the perspective of etiology, and how microorganisms affect the normal function of the male reproductive system through immune mechanisms. Linking male infertility with microbiome and immunomics can help us recognize the immune response under different disease states, providing more targeted immune target therapy for these diseases, and even the possibility of combined immunotherapy and microbial therapy for male infertility.

Cost-effectiveness analysis of durvalumab as a maintenance treatment for patients with locally advanced, unresectable, stage â ¢ nsclc in china.

OBJECTIVE: The aim of this study was to evaluate the cost-effectiveness of durvalumab compared with Best supportive care (BSC) after chemoradiotherapy in patients with stage III non-small cell lung cancer from healthcare system perspective in China. METHODS: A dynamic state transition model was adopted to simulate life time, direct medical costs and QALYs. In the base case scenario, for patients with unresectable, stage Ⅲ non-small cell lung cancer whose disease has not progressed after platinum-based chemoradiation therapy, the treatment group would use durvalumab whereas the control group would use BSC. Clinical data and health utility were derived from the patient-level data of Asian ethnicity in the PACIFIC trial. Cost of drug acquisition, follow-up, medical service, inspection, terminal care and adverse event treatment were considered in this model. The cost of durvalumab was calculated based on retail prices and Patient Assistance Program. RESULTS: In the base case, the durvalumab group yielded an additional 2.60 LYs and 2.37QALYs (discounted), causing an additional cost of 0.459 million RMB and 0.109 million RMB without and with PAP, so the ICER was 193,898 RMB/QALY and 46,093.12 RMB/QALY respectively. CONCLUSIONS: This study demonstrated that durvalumab can improve the survival of patients with unresectable, stage Ⅲ non-small cell lung cancer whose disease has not progressed after platinum-based chemoradiation therapy and would be a cost-effective option compared with BSC at a willingness to pay (WTP) threshold of 212676 RMB (three times GDP per capita of China in 2019).

Asymmetric effects of non-ferrous metal price shocks on clean energy stocks: Evidence from a quantile-on-quantile method

Combining advanced quantile-on-quantile (QQ) regression and causality-in-quantiles (QC) methods, we examine the asymmetric effects of non-ferrous metal price shocks on clean energy stocks at aggregate and sub-sector levels. From the aggregate perspective, the impact of non-ferrous metal price shocks is strongly negative for bull clean energy stock markets but is positive under bear circumstances. According to QC analysis, non-ferrous metal price shocks can effectively predict returns on clean energy stocks in some quantiles. Sub-sectors of clean energy stocks react differently, proving the heterogeneity of different industries. Synergistic movements between non-ferrous metal price shocks and some clean energy sectors in bear markets are detected, indicating that non-ferrous metals are not safe havens for clean energy stock markets under extreme market conditions. Furthermore, non-ferrous metals have a significantly stronger negative impact on clean energy stocks during the epidemic, demonstrating the structural changes effect of COVID-19.

Dynamics of the sheltering role of Bitcoin against crude oil market crash with varying severity of the COVID-19: A comparison with gold.

This paper studies evolution of the asymmetric sheltering role of Bitcoin compared to gold against oil-related uncertainties with varying severity of the COVID-19 pandemic. Using a varying-coefficient quantile approach, we find a safe haven role of Bitcoin, and it becomes gradually stronger when the pandemic intensifies. The relationship between gold and oil markets is shown to vary with changing severity of the pandemic. We find that gold acts as an increasingly weakened diversifier as the pandemic intensifies until a level, above which its diversification gains would dissipate then. In normal market conditions, both Bitcoin and gold perform as weak hedges for oil portfolios. Our findings demonstrate that interpretation of the sheltering role of Bitcoin and gold against oil market downturns would be biased unless the role dynamics in different market conditions and pandemic severity are considered. Additional analyses reassure robustness of our findings.

Epidemic versus economic performances of the COVID-19 lockdown: A big data driven analysis.

Lockdown measures have been a "panacea" for pandemic control but also a violent "poison" for economies. Lockdown policies strongly restrict human mobility but mobility reduce does harm to economics. Governments meet a thorny problem in balancing the pros and cons of lockdown policies, but lack comprehensive and quantified guides. Based on millions of financial transaction records, and billions of mobility data, we tracked spatio-temporal business networks and human daily mobility, then proposed a high-resolution two-sided framework to assess the epidemiological performance and economic damage of different lockdown policies. We found that the pandemic duration under the strictest lockdown is less about two months than that under the lightest lockdown, which makes the strictest lockdown characterize both epidemiologically and economically efficient. Moreover, based on the two-sided model, we explored the spatial lockdown strategy. We argue that cutting off intercity commuting is significant in both epidemiological and economical aspects, and finally helped governments figure out the Pareto optimal solution set of lockdown strategy.

Dynamic and frequency-domain risk spillovers among oil, gold, and foreign exchange markets: Evidence from implied volatility

Utilising the time-frequency spillover framework constructed by Baruník and Křehlík (2018), this study explores the time-frequency risk spillovers among the oil, gold and foreign exchange (FX) markets using implied volatility indices. The results indicate that total spillovers are higher in the short term than in the long term. The impact of FX markets on oil and gold markets is greater than the reverse impact, and the FX markets of advanced economies are the main drivers. The results for the spillover network show that the short-term risk spillover effects are stronger during the European debt crisis and the COVID-19 pandemic. The euro, Australian dollar, and Canadian dollar are dominant risk transmitters during the crisis, and gold and oil are net risk receivers with high short-term vulnerability. Our results have significant implications for risk managers and portfolio managers at different investment horizons.

Dynamic spillovers between energy and stock markets and their implications in the context of COVID-19.

This study combined time-varying parameter vector autoregression (TVP-VAR) and a spillover index model to analyze the static, total, and net spillover effects of energy and stock markets before and after the COVID-19 outbreak. A network method was also used to depict structural changes more intuitively. Furthermore, we calculated and compared changes in the hedge ratio, optimal portfolio weights, and hedge effectiveness to guide investors to adjust portfolio strategies during COVID-19. The main findings were as follows: First, COVID-19 had a significant impact on spillover effects, and the average value of total spillover index increased by 19.94% compared with that before the epidemic. Second, the energy market was an important risk recipient of the stock market before COVID-19, and the extent of risk acceptance increased after the COVID-19 outbreak. Third, the hedging ratio, optimal portfolio weights, and hedge effectiveness showed huge changes after the COVID-19 outbreak, requiring investors to adjust their portfolio strategies.

Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis.

The COVID-19 pandemic spreads rapidly around the world, and has given rise to huge impacts on all aspects of human society. This study utilizes big data techniques to analyze the impacts of COVID-19 on the user behaviors and environmental benefits of bike sharing. In this study, a novel method is proposed to calculate the trip distances and trajectories via a python package OSMnx so as to accurately estimate the environmental benefits of bike sharing. In addition, we employ the topological indices arising from complex network theory to quantitatively analyze the transformation of user behavior pattern of bike sharing during the COVID-19 pandemic. The results show that this pandemic has impacted the user behaviors and environmental benefits of bike sharing in Beijing significantly. During the pandemic, the estimated reductions of energy consumption and emissions on 6th Feb decreased to approximately 1 in 17 of those on a normal day, and the environmental benefits at most recovered to 70% of those in normal days. The impacts of COVID-19 on the environmental benefits in different districts are different. Furthermore, the decline of average strength and strength distribution obeying exponential distribution but with different slope rates suggests that people are less likely to take bike sharing to the places where were popular before. The pandemic has also increased the average trip time of bike sharing. Our research may facilitate the understanding of the impacts of COVID-19 pandemic on our society and environment, and also provide clues to adapt to this unprecedented pandemic so as to respond to similar events in the future.

1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown.

Solar PV has seen a spectacular market development in recent years and has become a cost competitive source of electricity in many parts of the world. Yet, prospective observations show that the coronavirus pandemic could impact renewable energy projects, especially in the distributed market. Tracking and attributing the economic footprint of COVID-19 lockdowns in the photovoltaic sector poses a significant research challenge. Based on millions of financial transaction records and 44 thousand photovoltaic installation records, we tracked the spatio-temporal sale network of the distributed photovoltaic market and explored the extent of market slowdown. We found that a two-month lockdown duration can be assessed as a high-risk threshold value. When the lockdown duration exceeds the threshold value, the monthly value-added loss reaches 67.7%, and emission reduction capacity is cut by 64.2% over the whole year. We show that risks of a slowdown in PV deployment due to COVID-19 lockdowns can be mitigated by comprehensive incentive strategies for the distributed PV market amid market uncertainties.