DistCare: Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis

Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from life-threatening systemic problems and need to be carefully monitored in ICUs. An intelligent prognosis can help physicians take an early intervention, prevent adverse outcomes, and optimize the medical resource allocation, which is urgently needed, especially in this ongoing global pandemic crisis. However, in the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, the rarity of the cases, and privacy concerns. In this paper, we propose a distilled transfer learning framework, DistCare, which leverages the existing publicly available online Electronic Medical Records to enhance the prognosis for inpatients with emerging infectious diseases. It learns to embed the COVID-19-related medical features based on massive existing EMR data. The transferred parameters are further trained to imitate the teacher model's representation based on distillation, which embeds the health status more comprehensively on the source dataset. We conduct Length-of-Stay prediction experiments for patients in ICUs on real-world COVID-19 datasets. The experiment results indicate that our proposed model consistently outperforms competitive baseline methods. In order to further verify the scalability of DistCare to deal with different clinical tasks on different EMR datasets, we conduct an additional mortality prediction experiment on End-Stage Renal Disease datasets. The extensive experiments demonstrate that DistCare can benefit the prognosis for emerging pandemics and other diseases with limited EMR.

[Enlightment of routine vaccination under the prevention and control of COVID-19 based on the circulating event of type â ¢ vaccine-derived poliovirus in Shanghai].

Since the Global Polio Eradication Initiative was launched by the World Health Assembly in 1988, significant progress has been made in global polio prevention and control. But the occurrence of vaccine-associated paralytic poliomyelitis cases and vaccine-derived poliovirus related cases have become a major challenge during the post-polio era. While coronavirus disease 2019(COVID-19) has brought serious disease burden and economic burden to all countries in the world, prevention and control of vaccine-preventable infectious diseases such as polio should not be neglected under the background of the global common fight against COVID-19. Taking the type Ⅲ VDPV cycle event in Shanghai as an example, the paper discussed how to do a good job of routine inoculation under the prevention and control of COVID-19 to strictly prevent the outbreak of vaccine-preventable infectious diseases.

Assessing the Impact of the Digital Economy on Green Total Factor Energy Efficiency in the Post-COVID-19 Era

Digital transformation in the energy sector is an essential tool for promoting the construction of a clean energy system in the post-COVID-19 era. Under the background of digital China strategy and sustainable energy transformation in the post-COVID-19 era, it is meaningful to investigate the relationship between the digital economy and green total factor energy efficiency (GTFEE) to better drive the development of the digital economy and improve GTFEE. For this purpose, this study estimates deeply the impact of the digital economy on GTFEE by applying ordinary least squares (OLS), panel vector autoregression (PVAR), panel threshold, and mediation effect models based on panel data of 30 Chinese provinces from 2006 to 2018. The statistical results indicate that digital economy is conducive to improving GTFEE. Digital economy can significantly contribute to GTFEE by improving economic growth level, urbanization level, R&D investment, and human capital. The most interesting finding was that there is also a non-linear relationship between digital economy and GTFEE. The effect of digital economy on GTFEE is shown to be first promoted and then inhibited as digital economy level continues to increase. Further, the positive impact of the digital economy on GTFEE is strengthened with increasing levels of economic growth, urbanization, R&D input, and human capital. Finally, A positive correlation was found between digital economy and GTFEE in the eastern and central regions, but insignificantly in other regions.

Stay-at-Home Orders during the COVID-19 Pandemic Reduced Urban Water Use

In response to the outbreak of the COVID-19 pandemic, many governments instituted "stay-at-home" orders to prevent the spread of the coronavirus. The resulting changes in work and life routines had the potential to substantially perturb typical patterns of urban water use. We present here an analysis of how these pandemic responses affected California's urban water consumption. Using water demand modeling that fuses an integrated water use database, we first simulated the water use in a business-as-usual (non-pandemic) scenario for essentially all urban areas in California. We then subtracted the business-as-usual water use from the actual use to isolate the changes caused solely by the pandemic response. We found that the pandemic response decreased California's urban water use by 7.9%, which can be largely attributed to an 11.2% decrease in the commercial, industrial, and institutional sector that more than offset a 1.4% increase in the residential sector. The influence of the stay-at-home practices on urban water use is slightly stronger than the combined influences of all non-pandemic factors. This study covers both metropolitans and suburbs;therefore, the results could also be useful for analysis of the impacts of COVID-19 on water use in other urban areas.