Accurately estimating human mobility and gauging its relationship with virus transmission is critical for the control of COVID-19 spreading. Using mobile device location data of over 100 million ...monthly active samples, we compute origin–destination travel demand and aggregate mobility inflow at each US county from March 1 to June 9, 2020. Then, we quantify the change of mobility inflow across the nation and statistically model the time-varying relationship between inflow and the infections. We find that external travel to other counties decreased by 35% soon after the nation entered the emergency situation, but recovered rapidly during the partial reopening phase. Moreover, our simultaneous equations analysis highlights the dynamics in a positive relationship between mobility inflow and the number of infections during the COVID-19 onset. This relationship is found to be increasingly stronger in partially reopened regions. Our study provides a quick reference and timely data availability for researchers and decision makers to understand the national mobility trends before and during the pandemic. The modeling results can be used to predict mobility and transmissions risks and integrated with epidemics models to further assess the public health outcomes.
•This paper inferred the impact of COVID-19 on transit ridership.•COVID-19 pandemic led to an average 72.4% drop in ridership for 95% of stations in Chicago.•Ridership declined more in zones with ...more commercial lands.•Ridership declined more in zones with higher percentages of white, educated, and high-income individuals.•Ridership declined less in zones with more “essential” jobs.
The COVID-19 pandemic has led to a globally unprecedented decline in transit ridership. This paper leveraged the 20-years daily transit ridership data in Chicago to infer the impact of COVID-19 on ridership using the Bayesian structural time series model, controlling confounding effects of trend, seasonality, holiday, and weather. A partial least square regression was then employed to examine the relationships between the impact of ridership and various explanatory factors. Results suggested: (1) COVID-19 pandemic exerted significant effects on 95% of transit stations, leading to an average 72.4% drop in ridership. (2) Ridership declined more in regions with more commercial lands and higher percentages of white, educated, and high-income individuals. (3) Regions with more jobs in trade, transportation, and utility sectors presented smaller declines. (4) Regions with more COVID-19 cases/deaths presented smaller declines in transit ridership. Findings provide a timely understanding of the significantly reduced ridership during the pandemic and help transit agencies adjust services across different socioeconomic groups and space to better constrain virus transmission.
Naive Bayesian classification algorithm is widely used in big data analysis and other fields because of its simple and fast algorithm structure. Aiming at the shortcomings of the naive Bayes ...classification algorithm, this paper uses feature weighting and Laplace calibration to improve it, and obtains the improved naive Bayes classification algorithm. Through numerical simulation, it is found that when the sample size is large, the accuracy of the improved naive Bayes classification algorithm is more than 99%, and it is very stable; when the sample attribute is less than 400 and the number of categories is less than 24, the accuracy of the improved naive Bayes classification algorithm is more than 95%. Through empirical research, it is found that the improved naive Bayes classification algorithm can greatly improve the correct rate of discrimination analysis from 49.5 to 92%. Through robustness analysis, the improved naive Bayes classification algorithm has higher accuracy.
Abstract New generation vaccines such as recombinant, antigen purified and DNA vaccines are poorly immunogenic due to the lack of an innate immune stimulus. Therefore, search of new adjuvants for ...these vaccines has become a topic of interesting. In new adjuvant development, saponins are outstanding candidates. Recently, increased attention has been received on plant-derived saponins in search of new adjuvant candidates from traditional Chinese medicinal herbs such as Panax ginseng , Astragalus species, Panax notoginseng, Cochinchina momordica, Glycyrrhiza uralensis and Achyranthes bidentata . Many of the saponins have been found to have adjuvant effects on purified protein antigens. The chemical structures of the saponins are related to their adjuvant activities, and influence the nature of the immune responses. Saponin adjuvants have been reported to stimulate secretion of a broad range of cytokines, suggesting that saponins may act by triggering innate immunity. As these plant-originated adjuvants may promote different branches of the immune system, they have the potential to be used in design of new vaccines so as to induce a desired immune response.
One approach to delaying the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such ...policies remains a challenge owing to the lack of an observed and large-scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a 'floor' phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states' stay-at-home policies have only led to about a 5% reduction in average daily human mobility. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge or another virus outbreak in the future.
Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease (ILD) without an identifiable cause. If not treated after diagnosis, the average life expectancy is 3-5 years. ...Currently approved drugs for the treatment of IPF are Pirfenidone and Nintedanib, as antifibrotic drugs, which can reduce the decline rate of forced vital capacity (FVC) and reduce the risk of acute exacerbation of IPF. However these drugs can not relieve the symptoms associated with IPF, nor improve the overall survival rate of IPF patients. We need to develop new, safe and effective drugs to treat pulmonary fibrosis. Previous studies have shown that cyclic nucleotides participate in the pathway and play an essential role in the process of pulmonary fibrosis. Phosphodiesterase (PDEs) is involved in cyclic nucleotide metabolism, so PDE inhibitors are candidates for pulmonary fibrosis. This paper reviews the research progress of PDE inhibitors related to pulmonary fibrosis, so as to provide ideas for the development of anti-pulmonary fibrosis drugs.
•This study examines factors correlated with booking requests and turnover rates of shared cars.•This study employs a generalized additive mixed model.•Carsharing seems more competitive in a distance ...approximating a bus stop between 1.2 km and 2.4 km.•Carsharing should be optimally discouraged at transit hubs to avoid the oversupply of shared cars.•Carsharing is more effectively served in areas with constraints in accessing metro services.
Carsharing has grown significantly over recent years. Understanding factors related to the usage and turnover rate of shared cars will help promote the growth of carsharing programs. This study sets station-based shared car booking requests and turnover rates as learning objectives, by which generalized additive mixed models are employed to examine various effects. The results are: (1) stations with more parking spaces, longer business hours and fewer nearby stations are likely to receive more booking requests and have a higher turnover rate; (2) an area with a higher population density, a higher percentage of adults, a higher percentage of males, a greater road density, or more mixed land use is associated with more car usage and a higher turnover rate; (3) stations nearby transit hubs, colleges, and shopping centers attract more shared car users; (4) shared cars are often oversupplied at transit hubs; (5) both transit proximity and housing price present high degrees of nonlinearity in relation to shared car usage and turnover rates. Findings provide evidence for optimizing the usage and efficiency of carsharing programs: carsharing companies should identify underserved areas to initiate new businesses; carsharing seems more competitive in a distance to a bus stop between 1.2 km and 2.4 km, and carsharing is more effectively served in areas with constraints in accessing metro services; carsharing should be optimally discouraged at transit hubs to avoid the oversupply of shared cars; local authorities should develop a location-based and geographically differentiated quota in managing carsharing programs.
Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study ...jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across ~4.4 million point-of-interests (POIs) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POIs’ visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents’ responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.
During the outbreak of the COVID-19 pandemic, Non-Pharmaceutical and Pharmaceutical treatments were alternative strategies for governments to intervene. Though many of these intervention methods ...proved to be effective to stop the spread of COVID-19, i.e., lockdown and curfew, they also posed risk to the economy; in such a scenario, an analysis on how to strike a balance becomes urgent. Our research leverages the mobility big data from the University of Maryland COVID-19 Impact Analysis Platform and employs the Generalized Additive Model (GAM), to understand how the social demographic variables, NPTs (Non-Pharmaceutical Treatments) and PTs (Pharmaceutical Treatments) affect the New Death Rate (NDR) at county-level. We also portray the mutual and interactive effects of NPTs and PTs on NDR. Our results show that there exists a specific usage rate of PTs where its marginal effect starts to suppress the NDR growth, and this specific rate can be reduced through implementing the NPTs.
Generation of protective immunity at mucosal surfaces can greatly assist the host defense against pathogens which either cause disease at the mucosal epithelial barriers or enter the host through ...these surfaces. Although mucosal routes of immunization, such as intranasal and oral, are being intensely explored and appear promising for eliciting protective mucosal immunity in mammals, their application in clinical practice has been limited due to technical and safety related challenges. Most of the currently approved human vaccines are administered via systemic (such as intramuscular and subcutaneous) routes. Whereas these routes are acknowledged as being capable to elicit antigen-specific systemic humoral and cell-mediated immune responses, they are generally perceived as incapable of generating IgA responses or protective mucosal immunity. Nevertheless, currently licensed systemic vaccines do provide effective protection against mucosal pathogens such as influenza viruses and Streptococcus pneumoniae. However, whether systemic immunization induces protective mucosal immunity remains a controversial topic. Here we reviewed the current literature and discussed the potential of systemic routes of immunization for the induction of mucosal immunity.
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