The impact of ethnic segregation on neighbourhood-level social distancing in the United States amid the early outbreak of COVID-19.

The COVID-19 pandemic has been argued to be the 'great equaliser', but, in fact, ethnically and racially segregated communities are bearing a disproportionate burden from the disease. Although more people have been infected and died from the disease among these minority communities, still fewer people in these communities are complying with the suggested public health measures like social distancing. The factors contributing to these ramifications remain a long-lasting debate, in part due to the contested theories between ethnic stratification and ethnic community. To offer empirical evidence to this theoretical debate, we tracked public social-distancing behaviours from mobile phone devices across urban census tracts in the United States and employed a difference-in-difference model to examine the impact of racial/ethnic segregation on these behaviours. Specifically, we focussed on non-Hispanic Black and Hispanic communities at the neighbourhood level from three principal dimensions of ethnic segregation, namely, evenness, exposure, and concentration. Our results suggest that (1) the high ethnic diversity index can decrease social-distancing behaviours and (2) the high dissimilarity between ethnic minorities and non-Hispanic Whites can increase social-distancing behavior; (3) the high interaction index can decrease social-distancing behaviours; and (4) the high concentration of ethnic minorities can increase travel distance and non-home time but decrease work behaviours. The findings of this study shed new light on public health behaviours among minority communities and offer empirical knowledge for policymakers to better inform just and evidence-based public health orders.

Green space justice amid COVID-19: Unequal access to public green space across American neighborhoods.

Countries around the world have resorted to issuing stay-at-home orders to slow viral transmission since the COVID-19 pandemic. During the lockdown, access to public park plays a central role in the public health of surrounding communities. However, we know little about how such an unprecedented policy may exacerbate the preexisting unequal access to green space (i.e., green space justice). To address this research void, we used difference-in-difference models to examine socioeconomic disparities, urban-rural disparities, and mobility disparities in terms of public park access in the United States. Our national analysis using the weekly mobile phone movement data robustly suggests the following three key findings during COVID-19: (1) The elderly, non-college-educated people, poor people, and blacks are less likely to visit public parks frequently, while unemployed people appear to be the opposite. (2) Compared to rural areas, populations in urban neighborhoods appear to visit public parks more frequently and they generally go to larger parks to minimize the risk of infection. (3) Populations in neighborhoods with higher private vehicle ownership or those with a higher density of transit stops would more frequently visit and travel a longer distance to public parks during the stay-at-home order. Our results imply that conventional inequality in green space access may still exist and even become worse during COVID-19, which could negatively impact people's health during isolation. We suggest that special attention should be paid to park-poor neighborhoods during the pandemic and in the post-pandemic recovery phase.

A retrospective study of Pupingqinghua prescription versus Lianhuaqingwen in Chinese participants infected with SARS-CoV-2 Omicron variants.

Background: Coronavirus disease (COVID-19) seriously endangers global public health. Pupingqinghua prescription (PPQH) is an herbal formula from traditional Chinese medicine used for treatment of SARS-CoV-2 infection. This study aims to evaluate the clinical efficacy and safety of PPQH in Chinese participants infected with the SARS-CoV-2 Omicron variant. Methods: A total of 873 SARS-CoV-2 (Omicron)-infected patients were included. Among them, the patients were divided into the PPQH group (653 cases) and LHQW group (220 cases) according to different medications. The effectiveness indicators (hematological indicators, Ct values of novel Coronavirus nucleic acid tests, and viral load-shedding time) and safety indicators (liver and kidney function and adverse events) were analyzed. Results: There was no significant difference in baseline characteristics between the PPQH group and the LHQW group, except the gender; After the treatment, the levels of IL-5, IL-6, IL-10, NK cells, and INF-α of the patients in the PPQH group showed a downward trend (p < 0.05); The viral load shedding time was 5.0 (5.0, 7.0) in the PPQH group and 5.0 (4.0, 7.0) in the LHQW group; both PPQH and LHQW can shorten the duration of symptoms of fever, cough, and sore throat. The re-positive rate of COVID-19 test was 1.5 % in the PPQH group and 2.3 % in the LHQW group. In terms of safety, the levels of γ-GTT decreased significantly (p < 0.01); gastrointestinal reaction was the primary adverse reaction, and the reaction rate was 4.7 % in the PPQH group and 9.5 % in the LHQW group. Conclusion: PPQH can shorten the length of hospital stay and improve clinical symptoms of patients with SARS-COV-2 (Omicron), and it also has a good safety profile.

The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection.

Background: SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility. Methods: This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy. Results: Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities; lower Ct value at recurrence; more persistent cough; and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials. Conclusion: Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

Can the nucleic acid Ct value of discharged patients infected with SARS-CoV-2 Omicron variant be 35?--A retrospective study on fluctuation of nucleic acid Ct values in SNIEC mobile cabin hospital.

Objective: To explore the meaning of cycle threshold (Ct) value fluctuation and the appropriateness of setting the discharge Ct value to 35, which is the current standard in Chinese guidelines. Method: A retrospective study was conducted on 95 patients with Ct value fluctuation (Ct value below 35 on day 3; group A) and 97 patients with a normal discharge process (control; group B). Their clinical characteristics and follow-up data were collected. Results: (1) There was no significant difference between the groups in age, gender distribution, number of vaccinations, initial ORF-Ct value, and initial N-Ct value. The proportion of patients complicated with chronic internal disorders, respiratory symptoms, and abnormal chest radiology in group A was significantly higher than that in group B. (2) Between the two groups, there was no significant difference in the ORF-Ct or N-Ct value on day 1, but the ORF-Ct and N-Ct values of group B on days 2 to 4 were significantly higher than those of group A. (3) There was no significant difference between the groups in the ORF-Ct value at discharge, but there was a significant difference in the N-Ct value at discharge. Seven days after discharge, almost 100% of the patients had been cured. The mean negative conversion interval of nucleic acid of the patients in group A was 14.5 ± 4.6 days, which was longer than that of the patients in group B (11.8 ± 4 days). (4) Logistic regression analysis showed that the ORF-Ct value on day 2 was the key factor influencing the Ct value fluctuation. Conclusion: The fluctuation of Ct value is only a normal phenomenon in the recovery period of the disease, and there is no need for excessive intervention. It is reasonable to set the Ct value of the discharge standard to 35 and retest the nucleic acid on the 10th day after discharge for patients with underlying diseases or symptoms.

The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection

Background SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility. Methods This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy. Results Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities;lower Ct value at recurrence;more persistent cough;and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials. Conclusion Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

A retrospective study of Pupingqinghua prescription versus Lianhuaqingwen in Chinese participants infected with SARS-CoV-2 Omicron variants

Background: Coronavirus disease (COVID-19) seriously endangers global public health. Pupingqinghua prescription (PPQH) is an herbal formula from traditional Chinese medicine used for treatment of SARS-CoV-2 infection. This study aims to evaluate the clinical efficacy and safety of PPQH in Chinese participants infected with the SARS-CoV-2 Omicron variant. Methods: A total of 873 SARS-CoV-2 (Omicron)-infected patients were included. Among them, the patients were divided into the PPQH group (653 cases) and LHQW group (220 cases) according to different medications. The effectiveness indicators (hematological indicators, Ct values of novel Coronavirus nucleic acid tests, and viral load-shedding time) and safety indicators (liver and kidney function and adverse events) were analyzed. Results: There was no significant difference in baseline characteristics between the PPQH group and the LHQW group, except the gender;After the treatment, the levels of IL-5, IL-6, IL-10, NK cells, and INF-α of the patients in the PPQH group showed a downward trend (p < 0.05);The viral load shedding time was 5.0 (5.0, 7.0) in the PPQH group and 5.0 (4.0, 7.0) in the LHQW group;both PPQH and LHQW can shorten the duration of symptoms of fever, cough, and sore throat. The re-positive rate of COVID-19 test was 1.5 % in the PPQH group and 2.3 % in the LHQW group. In terms of safety, the levels of γ-GTT decreased significantly (p < 0.01);gastrointestinal reaction was the primary adverse reaction, and the reaction rate was 4.7 % in the PPQH group and 9.5 % in the LHQW group. Conclusion: PPQH can shorten the length of hospital stay and improve clinical symptoms of patients with SARS-COV-2 (Omicron), and it also has a good safety profile.

Vaccination, symptomatic infection and negative conversion of viral RNA by body mass index, diabetes, and age: An observational study.

BACKGROUND: The associations of doses of vaccine received with symptomatic infection with SARS-CoV-2 and negative conversion rate of viral RNA by BMI, diabetes, and age are unclear. METHODS: Included were adult cases of SARS-CoV-2 infection hospitalized at a makeshift hospital in Shanghai (N = 38,592). Each case received a real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test every day until discharge. Symptomatic cases had ≥1 pre-specified symptoms. Negative conversion time (NCT) was the duration between the specimen collection date associated with the first positive RT-PCR test and the first test date of the two consecutive negative tests at least 24 h apart. BMI-, diabetes- and age-stratified multivariable logistic and Poisson regressions were applied. FINDINGS: Coexistence of overweight/obesity and diabetes was associated with a higher risk of symptomatic infection and a longer NCT compared with coexistence of normal weight and without diabetes, but this association was primarily attributed to underlying comorbidities. Compared with absence of vaccination, booster vaccination, but not full vaccination, was consistently associated with a 42 %-56 % lower odds of symptomatic infection and ∼1.6-1.8 days of shorter NCT across different strata separately for weight and diabetes. Age-stratified analyses found that the effectiveness of booster vaccination did not attenuate with age, except for preventing symptomatic infection among adults with diabetes. INTERPRETATION: BMI and diabetes co-determined their associations with symptomatic infection and NCT. Booster vaccination but not full vaccination was associated a lower risk of symptomatic infection, a shorter NCT or both regardless of BMI, diabetes status and age.

Examining the association between urban green space and viral transmission of COVID-19 during the early outbreak.

Even though exposure to urban green spaces (UGS) has physical and mental health benefits during COVID-19, whether visiting UGS will exacerbate viral transmission and what types of counties would be more impacted remain to be answered. In this research, we adopted mobile phone data to measure the county-level UGS visitation across the United States. We developed a Bayesian model to estimate the effective production number of the pandemic. To consider the spatial dependency, we applied the geographically weighted panel regression to estimate the association between UGS visitation and viral transmission. We found that visitations to UGS may be positively correlated with the viral spread in Florida, Idaho, New Mexico, Texas, New York, Ohio, and Pennsylvania. Especially noteworthy is that the spread of COVID-19 in the majority of counties is not associated with green space visitation. Further, we found that when people visit UGS, there may be a positive association between median age and viral transmission in New Mexico, Colorado, and Missouri; a positive association between concentration of blacks and viral transmission in North Dakota, Minnesota, Wisconsin, Michigan, and Florida; and a positive association between poverty rate and viral transmission in Iowa, Missouri, Colorado, New Mexico, and the Northeast United States.

Two-dimensional material-based virus detection.

Cost-effective, rapid, and accurate virus detection technologies play key roles in reducing viral transmission. Prompt and accurate virus detection enables timely treatment and effective quarantine of virus carrier, and therefore effectively reduces the possibility of large-scale spread. However, conventional virus detection techniques often suffer from slow response, high cost or sophisticated procedures. Recently, two-dimensional (2D) materials have been used as promising sensing platforms for the high-performance detection of a variety of chemical and biological substances. The unique properties of 2D materials, such as large specific area, active surface interaction with biomolecules and facile surface functionalization, provide advantages in developing novel virus detection technologies with fast response and high sensitivity. Furthermore, 2D materials possess versatile and tunable electronic, electrochemical and optical properties, making them ideal platforms to demonstrate conceptual sensing techniques and explore complex sensing mechanisms in next-generation biosensors. In this review, we first briefly summarize the virus detection techniques with an emphasis on the current efforts in fighting again COVID-19. Then, we introduce the preparation methods and properties of 2D materials utilized in biosensors, including graphene, transition metal dichalcogenides (TMDs) and other 2D materials. Furthermore, we discuss the working principles of various virus detection technologies based on emerging 2D materials, such as field-effect transistor-based virus detection, electrochemical virus detection, optical virus detection and other virus detection techniques. Then, we elaborate on the essential works in 2D material-based high-performance virus detection. Finally, our perspective on the challenges and future research direction in this field is discussed.

Two-dimensional material-based virus detection

Cost-effective, rapid, and accurate virus detection technologies play key roles in reducing viral transmission. Prompt and accurate virus detection enables timely treatment and effective quarantine of virus carrier, and therefore effectively reduces the possibility of large-scale spread. However, conventional virus detection techniques often suffer from slow response, high cost or sophisticated procedures. Recently, two-dimensional (2D) materials have been used as promising sensing platforms for the high-performance detection of a variety of chemical and biological substances. The unique properties of 2D materials, such as large specific area, active surface interaction with biomolecules and facile surface functionalization, provide advantages in developing novel virus detection technologies with fast response and high sensitivity. Furthermore, 2D materials possess versatile and tunable electronic, electrochemical and optical properties, making them ideal platforms to demonstrate conceptual sensing techniques and explore complex sensing mechanisms in next-generation biosensors. In this review, we first briefly summarize the virus detection techniques with an emphasis on the current efforts in fighting again COVID-19. Then, we introduce the preparation methods and properties of 2D materials utilized in biosensors, including graphene, transition metal dichalcogenides (TMDs) and other 2D materials. Furthermore, we discuss the working principles of various virus detection technologies based on emerging 2D materials, such as field-effect transistor-based virus detection, electrochemical virus detection, optical virus detection and other virus detection techniques. Then, we elaborate on the essential works in 2D material-based high-performance virus detection. Finally, our perspective on the challenges and future research direction in this field is discussed.

Economic resilience during COVID-19: An insight from permanent business closures

The rapid spread of COVID-19 has damaged the economy in the United States. To understand the resilience capacities of local businesses, we visualized the spatiotemporal patterns of permanently closed Points of Interest (POIs) after the outbreak of the pandemic. The results show that the counties with more business closures are also the counties with a larger population. The permanent closures of most local businesses occurred immediately after the lift of stay-at-home order. We also found that those business owners, who permanently shut down their businesses, may not open new ones elsewhere. In addition, the permanent closures of entertainment and recreation businesses are significantly less than that of retail and trade businesses and food services. [ABSTRACT FROM AUTHOR] Copyright of Environment & Planning A is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Examining the spatial and temporal relationship between social vulnerability and stay-at-home behaviors in New York City during the COVID-19 pandemic.

Social distancing and particularly staying at home are effective public health responses to the COVID-19 pandemic. The sheer scale of behavior changes across a mass population scale is unprecedented and will undoubtedly cause disproportionate hardships for certain vulnerable groups of population and marginalized communities during different periods of the pandemic. However, at the community level, few studies have considered the spatial and temporal variations in such public health behavior changes during this pandemic. We applied a geographically and temporally weighted regression (GTWR) to analyze the spatiotemporal pattern of community stay-at-home behaviors against social vulnerability indicators at the census tract level in New York City from March to August 2020. Our findings are generally supporting the conventional wisdom of social vulnerability yet they also offer new insights. Despite the spatial variations in the effects of social vulnerability on stay-at-home behaviors, people from different vulnerable groups are also exhibiting varying reactions to the pandemic over the duration of this study, thereby highlighting the importance of understanding the spatiotemporal pattern of public health behaviors to develop an effective policy response to avoid the risk of deepening inequalities and to promote a just and sustainable urban future.

Social distancing and inequality in the United States amid COVID-19 outbreak

Amid sweeping efforts to get Americans to stay at home to slow the spread of the coronavirus disease, we geovisualized how foot traffic has increased or declined in relation to six types of trips across the United States: homes, workplaces, retail and recreation establishments, parks, grocery stores and pharmacies, and transit stations. The geovisualization shows that most West and East Coast cities have reduced extensive movements while many Middle American cities even increased their movements, such as trips to grocery stores and parks. We further found that the poorest communities reduced fewer movements than the wealthiest communities, except for the trips to parks.

Where to buy a house in the United States amid COVID-19?

Home prices and rent prices in the USA have been growing steadily over the past decade. However, the COVID-19 pandemic has decimated entire sectors of the American economy, which makes the homebuying decision more intricate. We mapped multiple metrics to indicate the best place to buy a house amid COVID-19. For many counties in the central area of the USA, the price-to-rent ratio highly recommends people to buy a house, but the home prices have declined since the outbreak of COVID-19. The price-to-rent ratio and increasing home price suggest that people should not buy a home in big coastal cities under the current circumstances.