Change of Residents' Attitudes and Behaviors toward Urban Green Space Pre- and Post- COVID-19 Pandemic

The COVID-19 pandemic has changed and influenced people's attitudes and behaviors toward visiting green spaces. This paper aims to explore the association between residents' health and urban green spaces (UGS) through an in-depth study of changes in residents' use of UGS under the influence of the COVID-19 pandemic. The Wuhan East Lake Greenway Park was selected as the location for the field survey and in-depth interviews. At the same time, an online survey was also conducted (total number = 302) regarding participants' physical and mental health and their attitude and behavior toward the UGS. A paired sample t-test and binary logistic regression were performed to investigate the association between participants' health and UGS during COVID-19. The results show that: (1) the COVID-19 pandemic has primarily changed the leisure patterns of parks, with potential impacts on the physical and mental health of participants;(2) the purpose, frequency, timing, and preferred areas of participants' park visits have changed to varying degrees after the pandemic, highlighting the important role and benefits of UGSs;(3) the physical and mental health of participants and urban development issues reflected by UGS use are prominent. This study reveals that awareness of the construction and protection of UGSs is an important prerequisite for ensuring the health of urban residents.

Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data.

The outbreak of the COVID-19 has become a worldwide public health challenge for contemporary cities during the background of globalization and planetary urbanization. However, spatial factors affecting the transmission of the disease in urban spaces remain unclear. Based on geotagged COVID-19 cases from social media data in the early stage of the pandemic, this study explored the correlation between different infectious outcomes of COVID-19 transmission and various factors of the urban environment in the main urban area of Wuhan, utilizing the multiple regression model. The result shows that most spatial factors were strongly correlated to case aggregation areas of COVID-19 in terms of population density, human mobility and environmental quality, which provides urban planners and administrators valuable insights for building healthy and safe cities in an uncertain future.

The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States

(1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating epidemic policies. (2) Methods: We utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (excluding the District of Colombia) with daily new cases at the county level from 22 January 2020 to 20 August 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation test and stepwise OLS regression with socioeconomic factors. (3) Results: The K-means clustering divided the time-varying spatial autocorrelation curves of the 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with the variables of median age, population density, and proportions of international immigrants and highly educated population, but negatively correlated with the birth rate. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and highly educated population proportion. (4) Conclusions: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population;high-density populated states need to strengthen regional mobility restrictions;and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model.

BACKGROUND: Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. METHODS: Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. RESULTS: Based on the US county-level COVID-19 data from 22 January (T1) to 20 August (T212) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T212. INTERPRETATION: Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.

Antibody Detection and Dynamic Characteristics in Patients With Coronavirus Disease 2019.

BACKGROUND: The coronavirus disease 2019 (COVID-19), caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been rapidly spreading nationwide and abroad. A serologic test to identify antibody dynamics and response to SARS-CoV-2 was developed. METHODS: The antibodies against SARS-CoV-2 were detected by an enzyme-linked immunosorbent assay based on the recombinant nucleocapsid protein of SARS-CoV-2 in patients with confirmed or suspected COVID-19 at 3-40 days after symptom onset. The gold standard for COVID-19 diagnosis was nucleic acid testing for SARS-CoV-2 by real-time reverse-transcription polymerase chain reaction (rRT-PCR). The serodiagnostic power of the specific immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies against SARS-CoV-2 was investigated in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and consistency rate. RESULTS: The seroconversion of specific IgM and IgG antibodies were observed as early as the fourth day after symptom onset. In the patients with confirmed COVID-19, sensitivity, specificity, PPV, NPV, and consistency rate of IgM were 77.3% (51/66), 100%, 100%, 80.0%, and 88.1%, respectively, and those of IgG were 83.3% (55/66), 95.0%, 94.8%, 83.8%, and 88.9%. In patients with suspected COVID-19, sensitivity, specificity, PPV, NPV, and consistency rate of IgM were 87.5% (21/24), 100%, 100%, 95.2%, and 96.4%, respectively, and those of IgG were 70.8% (17/24), 96.6%, 85.0%, 89.1%, and 88.1%. Both antibodies performed well in serodiagnosis for COVID-19 and rely on great specificity. CONCLUSIONS: The antibodies against SARS-CoV-2 can be detected in the middle and later stages of the illness. Antibody detection may play an important role in the diagnosis of COVID-19 as a complementary approach to viral nucleic acid assays.

Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data

<p>During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization.</p>