Psychological Impact and Compliance with Staying at Home of the Public to COVID-19 Outbreak during Chinese Spring Festival

In December 2019, COVID-19 was reported in Wuhan, China. Most of the studies related to the psychological impact and compliance with staying at home due to COVID-19 focused on ten days or one month after the initial “stay-at-home” phase of the COVID-19 pandemic. The early psychological impact and behavior change to COVID-19 during the Chinese Spring Festival (the start time for recommendations to stay at home) is uncertain. In this study, people from 23 provinces in China were recruited to participate in an online survey, using Credamo. Psychological impact and compliance with staying at home were evaluated by a self-designed and validated questionnaire. The results indicated that anxiety was the most often reported feeling (mean: 3.69), followed by sadness (mean: 3.63). Participants employed in foreign-owned companies were most likely to express anxiety and sadness. Overall, 61.8% of participants reported hardly going out, whereas 2.4% said they frequently went out during the initial “stay-at-home” phase of the COVID-19 pandemic. Participants with higher levels of anxiety and sadness were most likely to stay at home against the spread of COVID-19, as were female gender. This survey is an important study of the first reaction to staying at home during the initial “stay-at-home” phase coinciding with Chinese Spring Festival. Our findings identified factors associated with higher level of psychological impact and better compliance with staying at home recommendations during Chinese Spring Festival. The findings can be used to formulate precaution interventions to improve the mental health of vulnerable groups and high uptake of policy during the COVID-19 epidemic.

Quantifying behavior change during the first year of the COVID-19 pandemic in the United States

Background: During the first year of the COVID-19 pandemic, the most effective way to reduce transmission and to protect oneself was to reduce contact with others. However, it is unclear how behavior changed, despite numerous surveys about peoples' attitudes and actions during the pandemic and public health efforts to influence behavior. Methods: We used two sources of data to quantify changes in behavior at the county level during the first year of the pandemic in the United States: aggregated mobile device (smartphone) location data to approximate the fraction of people staying at home each day and digital invitation data to capture the number and size of social gatherings. Results: Between mid-March to early April 2020, the number of events fell and the fraction of devices staying at home peaked, independently of when states issued emergency orders or stay-at-home recommendations. Activity began to recover in May or June, with later rebounds in counties that suffered an early spring wave of reported COVID-19 cases. Counties with high incidence in the summer had more events, higher mobility, and less stringent state-level COVID-related restrictions the month before than counties with low incidence. Counties with high incidence in early fall stayed at home less and had less stringent state-level COVID-related restrictions in October, when cases began to rise in some parts of the US. During the early months of the pandemic, the number of events was inversely correlated with the fraction of devices staying at home, but after the fall of 2020 mobility appeared to stay constant as the number of events fell. Greater changes in behavior were observed in counties where a larger fraction voted for Biden in the 2020 US Presidential election. The number of people invited per event dropped gradually throughout the first year of the pandemic. Conclusions: The mobility and events datasets uncovered different kinds of behavioral responses to the pandemic. Our results indicate that people did in fact change their behavior in ways that likely reduced COVID exposure and transmission, though the degree of change appeared to be affected by political views. Though the mobility data captured the initial massive behavior changes in the first months of the pandemic, the digital invitation data, presented here for the first time, continued to show large changes in behavior later in the first year of the pandemic.

Examining Social Vulnerability and the Association With COVID-19 Incidence in Harris County, Texas

Studies have investigated the association between social vulnerability and SARS-CoV-2 incidence. However, few studies have examined small geographic units such as census tracts, examined geographic regions with large numbers of Hispanic and Black populations, controlled for testing rates, and incorporated stay-at-home measures into their analyses. Understanding the relationship between social vulnerability and SARS-CoV-2 incidence is critical to understanding the interplay between social determinants and implementing risk mitigation guidelines to curtail the spread of infectious diseases. The objective of this study was to examine the relationship between CDC's Social Vulnerability Index (SVI) and SARS-CoV-2 incidence while controlling for testing rates and the proportion of those who stayed completely at home among 783 Harris County, Texas census tracts. SARS-CoV-2 incidence data were collected between May 15 and October 1, 2020. The SVI and its themes were the primary exposures. Median percent time at home was used as a covariate to measure the effect of staying at home on the association between social vulnerability and SARS-CoV-2 incidence. Data were analyzed using Kruskal Wallis and negative binomial regressions (NBR) controlling for testing rates and staying at home. Results showed that a unit increase in the SVI score and the SVI themes were associated with significant increases in SARS-CoV-2 incidence. The incidence risk ratio (IRR) was 1.090 (95% CI, 1.082, 1.098) for the overall SVI; 1.107 (95% CI, 1.098, 1.115) for minority status/language; 1.090 (95% CI, 1.083, 1.098) for socioeconomic; 1.060 (95% CI, 1.050, 1.071) for household composition/disability, and 1.057 (95% CI, 1.047, 1.066) for housing type/transportation. When controlling for stay-at-home, the association between SVI themes and SARS-CoV-2 incidence remained significant. In the NBR model that included all four SVI themes, only the socioeconomic and minority status/language themes remained significantly associated with SARS-CoV-2 incidence. Community-level infections were not explained by a communities' inability to stay at home. These findings suggest that community-level social vulnerability, such as socioeconomic status, language barriers, use of public transportation, and housing density may play a role in the risk of SARS-CoV-2 infection regardless of the ability of some communities to stay at home because of the need to work or other reasons.

`One Man´s Gain Is Another Man´s Loss´? Interim Report on the Consequences of Emigration for Those Staying at Home in the 18th Century

Research on historical migration has so far focused on the impact of immigration on recipient areas. Although several researchers have already pointed out this bias, no studies have been conducted on the impact of emigration from the early German Empire on the affected areas. In this study, the southern German territories affected by emigration to Hungary in the 18th century are examined. Through some examples, the paper seeks to assess potential source groups and provide a preliminary picture of impacts. In conclusion, further research needs to be carried out through intensive resource exploration, covering individuals, smaller communities along with distinct provinces and regions.

The Effect of COVID-19 on Hospitalized Cancer Patients and Their Healthcare

Introduction: This article includes the effect of Corona virus disease on cancer patients and their healthcare facilities. The global pandemic mentioned around the world has impacted the most vulnerable group of patients- cancer. With the assistance of RT PCR tests and HRCT, oncologists and doctors have tried to provide treatment to Covid-19 patients. Cancer patients are more susceptible to Covid-19 than non-cancer or non-survivor patients, according to reports. To avoid the prevalence and infection of cancer victims, WHO has suggested staying at home and continuing their treatment through telemedicine unless the situation is critical for which they might require therapy and/or surgery. Covid-19 is here to stay so we should practice with utmost care and precaution.

Chicago Older Adults’ Likelihood of Being Home Before and During the COVID-19 Pandemic

Staying at home has particularly been emphasized for older adults during the COVID-19 pandemic, given their elevated risk of infection and complications. However, little is known about the extent to which this population is indeed spending more time at home during the pandemic, compared to before it began. The present investigation addresses this question, also examining differences by gender and race/ethnicity. We analyzed ecological momentary assessments among 98 older adults (age 65-88 in 2020) who participated in two waves of the Chicago Health and Activity Space in Real Time study. Pre-pandemic data were collected from July-October 2019, and pandemic data were collected from June-September 2020. Participants responded to smartphone “pings” (five per day for 7 days in each wave; n=1,910 and n=2,437 before and during the pandemic, respectively) by reporting their momentary location (e.g., home). Findings suggest that respondents were indeed at home more often in mid-2020 than 1 year prior. Multilevel logistic regression models revealed that net of demographics, marital and employment status, and physical health, respondents were more likely to be momentarily at home during versus before the pandemic (B=0.70, SE=0.08, p<.001). This effect was larger among women than men (B=0.50, SE=0.16, p=.002), but did not differ by race/ethnicity. Additional analyses examine whether and how the observed increased reports of being at home may be associated with increased reports of momentary loneliness across the two waves. Findings characterize where Chicago older adults are spending their time amid the pandemic and how this may relate to their well-being.

A decrease in reports on road-killed animals based on citizen science during COVID-19 lockdown

Background To avoid the uncontrolled spread of COVID-19 in early 2020, many countries have implemented strict lockdown measures for several weeks. In Austria, the lockdown in early spring has led to a significant drop in human outdoor activities, especially in road traffic. In Project Roadkill, a citizen science project which aims to collect data on road-killed animals, we observed a significant decrease in reported roadkills. Methods By asking the citizen scientists through a survey how their travelling routines were affected, we investigated if the observed decrease in roadkills was grounded in less animals being killed by traffic, or in citizen scientists staying at home and thus reporting less road-killed animals. Results A majority of the respondents stated that they felt to have reported less roadkills during the lockdown, regardless if they changed their travelling routine or not. This observation in combination with the overall decrease in road traffic indicates that fewer animals were killed during the lockdown. We conclude that when analyzing citizen science data, the effects of lockdown measures on reporting behaviour should be considered, because they can significantly affect data and interpretation of these data.

Effectiveness of the COVID-19 Contact-Confirming Application (COCOA) Based on Multi-Agent Simulation

As of Aug. 2020, coronavirus disease 2019 (COVID-19) is still spreading in the world. In Japan, the Ministry of Health, Labour and Welfare developed “COVID-19 Contact-Confirming Application (COCOA),” which was released on June 19, 2020. By utilizing COCOA, users can know whether or not they had contact with infected persons. If those who had contact with infected individuals keep staying at home, they may not infect those outside. However, effectiveness decreasing the number of infected individuals depending on the app’s various usage parameters is not clear. If it is clear, we could set the objective value of the app’s usage parameters (e.g., the usage rate of the total populations) and call for installation of the app. Therefore, we develop a multi-agent simulator that can express COVID-19 spreading and usage of the apps, such as COCOA. In this study, we describe the simulator and the effectiveness of the app in various scenarios. The result obtained in this study supports those of previously conducted studies.

Using Mobile Device Data to Understand the Effect of Stay-at-Home Orders on Residents’ Mobility

Problem definition: This study addresses three important questions concerning the effectiveness of stay-at-home orders and sociodemographic disparities. (1) What is the average effect of the orders on the percentage of residents staying at home? (2) Is the effect heterogeneous across counties with different percentages of vulnerable populations (defined as those without health insurance or who did not attend high school)? (3) If so, why are the orders less effective for some counties than for others? Academic/practical relevance: To combat the spread of coronavirus disease 2019 (COVID-19), a number of states in the United States implemented stay-at-home orders that prevent residents from leaving their homes except for essential trips. These orders have drawn heavy criticism from the public because whether they are necessary and effective in increasing the number of residents staying at home is unclear. Methodology: We estimate the average effect of the orders using a difference-in-differences model, where the control group is the counties that did not implement the orders and the treatment group is the counties that did implement the orders during our study period. We estimate the heterogeneous effects of the orders by interacting county features with treatment dummies in a triple-difference model. Results: Using a unique set of mobile device data that track residents’ mobility, we find that, although some residents already voluntarily stayed at home before the implementation of any order, the stay-at-home orders increased the number of residents staying at home by 2.832 percentage points (or 11.25%). We also find that these orders are less effective for counties with higher percentages of uninsured or less educated (i.e., did not attend high school) residents. To explore the mechanisms behind these results, we analyze the effect of the orders on the average number of work and nonwork trips per person. We find that the orders reduce the number of work trips by 0.053 (or 7.87%) and nonwork trips by 0.183 (or 6.50%). The percentage of uninsured or less educated residents in a county negatively correlates with the reduction in the number of work trips but does not correlate with the reduction in the number of nonwork trips. Managerial implications: Our results suggest that uninsured and less educated residents are less likely to follow the orders because their jobs prevent them from working from home. Policy makers must take into account the differences in residents’ socioeconomic status when developing new policies or allocating limited healthcare resources.