JUE Insight: College student travel contributed to local COVID-19 spread

Developing counter-narratives to address COVID-19 misinformation among 18-24 year olds in community and campus settings

During the COVID-19 pandemic, young adults represented one of the groups with the highest number of COVID-19 infections but experienced less severe symptoms and lower rates of mortality. The Georgia Peers for Equity Against COVID-19 and for Health (Georgia PEACH) was developed to address COVID-19 vaccine hesitancy among African American/Black and Latinx young adults (18-25) on college campuses and in the community. Existing community relationships and partnerships were leveraged to quickly engage young adults (Peer Champions) to develop counter-narratives to address mis/disinformation in their communities, using a community engaged approach. These counter-narratives were then used in community engagement activities and health communication messages on college campuses and in the community to increase vaccine confidence among their peers. The Peer Champions were able to reach 350 young adults through these strategies.

Externality and policy intervention in interregional travel with infectious diseases

This paper theoretically investigates externalities and policy interventions in travel during a pandemic. We develop a tractable static model of two regions from a short-run perspective. The model shows that the externalities can be both negative and positive, depending on regional asymmetry. Thus, even when infectious diseases are widespread, travel restrictions do not necessarily reduce infections and do not necessarily improve social welfare. A formula for the optimal policy intervention is derived and shown to be the weighted average of four types of externalities defined by the direction of travel and the epidemiological status of a traveler.

Targeting COVID-19 vaccine-hesitancy in college students: An audience-centered approach

Objective: The study tested potential factors that differentiated the COVID-19 vaccine-hesitant and -inclined college students and, based on these factors, identified subgroups of the vaccine-hesitant students. Participants: Participants were 1,183 U.S. college students attending four-year universities or community colleges recruited through Qualtrics between January 25 and March 3, 2021. Methods: Participants completed an online survey assessing their COVID-19 vaccination intention, perceived risks of COVID-19 and the COVID-19 vaccines, efficacy beliefs regarding COVID-19 and the COVID-19 vaccines, and emotions toward taking the COVID-19 vaccines. Results: Vaccine-hesitant and -inclined college students varied in their emotions, risk perceptions, and efficacy beliefs regarding the virus and the vaccines. Using these factors as indicators, vaccine-hesitant college students were classified into five latent subgroups with distinct characteristics. Conclusions: In identifying subgroups of the vaccine-hesitant college students, the study has important insights to offer regarding the design of vaccine-promotion messaging strategies targeting the college student population.

COVID-19 risk perceptions, self-efficacy, and prevention behaviors among California undergraduate students

OBJECTIVE

College students play a major role in the transmission of SARS-CoV-2, the viral agent responsible for COVID-19. We aim to understand risk perceptions, self-efficacy, and adoption of prevention behaviors in this population to inform prevention strategies.

PARTICIPANTS

Undergraduate students attending a large public university.

METHODS

A convenience sample of students were surveyed (April-June 2020). Participants self-reported risk perceptions, perceived risk of contracting COVID-19, self-efficacy, and prevention behavior engagement.

RESULTS

A total of 1,449 students were included in the analysis. The majority were women (71.2%) and aged 18-24 (86.6%). Freshmen had the lowest risk and threat perceptions, as did men; men also had lower self-efficacy. Women engaged significantly more in prevention behaviors compared to men.

CONCLUSIONS

Perceived risk of contracting COVID-19 was low, but overall adoption of prevention behaviors was high due to local mandates. Freshmen men were identified as having the greatest need for changing perceptions and behaviors.

Preventive behavior intention for a viral outbreak among college students: The case of Mpox

OBJECTIVE

Examine college students' behavior intention to reduce viral outbreak transmission in the context of Mpox, and what preventive messaging strategies would be most effective in future transmissible disease outbreaks based on the Reasoned Action Approach (RAA).

PARTICIPANTS

Undergraduates at a mid-Atlantic U.S. University.

METHODS

An online survey (n = 266) conducted at the height of the recent Mpox outbreak in the U.S., asked about five target behaviors to reduce Mpox transmission, including RAA determinants for each behavior.

RESULTS

Highest intention was safe sex practices, lowest were vaccination and sexual abstinence, and sharing dishes and fabrics were in between. RAA determinants differed by target behavior, although attitudes were significantly positively associated with intention for all five behaviors.

CONCLUSIONS

College students are potentially open to preventative behaviors to reduce viral transmission during an outbreak. However, specific target behavior matters and messaging should focus on differing RAA determinants depending on target behavior.

SARS-CoV-2 variant introduction following spring break travel and transmission mitigation strategies

BACKGROUND

University spring break carries a two-pronged SARS-CoV-2 variant transmission risk. Circulating variants from universities can spread to spring break destinations, and variants from spring break destinations can spread to universities and surrounding communities. Therefore, it is critical to implement SARS-CoV-2 variant surveillance and testing strategies to limit community spread before and after spring break to mitigate virus transmission and facilitate universities safely returning to in-person teaching.

METHODS

We examined the SARS-CoV-2 positivity rate and changes in variant lineages before and after the university spring break for two consecutive years. 155 samples were sequenced across four time periods: pre- and post-spring break 2021 and pre- and post-spring break 2022; following whole genome sequencing, samples were assigned clades. The clades were then paired with positivity and testing data from over 50,000 samples.

RESULTS

In 2021, the number of variants in the observed population increased from four to nine over spring break, with variants of concern being responsible for most of the cases; Alpha percent composition increased from 22.2% to 56.4%. In 2022, the number of clades in the population increased only from two to three, all of which were Omicron or a sub-lineage of Omicron. However, phylogenetic analysis showed the emergence of distantly related sub-lineages. 2022 saw a greater increase in positivity than 2021, which coincided with a milder mitigation strategy. Analysis of social media data provided insight into student travel destinations and how those travel events may have impacted spread.

CONCLUSIONS

We show the role that repetitive testing can play in transmission mitigation, reducing community spread, and maintaining in-person education. We identified that distantly related lineages were brought to the area after spring break travel regardless of the presence of a dominant variant of concern.

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.

Monitoring and responding to emerging infectious diseases in a university setting: A case study using COVID-19

Emerging infection diseases (EIDs) are an increasing threat to global public health, especially when the disease is newly emerging. Institutions of higher education (IHEs) are particularly vulnerable to EIDs because student populations frequently share high-density residences and strongly mix with local and distant populations. In fall 2020, IHEs responded to a novel EID, COVID-19. Here, we describe Quinnipiac University's response to SARS-CoV-2 and evaluate its effectiveness through empirical data and model results. Using an agent-based model to approximate disease dynamics in the student body, the University established a policy of dedensification, universal masking, surveillance testing via a targeted sampling design, and app-based symptom monitoring. After an extended period of low incidence, the infection rate grew through October, likely due to growing incidence rates in the surrounding community. A super-spreader event at the end of October caused a spike in cases in November. Student violations of the University's policies contributed to this event, but lax adherence to state health laws in the community may have also contributed. The model results further suggest that the infection rate was sensitive to the rate of imported infections and was disproportionately impacted by non-residential students, a result supported by the observed data. Collectively, this suggests that campus-community interactions play a major role in campus disease dynamics. Further model results suggest that app-based symptom monitoring may have been an important regulator of the University's incidence, likely because it quarantined infectious students without necessitating test results. Targeted sampling had no substantial advantages over simple random sampling when the model incorporated contact tracing and app-based symptom monitoring but reduced the upper boundary on 90% prediction intervals for cumulative infections when either was removed. Thus, targeted sampling designs for surveillance testing may mitigate worst-case outcomes when other interventions are less effective. The results' implications for future EIDs are discussed.

Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students' learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility-a methodology we refer to as WiFi mobility models (WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC). WiMob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WiMob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WiMob, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WiMob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.

Cross-regional analysis of the association between human mobility and COVID-19 infection in Southeast Asia during the transitional period of “living with COVID-19”

Background

In response to COVID-19, Southeast Asian (SEA) countries had imposed stringent lockdowns and restrictions to mitigate the pandemic ever since 2019. Because of a gradually boosting vaccination rate along with a strong demand for economic recovery, many governments have shifted the intervention strategy from restrictions to “Living with COVID-19” where people gradually resumed their normal activities since the second half of the year 2021. Noticeably, timelines for enacting the loosened strategy varied across Southeast Asian countries, which resulted in different patterns of human mobility across space and time. This thus presents an opportunity to study the relationship between mobility and the number of infection cases across regions, which could provide support for ongoing interventions in terms of effectiveness.

Objective

This study aimed to investigate the association between human mobility and COVID-19 infections across space and time during the transition period of shifting strategies from restrictions to normal living in Southeast Asia. Our research results have significant implications for evidence-based policymaking at the present of the COVID-19 pandemic and other public health issues.

Methods

We aggregated weekly average human mobility data derived from the Facebook origin and destination Movement dataset. and weekly average new cases of COVID-19 at the district level from 01-Jun-2021 to 26-Dec-2021 (a total of 30 weeks). We mapped the spatiotemporal dynamics of human mobility and COVID-19 cases across countries in SEA. We further adopted the Geographically and Temporally Weighted Regression model to identify the spatiotemporal variations of the association between human mobility and COVID-19 infections over 30 weeks. Our model also controls for socioeconomic status, vaccination, and stringency of intervention to better identify the impact of human mobility on COVID-19 spread.

Results

The percentage of districts that presented a statistically significant association between human mobility and COVID-19 infections generally decreased from 96.15% in week 1 to 90.38% in week 30, indicating a gradual disconnection between human mobility and COVID-19 spread. Over the study period, the average coefficients in 7 SEA countries increased, decreased, and finally kept stable. The association between human mobility and COVID-19 spread also presents spatial heterogeneity where higher coefficients were mainly concentrated in districts of Indonesia from week 1 to week 10 (ranging from 0.336 to 0.826), while lower coefficients were mainly located in districts of Vietnam (ranging from 0.044 to 0.130). From week 10 to week 25, higher coefficients were mainly observed in Singapore, Malaysia, Brunei, north Indonesia, and several districts of the Philippines. Despite the association showing a general weakening trend over time, significant positive coefficients were observed in Singapore, Malaysia, western Indonesia, and the Philippines, with the relatively highest coefficients observed in the Philippines in week 30 (ranging from 0.101 to 0.139).

Conclusions

The loosening interventions in response to COVID-19 in SEA countries during the second half of 2021 led to diverse changes in human mobility over time, which may result in the COVID-19 infection dynamics. This study investigated the association between mobility and infections at the regional level during the special transitional period. Our study has important implications for public policy interventions, especially at the later stage of a public health crisis.

JUE Insight: Urban flight seeded the COVID-19 pandemic across the United States

We document large-scale urban flight in the United States during the COVID-19 pandemic. Regions that saw migrant influx experienced greater subsequent new COVID-19 cases, linking urban flight (as a disease vector) and coronavirus spread in destination areas. Urban residents fled to socially connected areas, consistent with the theory that individuals sheltered with friends and family, or in second homes. Populations that fled were disproportionately younger, whiter, and wealthier. The association between migration and subsequent new cases persists when instrumenting for migration with social networks.

High-Speed railways and the spread of Covid-19

High-speed railways (HSRs) greatly decrease transportation costs and facilitate the movement of goods, services, and passengers across cities. In the context of the Covid-19 pandemic, however, HSRs may contribute to the cross-regional spread of the new coronavirus. This paper evaluates the role of HSRs in spreading Covid-19 from Wuhan to other Chinese cities. We use train frequencies in 1971 and 1990 as instrumental variables. Empirical results from gravity models demonstrate that one more HSR train originating from Wuhan each day before the Wuhan lockdown increases the cumulative number of Covid-19 cases in a city by about 10 percent. The empirical analysis suggests that other transportation modes, including normal-speed trains and airline flights, also contribute to the spread of Covid-19, but their effects are smaller than the effect of HSRs. This paper's findings indicate that transportation infrastructures, especially HSR trains originating from a city where a pandemic broke out, can be important factors promoting the spread of an infectious disease.

Investigating the two-way relationship between mobility flows and COVID-19 cases

Following a pandemic disease outbreak, people travel to areas with low infection risk, but at the same time the epidemiological situation worsens as mobility flows to those areas increase. These feedback effects from epidemiological conditions to inflows and from inflows to subsequent infections are underexplored to date. This study investigates the two-way relationship between mobility flows and COVID-19 cases in a context of unrestricted mobility without COVID-19 vaccines. To this end, we merge data on COVID-19 cases in Spain during the summer of 2020 at the province level with mobility records based on mobile position tracking. Using a control function approach, we find that a 1% increase in arrivals translates into a 3.5% increase in cases in the following week and 5.6% ten days later. A simulation exercise shows the cases would have dropped by around 64% if the Second State of Alarm had been implemented earlier.

Diminishing returns: Nudging Covid-19 prevention among Colombian young adults

Nonpharmaceutical interventions (NPIs) like social distancing, face masks, and handwashing will continue to be a frontline defense against Covid-19 for some time. But their effectiveness depends critically on compliance by young adults, who are most likely both to become infected and to infect others. We conducted a randomized controlled trial in Bogotá, Colombia, to assess the effectiveness of informational nudges emphasizing the private and public benefits of compliance on university students' concern about Covid-19, recent compliance with NPI recommendations, and intended future compliance. Although nudges boosted concern, they had limited effects on either recent or intended future compliance. We attribute these null results to high baseline levels of information about and compliance with NPIs, an informational diminishing returns scenario that is likely to be increasingly common globally.

Cities in a pandemic: Evidence from China

This paper studies the impact of urban density, city government efficiency, and medical resources on COVID-19 infection and death outcomes in China. We adopt a simultaneous spatial dynamic panel data model to account for (i) the simultaneity of infection and death outcomes, (ii) the spatial pattern of the transmission, (iii) the intertemporal dynamics of the disease, and (iv) the unobserved city-specific and time-specific effects. We find that, while population density increases the level of infections, government efficiency significantly mitigates the negative impact of urban density. We also find that the availability of medical resources improves public health outcomes conditional on lagged infections. Moreover, there exists significant heterogeneity at different phases of the epidemiological cycle.

Assessing the impact of COVID-19 mass testing in South Tyrol using a semi-parametric growth model

Mass antigen testing has been proposed as a possible cost-effective tool to contain the COVID-19 pandemic. We test the impact of a voluntary mass testing campaign implemented in the Italian region of South Tyrol on the spread of the virus in the following months. We do so by using an innovative empirical approach which embeds a semi-parametric growth model-where COVID-19 transmission dynamics are allowed to vary across regions and to be impacted by the implementation of the mass testing campaign-into a synthetic control framework which creates an appropriate control group of other Italian regions. Our results suggest that mass testing campaigns are useful instruments for mitigating the pandemic.

A Qualitative Evaluation of the Barriers and Enablers for Implementation of an Asymptomatic SARS-CoV-2 Testing Service at the University of Nottingham: A Multi-Site Higher Education Setting in England

Asymptomatic testing for SARS-CoV-2 RNA has been used to prevent and manage COVID-19 outbreaks in university settings, but few studies have explored their implementation. The aim of the study was to evaluate how an accredited asymptomatic SARS-CoV-2 testing service (ATS) was implemented at the University of Nottingham, a multi-campus university in England, to identify barriers and enablers of implementation and to draw out lessons for implementing pandemic response initiatives in higher education settings. A qualitative interview study was conducted with 25 ATS personnel between May and July 2022. Interviews were conducted online, audio-recorded, and transcribed. Participants were asked about their experience of the ATS, barriers and enablers of implementation. Transcripts were thematically analysed. There were four overarching themes: (1) social responsibility and innovation, (2) when, how and why people accessed testing, (3) impact of the ATS on the spread of COVID-19, and (4) lessons learned for the future. In establishing the service, the institution was seen to be valuing its community and socially responsible. The service was viewed to be broadly successful as a COVID-19 mitigation approach. Challenges to service implementation were the rapidly changing pandemic situation and government advice, delays in service accreditation and rollout to staff, ambivalence towards testing and isolating in the target population, and an inability to provide follow-up support for positive cases within the service. Facilitators included service visibility, reduction in organisational bureaucracy and red tape, inclusive leadership, collaborative working with regular feedback on service status, flexibility in service delivery approaches and simplicity of saliva testing. The ATS instilled a perception of early 'return to normality' and impacted positively on staff feelings of safety and wellbeing, with wider benefits for healthcare services and local communities. In conclusion, we identified common themes that have facilitated or hindered the implementation of a SARS-CoV-2 testing service at a university in England. Lessons learned from ATS implementation will inform future pandemic response interventions in higher education settings.

The effectiveness and costs of nonpharmaceutical interventions for COVID-19 containment: A border discontinuous difference-in-difference approach

We examine the effectiveness and costs of alternative nonpharmaceutical interventions (NPIs) for COVID-19 containment. Using a border discontinuous difference-in-difference approach, we find that the enforcement of rigid NPIs reduces the number of new COVID-19 cases by 10.8% in China, comparied with cities with less NPIs. Among the three NPIs, contact tracing is much more effective than the other two NPIs, namely, public information provision and social distancing. The connections of mayors to the upper-level politicians reinforce the city's implementation of rigid NPIs. These networks also serve as an informal signaling channel to the neighboring cities, encouraging the adjacent cities to impose strict NPIs to curb the spread of COVID-19. We further estimate the long-term costs of the NPIs - a net present value of 2153 yuan per child in the human capital loss attributed to more prolonged school closure alone.

High-speed railway and the intercity transmission of epidemics: Evidence from COVID-19 in China

In the context of the COVID-19 outbreak in Wuhan, we investigate the effect of intercity high-speed railway (HSR) connections on intercity transmission of epidemics in the absence of government intervention. Intercity HSR connections increase the number of COVID-19 patients per 10,000 population by 0.029, accounting for 45% of the total infections. Our results remain intact in several robustness assessments. The total economic loss owing to HSR connections to Wuhan is estimated to be USD 0.62 billion. The internal mechanism demonstrates that intercity HSR connections increase intercity COVID-19 transmission by facilitating human mobility between cities. Based on intercity transportation connections, our findings can help the government predict the direction and scope of virus transmission and control the intercity transmission of epidemics.

Seasonal farm labor and COVID-19 spread

The COVID-19 pandemic in 2020 caused unprecedented shocks to agricultural food systems, including increased risk to worker health, labor-related input costs, and production uncertainty. Despite employer precautions, there were numerous worksite outbreaks of COVID-19. This paper examines the relationship between month-to-month variation in historical agricultural employment and changes in the incidence of confirmed COVID-19 cases and deaths within U.S. counties from April to August 2020. The results show that employment of 100 additional workers in fruit, vegetable, and horticultural production was associated with 4.5% more COVID-19 cases within counties or an additional 18.65 COVID-19 cases and 0.34 additional COVID-19 deaths per 100,000 individuals in the county workforce.

Voting, contagion and the trade-off between public health and political rights: Quasi-experimental evidence from the Italian 2020 polls

Natural disasters raise challenging trade-offs between public health safety and inalienable rights like the active involvement in political choices through voting. We exploit a quasi-experimental setting provided by multiple ballots across regions and municipalities during the Italian 2020 elections to estimate the effect of voters' turnout on the spread of COVID-19. By employing an event-study design with a two-stage Control Function strategy, we find that post-poll new COVID infections increased by an average of 1.1% for each additional percentage point of turnout. Based on these estimates and real political events, we also show through a simulation that in-person voting during a high-infection regime may have a large impact on public health outcomes, more than doubling new infections, deaths and hospitalizations. These findings suggest that policy-makers' responses to natural disasters should be flexible and contingent to the emergency severity, in order to minimize social costs for citizens.

Political violence, risk aversion, and population health: Evidence from the US Capitol riot

This study is the first to explore the impact of the January 6, 2021 Capitol riot on risk avoidance behavior and the spread of COVID-19. First, using anonymized smartphone data from SafeGraph, Inc., and an event-study approach, we document a substantial increase on January 6 in non-resident smartphone pings at the sites of the protest: the Ellipse, the National Mall, and the US Capitol Building. Then, using data from the same source and a synthetic control approach, we find that the Capitol riot led to an increase in stay-at-home behavior among District of Columbia residents, consistent with risk avoidance behavior and post-riot policies designed to limit large in-person gatherings. Finally, while we find no evidence that the Capitol riot substantially increased the spread of COVID-19 in the District of Columbia, we do find that counties with the highest inflows of out-of-town protesters experienced a 0.004 to 0.010 increase in the rate of daily cumulative COVID-19 case growth during the month following the event. These findings are exacerbated in counties without COVID-19 mitigation policies in place.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00148-022-00914-0.

Throwing caution to the wind: How hurricanes affect COVID-19 spread

This study exploits the pathway of Hurricane Laura to assess its impact on the spread of COVID-19. Using US hospital data on confirmed and suspected adult COVID-19 cases, we find average daily cases per week rose by more than 12% primarily in tropical storm-affected counties in subsequent weeks. We suspect the key mechanisms involve constraints on social distancing for two reasons. First, there is significant evidence of storm-induced mobility. Second, lower income areas endured higher growth in hospital cases during the post-hurricane period. These findings provide crucial insights for policy-makers when designing natural disaster protocols to adjust for potential respiratory viral illnesses.

Comparison of high-frequency in-pipe SARS-CoV-2 wastewater-based surveillance to concurrent COVID-19 random clinical testing on a public U.S. university campus

Wastewater-based epidemiology (WBE) is utilized globally as a tool for quantifying the amount of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within communities, yet the efficacy of community-level wastewater monitoring has yet to be directly compared to random Coronavirus Disease of 2019 (COVID-19) clinical testing; the best-supported method of virus surveillance within a single population. This study evaluated the relationship between SARS-CoV-2 RNA in raw wastewater and random COVID-19 clinical testing on a large university campus in the Southwestern United States during the Fall 2020 semester. Daily composites of wastewater (24-hour samples) were collected three times per week at two campus locations from 16 August 2020 to 1 January 2021 (n = 95) and analyzed by reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) targeting the SARS-CoV-2 E gene. Campus populations were estimated using campus resident information and anonymized, unique user Wi-Fi connections. Resultant trends of SARS-CoV-2 RNA levels in wastewater were consistent with local and nationwide pandemic trends showing peaks in infections at the start of the Fall semester in mid-August 2020 and mid-to-late December 2020. A strong positive correlation (r = 0.71 (p < 0.01); n = 15) was identified between random COVID-19 clinical testing and WBE surveillance methods, suggesting that wastewater surveillance has a predictive power similar to that of random clinical testing. Additionally, a comparative cost analysis between wastewater and clinical methods conducted here show that WBE was more cost effective, providing data at 1.7% of the total cost of clinical testing ($6042 versus $338,000, respectively). We conclude that wastewater monitoring of SARS-CoV-2 performed in tandem with random clinical testing can strengthen campus health surveillance, and its economic advantages are maximized when performed routinely as a primary surveillance method, with random clinical testing reserved for an active outbreak situation.

COVID-19 and Attendance Demand for Professional Sport in Japan: A Multilevel Analysis of Repeated Cross-Sectional National Data during the Pandemic

In the current investigation, we assess the effect of COVID-19 on intention-based spectator demand for professional sports in Japan captured by eight, monthly repeated cross-sectional national surveys from May to December 2020 (n = 20,121). We regress spectator demand on individual (e.g., gender), prefecture-wave (e.g., COVID-19 infection status), and prefecture-level factors (i.e., with or without quality professional teams). The results of multilevel logistic regression demonstrate that individual (i.e., male, younger, full-time employment, and with children status) and prefecture-level team factors (i.e., with teams) were associated with intention-based spectator demand. Nevertheless, COVID-19-related factors were found to be unrelated to spectator demand. The findings imply that sports fans are likely to return to the stadium once behavioral restrictions are lifted. The current research provided further evidence that individual factors and team quality serve as influential antecedents of spectator demand in the context of the COVID-19 epidemic.

Effectiveness of alternative semester break schedules on reducing COVID-19 incidence on college campuses

Despite COVID-19 vaccination programs, the threat of new SARS-CoV-2 strains and continuing pockets of transmission persists. While many U.S. universities replaced their traditional nine-day spring 2021 break with multiple breaks of shorter duration, the effects these schedules have on reducing COVID-19 incidence remains unclear. The main objective of this study is to quantify the impact of alternative break schedules on cumulative COVID-19 incidence on university campuses. Using student mobility data and Monte Carlo simulations of returning infectious student size, we developed a compartmental susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) model to simulate transmission dynamics among university students. As a case study, four alternative spring break schedules were derived from a sample of universities and evaluated. Across alternative multi-break schedules, the median percent reduction of total semester COVID-19 incidence, relative to a traditional nine-day break, ranged from 2 to 4% (for 2% travel destination prevalence) and 8-16% (for 10% travel destination prevalence). The maximum percent reduction from an alternate break schedule was estimated to be 37.6%. Simulation results show that adjusting academic calendars to limit student travel can reduce disease burden. Insights gleaned from our simulations could inform policies regarding appropriate planning of schedules for upcoming semesters upon returning to in-person teaching modalities.

Early-stage spatial disease surveillance of novel SARS-CoV-2 variants of concern in Germany with crowdsourced data

The emergence and rapid spread of novel variants of concern (VOC) of the coronavirus 2 constitute a major challenge for spatial disease surveillance. We explore the possibility to use close to real-time crowdsourced data on reported VOC cases (mainly the Alpha variant) at the local area level in Germany. The aim is to use these data for early-stage estimates of the statistical association between VOC reporting and the overall COVID-19 epidemiological development. For the first weeks in 2021 after international importation of VOC to Germany, our findings point to significant increases of up to 35-40% in the 7-day incidence rate and the hospitalization rate in regions with confirmed VOC cases compared to those without such cases. This is in line with simultaneously produced international evidence. We evaluate the sensitivity of our estimates to sampling errors associated with the collection of crowdsourced data. Overall, we find no statistical evidence for an over- or underestimation of effects once we account for differences in data representativeness at the regional level. This points to the potential use of crowdsourced data for spatial disease surveillance, local outbreak monitoring and public health decisions if no other data on new virus developments are available.

The dynamical formation of ephemeral groups on networks and their effects on epidemics spreading

In network models of propagation processes, the individual, microscopic level perspective is the norm, with aggregations studied as possible outcomes. On the contrary, we adopted a mesoscale perspective with groups as the core element and in this sense we present a novel agent-group dynamic model of propagation in networks. In particular, we focus on ephemeral groups that dynamically form, create new links, and dissolve. The experiments simulated 160 model configurations and produced results describing cases of consecutive and non-consecutive dynamic grouping, bounded or unbounded in the number of repetitions. Results revealed the existence of complex dynamics and multiple behaviors. An efficiency metric is introduced to compare the different cases. A Null Model analysis disclosed a pattern in the difference between the group and random models, varying with the size of groups. Our findings indicate that a mesoscopic construct like the ephemeral group, based on assumptions about social behavior and absent any microscopic level change, could produce and describe complex propagation dynamics. A conclusion is that agent-group dynamic models may represent a powerful approach for modelers and a promising new direction for future research in models of coevolution between propagation and behavior in society.

JUE insight: Demand for transportation and spatial pattern of economic activity during the pandemic

Using traffic data from Taiwan for 2020, we quantify how the COVID-19 outbreak affected demand for public and private transportation. Despite there being no governmental restrictions, substantial shifts in travel modes were observed. During the peak of the pandemic in Taiwan within the study period (mid-March 2020), railway ridership declined by 40% to 60%, while highway traffic volume increased by 20%. Furthermore, railway ridership was well below pre-pandemic levels, though there were no locally transmitted cases in the eight-month period from mid-April to December. These changes in traffic patterns had implications for spatial patterns of economic activity: retail sales and nighttime luminosity data show that during the pandemic, economic activity shifted away from areas in the vicinity of major railway stations.

Does precise case disclosure limit precautionary behavior? Evidence from COVID-19 in Singapore

Limiting the spread of contagious diseases can involve both government-managed and voluntary efforts. Governments have a number of policy options beyond direct intervention that can shape individuals' responses to a pandemic and its associated costs. During its first wave of COVID-19 cases, Singapore was among a few countries that attempted to adjust behavior through the announcement of detailed case information. Singapore's Ministry of Health maintained and shared precise, daily information detailing local travel behavior and residences of COVID-19 cases. We use this policy along with device-level cellphone data to quantify how local and national COVID-19 case announcements trigger differential behavioral changes. We find evidence that individuals are three times more responsive to outbreaks in granularly defined locales. Conditional on keeping infection rates at a manageable level, the results suggest economic value in this type of transparency by mitigating the scope of precautionary activity reductions.

Effects of large gatherings on the COVID-19 epidemic: Evidence from professional and college sports

We compare COVID-19 case loads and mortality across counties that hosted more versus fewer NHL hockey games, NBA basketball games, and NCAA basketball games during the early months of 2020, before any large outbreaks were identified. We find that hosting one additional NHL/NBA game in March 2020 leads to an additional 7520 cases and 658 deaths. Similarly, we find that hosting an additional NCAA Division 1 men's basketball game in March 2020 results in an additional 34 deaths. Back-of-the-envelope calculations suggest that the per-game fatality costs were 200-300 times greater than per-game spending.

Effects of large gatherings on the COVID-19 epidemic: Evidence from professional and college sports

We compare COVID-19 case loads and mortality across counties that hosted more versus fewer NHL hockey games, NBA basketball games, and NCAA basketball games during the early months of 2020, before any large outbreaks were identified. We find that hosting one additional NHL/NBA game in March 2020 leads to an additional 7520 cases and 658 deaths. Similarly, we find that hosting an additional NCAA Division 1 men's basketball game in March 2020 results in an additional 34 deaths. Back-of-the-envelope calculations suggest that the per-game fatality costs were 200-300 times greater than per-game spending.

We Want You Back: Uncovering the Effects on In-Person Instructional Operations in Fall 2020

Postsecondary institutions' responses to COVID-19 are a topic of immediate relevance. Emergent research suggests that partisanship was more strongly linked to institutions offering in-person instruction for Fall 2020 than was COVID-19. Using data from the College Crisis Initiative and a multiple group structural equation modeling approach, we tested the relationships between our outcome of interest (in-person instruction in Fall 2020) and state and county sociopolitical features, state and county COVID-19 rates, and state revenue losses. Our full-sample model suggested that County Political Preferences had the strongest association with in-person instruction, followed by Pandemic Severity and State Sociopolitical Features. Because institutional sectors may be uniquely sensitive to these factors, we tested our models separately on 4-year public, 4-year private, and 2-year public and 2-year private institutions. State Sociopolitical Features were significantly related to in-person instruction for 4-year private and 2-year public institutions but were strongest for 4-year public institutions. For 4-year private and 2-year public institutions, County Political Preferences' effect sizes were 2-3 times stronger than effects from State Sociopolitical Features. Pandemic Severity was significantly, negatively related to in-person instruction for 4-year private and 2-year public institutions-similar in magnitude to State Sociopolitical Features. Our analysis revealed that COVID-19 played a stronger role in determining in-person instruction in Fall 2020 than initial research using less sophisticated methods suggested-and while State Sociopolitical Features may have played a role in the decision, 4-year private and 2-year public institutions were more sensitive to county-level preferences.

Coronavirus Disease 2019 Cases at Universities and Colleges in Seoul Metropolitan Area

BACKGROUND

To facilitate evidence-based policy-making on safe reopening of higher education facilities, there is an urgent need to assess baseline profile of coronavirus disease 2019 (COVID-19) incidents within the university/college settings. We aimed to describe the epidemiology of COVID-19 in universities/colleges in Seoul Metropolitan Area during COVID-19 pandemic period.

METHODS

Among the 38 universities in Seoul, 23 have agreed to participate in the study. Confirmed COVID-19 cases were identified from individual-level case reports submitted to the universities and to the health authorities from February 1, 2020, to June 30, 2021. Through the linkage with the Central Disease Control Headquarters' database, number of secondary infected cases (both within and outside of the campus) were counted.

RESULTS

Between February 2020 and June 2021, a total of 827 COVID-19 cases were confirmed and reported in the universities across Seoul Metropolitan City. Generally, the community-associated cases had peaks preceding the university/college-associated. Of those with the documented clinical parameters, 38.6% of the cases were asymptomatic. Among them, 93% were potentially exposed off-campus, and 87.7% of the cases had not produced the secondary infection cases.

CONCLUSION

In the setting of rigorous infection prevention measures in combination with on- and off- hybrid classes, COVID-19 incidences and outbreaks were limited in university and college campus area across Seoul Metropolitan Area. The evidence around the infection preventive measures in higher education facilities in Seoul Metropolitan Area, suggest insignificant impact on community transmission.

Socio-Economic Factors Influencing Travel Decision-Making of Poles and Nepalis during the COVID-19 Pandemic

This study aimed to present the socio-economic conditions, during the COVID-19 pandemic, and their effects on travel decision-making. Data were collected in January and February 2021 using an online questionnaire survey. We compared the data on personal attributes, socio-economic factors, and organizational-related factors of 957 Polish and 181 Nepalese respondents. We found that people would like to travel, even during the COVID-19 pandemic, in both countries; however, tourism in developed countries is expected to revive earlier than in developing countries. Tourists chose natural and rustic areas for travel destinations and family members and friends as travel companions in both countries. Similarly, the use of travel agencies decreased significantly as compared to before the COVID-19 pandemic in both countries. Levels of income and travel motivation were observed to be different between the two countries. Respondents from both countries similarly believed that the overall cost of travel had increased during the pandemic. Therefore, policymakers and authorities of tourism destinations should jointly introduce discounted or subsidized packages targeting family members.

Simulating the impacts of interregional mobility restriction on the spatial spread of COVID-19 in Japan

A spatial susceptible-exposed-infectious-recovered (SEIR) model is developed to analyze the effects of restricting interregional mobility on the spatial spread of the coronavirus disease 2019 (COVID-19) infection in Japan. National and local governments have requested that residents refrain from traveling between prefectures during the state of emergency. However, the extent to which restricting interregional mobility prevents infection expansion is unclear. The spatial SEIR model describes the spatial spread pattern of COVID-19 infection when people commute or travel to a prefecture in the daytime and return to their residential prefecture at night. It is assumed that people are exposed to an infection risk during their daytime activities. The spatial spread of COVID-19 infection is simulated by integrating interregional mobility data. According to the simulation results, interregional mobility restrictions can prevent the geographical expansion of the infection. On the other hand, in urban prefectures with many infectious individuals, residents are exposed to higher infection risk when their interregional mobility is restricted. The simulation results also show that interregional mobility restrictions play a limited role in reducing the total number of infected individuals in Japan, suggesting that other non-pharmaceutical interventions should be implemented to reduce the epidemic size.

Do elections accelerate the COVID-19 pandemic?: Evidence from a natural experiment

Elections define representative democracies but also produce spikes in physical mobility if voters need to travel to polling places. In this paper, we examine whether large-scale, in-person elections propagate the spread of COVID-19. We exploit a natural experiment from the Czech Republic, which biannually renews mandates in one-third of Senate constituencies that rotate according to the 1995 election law. We show that in the second and third weeks after the 2020 elections (held on October 9-10), new COVID-19 infections grew significantly faster in voting compared to non-voting constituencies. A temporarily related peak in hospital admissions and essentially no changes in test positivity rates suggest that the acceleration was not merely due to increased testing. The acceleration did not occur in the population above 65, consistently with strategic risk-avoidance by older voters. Our results have implications for postal voting reforms or postponing of large-scale, in-person (electoral) events during viral outbreaks.

Economic impact payment, human mobility and COVID-19 mitigation in the USA

This paper studies the effect of the economic impact payment (EIP) on individual contributions to COVID-19 mitigation efforts in the USA, where the mitigation efforts are measured by the reduction of daily human mobility. I empirically estimate the effect of the EIP in April 2020 and use cellphone GPS data of 45 million smartphone devices as a proxy for human mobility across 216,069 Census Block Groups. The results show that when receiving the EIP, households significantly increased "Median Home Dwell Time" by an average of 3-5% (about 26-45 min). The paper highlights this unintended effect of the EIP, namely, that in addition to providing economic assistance, the EIP also helped increase individual contributions to mitigation efforts that slowed COVID-19 virus transmission in early 2020.

Supplementary Information

The online version supplementary material available at 10.1007/s00181-021-02117-0.

Determinants of the Energy Poverty of Polish Students during the COVID-19 Pandemic

The aim of this paper is to define the factors influencing the level of energy poverty among students. The analysis of these factors is based on the results of a survey conducted among a group of 937 students at the Cracow University of Economics. The study takes into consideration the changes in the attitudes and behavior of students resulting from the introduction of distance learning during the COVID-19 pandemic. The switch to distance learning resulted in a significant increase in the number of responses related to feeling ill or sick due to inadequate temperature (from 24% before a lockdown to 32% after the introduction of a lockdown). Students experienced temporary surges in their overall living costs due to the pandemic, especially during the first wave. The respondents who experienced inappropriate temperatures (inadequate heating) due to excessive costs felt ill or became sick more often than others. The study demonstrated that those who pay more for energy (defined as a surplus payment in excess of 10%) tended to be, on average, less energy-aware than others. The following indicators of energy poverty among the students were distinguished: high living costs, small degree of influence over the choice of living quarters, as well as concerns over energy efficiency and environment. The conclusions drawn from the conducted studies may be utilized to design public policies aimed at curtailing the phenomenon of energy poverty among students. This issue is particularly prominent in large urban agglomerations where the costs of living are high and result in the feeling of pressure regarding the need to save money on thermal energy consumption.

Risk avoidance, offsetting community effects, and COVID-19: Evidence from an indoor political rally

The Centers for Disease Control and Prevention (CDC) deem large indoor gatherings without social distancing the "highest risk" activity for COVID-19 contagion. On June 20, 2020, President Donald J. Trump held his first mass campaign rally following the US coronavirus outbreak at the indoor Bank of Oklahoma arena. In the weeks following the event, numerous high-profile national news outlets reported that the Trump rally was "more than likely" the cause of a coronavirus surge in Tulsa County based on time series data. This study is the first to rigorously explore the impacts of this event on social distancing and COVID-19 spread. First, using data from SafeGraph Inc, we show that while non-resident visits to census block groups hosting the Trump event grew by approximately 25 percent, there was no decline in net stay-at-home behavior in Tulsa County, reflecting important offsetting behavioral effects. Then, using data on COVID-19 cases from the CDC and a synthetic control design, we find little evidence that COVID-19 grew more rapidly in Tulsa County, its border counties, or in the state of Oklahoma than each's estimated counterfactual during the five-week post-treatment period we observe. Difference-in-differences estimates further provide no evidence that COVID-19 rates grew faster in counties that drew relatively larger shares of residents to the event. We conclude that offsetting risk-related behavioral responses to the rally-including voluntary closures of restaurants and bars in downtown Tulsa, increases in stay-at-home behavior, displacement of usual activities of weekend inflows, and smaller-than-expected crowd attendance-may be important mechanisms.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11166-021-09359-4.