Lockdown Policies Research Articles

Exploring the experiences of college students in Chinese campus lockdown policy during the COVID-19 outbreak: A qualitative study

Campus lockdown policy is one of the most effective non-pharmaceutical intervention strategies used to prevent and control the coronavirus disease 2019epidemic worldwide. College students were greatly affected by this policy. Related studies center on English-speaking countries; few have highlighted discussion of the Chinese context. This study, therefore, aimed to broadly elicit the real experiences and unique insights of college students on Chinese campus lockdown policy. Through qualitative research, we identified four key themes with ten contributory subthemes: physiological experiences, safety experiences, love and belonging experiences, and self-esteem experiences. The unique contribution of this study relates to experiences relating to love and belonging and to self-esteem, which are little discussed in the existing literature. Our findings can provide enlightenment on how to improve college students’ health.

A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.

The outbreak of the Coronavirus Disease 2019 (COVID-19) has profoundly influenced daily life, necessitating the understanding of the relationship between the epidemic's progression and population dynamics. In this study, we present a data-driven framework that integrates GIS-based data mining technology and a Susceptible, Exposed, Infected and Recovered (SEIR) model. This approach helps delineate population dynamics at the grid and community scales and analyze the impacts of government policies, urban functional areas, and intercity flows on population dynamics during the pandemic. Xiamen Island was selected as a case study to validate the effectiveness of the data-driven framework. The results of the high/low cluster analysis provide 99% certainty (P < 0.01) that the population distribution between January 23 and March 16, 2020, was not random, a phenomenon referred to as high-value clustering. The SEIR model predicts that a ten-day delay in implementing a lockdown policy during an epidemic can lead to a significant increase in the number of individuals infected by the virus. Throughout the epidemic prevention and control period (January 23 to February 21, 2020), residential and transportation areas housed more residents. After the resumption of regular activities, the population was mainly concentrated in residential, industrial, and transportation, as well as road facility areas. Notably, the migration patterns into and out of Xiamen were primarily centered on neighboring cities both before and after the outbreak. However, migration indices from cities outside the affected province drastically decreased and approached zero following the COVID-19 outbreak. Our findings offer new insights into the interplay between the epidemic's development and population dynamics, which enhances the prevention and control of the coronavirus epidemic.

The Relationship Between Lockdowns and Video Game Playtime: Multilevel Time-Series Analysis Using Massive-Scale Data Telemetry.

COVID-19 led governments worldwide to enact a variety of containment and closure policies. Substantial attention has been directed toward the idea that these public health measures may have unanticipated negative side effects. One proposed effect relates to video games. There is a nascent evidence base suggesting that individuals played video games for longer and in a more disordered manner during lockdowns and school closures specifically. These increases are commonly framed as a potential health concern in relation to disordered gaming. However, the evidence base regarding changes in gaming during the COVID-19 pandemic is based on self-report and, thus, is susceptible to bias. Therefore, it is unclear what the true consequences of lockdowns were for gaming behavior worldwide. The primary objective of this study was to estimate whether any specific lockdown policy led to meaningful increases in the amount of time individuals spent playing video games. Rather than relying on self-report, we used >251 billion hours of raw gameplay telemetry data from 184 separate countries to assess the behavioral correlates of COVID-19-related policy decisions. A multilevel model estimated the impact of varying enforcement levels of 8 containment and closure policies on the amount of time that individual users spent in-game. Similar models estimated the impact of policy on overall playtime and the number of users within a country. No lockdown policy can explain substantial variance in playtime per gamer. School closures were uniquely associated with meaningful increases in total playtime within a country (r2=0.048). However, this was associated with increases in the number of unique individuals playing games (r2=0.057) rather than increases in playtime per gamer (r2<0.001). Previous work using self-report data has suggested that important increases in heavy gaming may occur during pandemics because of containment and closure ("lockdown") procedures. This study contrasts with the previous evidence base and finds no evidence of such a relationship. It suggests that significant further work is needed before increases in disordered or heavy gaming are considered when planning public health policies for pandemic preparedness.

Livelihood Changes, Spatial Anticontagion Policy Effects, and Structural Resilience of National Food Systems in a Sub-Saharan African Country Context: A Panel Machine Learning Approach

The livelihood changes due to the COVID-19 policies in low-income and transitional economies serve as a lever for gauging the structural resilience of national food systems. Yet, few studies have addressed the cascading effects of the pandemic policies on the livelihood changes of farming system actors or modeled and provided coherent hypotheses about the transitory structural shifts at the micro-level. Other studies on the subject have either captured the early impacts of the pandemic on food systems with limited or no insight into the sub-Saharan African context or have used macro-level data, due to sparsely available micro-level data. These early insights are relevant for the design of early warning systems. However, an ongoing and deeper insight into the effects of pandemic policies is critical, since new and more comprehensive policies are needed to address the economic fallout and the extenuating effects of COVID-19 on food supply chain disruptions. The overriding questions are as follows: what are the effects of the pandemic policies on the livelihoods of food system actors and are there spatial-economic variations in the effects of the pandemic policies on the livelihoods of the farming system actors? Using 2019 and 2020 primary data from 836 farming system actors in Ghana, we offer fresh insights into the transitory micro-level livelihood changes caused by the COVID-19 anticontagion policies. We analyzed the data using the generalized additive, subset regression, classical linear, and logistic regression models in a machine learning framework. We show that the changes in the livelihood outcomes of the food system actors in Ghana coincide with the nature of pandemic mitigation policies adopted in the spatial units. We found that the lockdown policies had a negative and significant effect on the livelihoods of the farming system actors in the lockdown areas. The policies also negatively affected the livelihoods of the farming system actors in distant communities that shared no direct boundary with the lockdown areas. On the contrary, the lockdown policies positively affected the livelihoods of the farming system actors in the directly contiguous communities to the lockdown areas. We also document the shifts in the livelihood outcomes of the farming system actors, such as income, employment, food demand, and food security in the different spatial policy areas.

Characterizing load profile-based enterprise profiling under COVID-19 lockdown policy: A provincial case in China

The COVID-19 epidemic has led to devastating consequences worldwide due to strict social distancing measures, travel bans, city lockdowns, and other activity restrictions. Numerous lessons have been accumulated by various industries or sectors in the process of coping with COVID-19. In our paper, the actual electricity consumption of 1.145 million enterprises in a province of China is investigated in the normal period, the breakout period, and the recovery period. An artificial intelligence based enterprise profiling model is established to characterize power consumption profile geographically, temporally, and industrially. The recovery rate of enterprise power consumption is proposed as a key indicator representing the status of production resumption. Under lockdown measures, enterprise in different regions or sectors exhibits diversified responses in power consumption, the unsupervised learning model and the correlated coefficient extract the abstract characteristics and labels from various enterprise power profile. Ultimately, enterprises with similar electricity consumption characteristics are grouped and labeled to provide customized power services. Accurate enterprise profiling can effectively aid in facing with the challenges of insufficient orders, tight capital chains, and rising costs caused by the epidemic.

Did Liberal Lockdown Policies Change Spatial Behaviour in Sweden? Mapping Daily Mobilities in Stockholm Using Mobile Phone Data During COVID-19

Sweden had the most liberal lockdown policies in Europe during the Covid-19 pandemic. Relying on individual responsibility and behavioural nudges, their effectiveness was questioned from the perspective of others who responded with legal restrictions on behaviour. In this study, using mobile phone data, we therefore examine daily spatial mobilities in Stockholm to understand how they changed during the pandemic from their pre-pandemic baseline given this background. The analysis demonstrates: that mobilities did indeed change but with some variations according to (a) the residential social composition of places and (b) their locations within the city; that the changes were long lasting; and that the average fall in spatial mobility across the whole was not caused by everybody moving less but instead by more people joining the group of those who stayed close to home. It showed, furthermore, that there were seasonal differences in spatial behaviour as well as those associated with major religious or national festivals. The analysis indicates the value of mobile phone data for spatially fine-grained mobility research but also shows its weaknesses, namely the lack of personal information on important covariates such as age, gender, and education.

The workload change and depression among emergency medical staff after the open policy during COVID-19: a cross-sectional survey in Shandong, China.

In the middle of December 2022, the Chinese government adjusted the lockdown policy on coronavirus disease 2019 (COVID-19), a large number of infected patients flooded into the emergency department. The emergency medical staff encountered significant working and mental stress while fighting the COVID-19 pandemic. We aimed to investigate the workload change, and the prevalence and associated factors for depression symptoms among emergency medical staff after the policy adjustment. We conducted a cross-sectional online survey of emergency medical staff who fought against COVID-19 in Shandong Province during January 16 to 31, 2023. The respondents' sociodemographic and work information were collected, and they were asked to complete the 9-item Patient Health Questionnaire (PHQ-9) then. Univariate and multivariate logistic regression analyses were applied to identify the potential associated factors for major depression. Nine hundred and sixteen emergency medical personnel from 108 hospitals responded to this survey. The respondents' weekly working hours (53.65 ± 17.36 vs 49.68 ± 14.84) and monthly night shifts (7.25 ± 3.85 vs 6.80 ± 3.77) increased after the open policy. About 54.3% of the respondents scored more than 10 points on the PHQ-9 standardized test, which is associated with depressive symptoms. In univariate analysis, being doctors, living with family members aged ≤16 or ≥ 65 years old, COVID-19 infection and increased weekly working hours after the open policy were significantly associated with a PHQ-9 score ≥ 10 points. In the multivariate analysis, only increased weekly working hours showed significant association with scoring ≥10 points. Emergency medical staff' workload had increased after the open policy announcement, which was strongly associated with a higher PHQ-9 scores, indicating a very high risk for major depression. Emergency medical staff working as doctors or with an intermediate title from grade-A tertiary hospitals had higher PHQ-9 scores, while COVID-19 infection and weekly working hours of 60 or more after the open policy were associated with higher PHQ-9 scores for those from grade-B tertiary hospitals. Hospital administrators should reinforce the importance of targeted emergency medical staff support during future outbreaks.

Oral Health-Seeking Behavior During The COVID-19 Emergency Policy Period in Padang City, Indonesia

The increase in positive cases of Coronavirus Disease (COVID-19) has encouraged the Indonesian government to take lockdown policies by implementing Large-Scale Social Restrictions (PSBB) and Emergency Community Activity Restrictions (PPKM), which affect the paralysis of the health sector, especially dentistry. All actions in dental practice are limited to emergency case handling only, and many dentists have closed during the pandemic. The purpose of this study was to determine the differences in oral health-seeking behavior during the first phase of the lockdown in Padang City, West Sumatra, Indonesia. A survey method with a cross-sectional design was done by involving 120 respondents who have dental pain history. The data was collected by a self-administered questionnaire via google-form then analyzed by computerization using the fisher test. The results showed that during the PSBB period, most of the people in Padang (85%) treated dental pain independently, and only 15% asked for caregiver aids, specifically, only 3.3% of respondents take professional service. Meanwhile, during the PPKM period, the utilization of healthcare has increased by around 56,7%, (especially 43.4% treated by dental professionals, and 43.3% of respondents did self-medication. Thus, there was a significant difference of finding dental pain treatment behavior during PSBB and PPKM in Padang City (p = 0.004). As a result, limitation search for managing oral problems during COVID-19 has encouraged society to take self-medication or non-professional caregivers, so it is highly recommended to consult with dental professionals in the course of the pandemic through teledentistry without face-to-face meetings.

Forest-Based Employment in the Southern United States amidst the COVID-19 Pandemic: A Causal Inference Analysis

Abstract In response to the COVID-19 pandemic, some governments instituted mandatory stay-at-home policies. As these policies made exceptions for essential industries such as the forest sector, it is not clear a priori whether and how these policies would affect forest-based employment. This study examined the effect of mandatory stay-at-home orders on employment in the forest sector in forest dependent counties in eleven southern states in the United States. We estimated panel event study models in addition to difference-in-difference models to evaluate the policy effects with respect to initial treatment as well as average effects to the treated counties during treatment. We found that employment in wood product manufacturing was lower on average in counties under a lockdown order compared with employment in counties without an active order. We also found that employment in the aggregate forest sector in counties that implemented the policy was significantly lower than employment in counties without the policy up to 9 months following initial enactment compared with the underlying difference between treated and control counties one month prior to treatment. Overall, our findings suggest that lockdown policies had a limited effect on employment in essential industries such as wood-based manufacturing sectors in the southern states. Study Implications: This study uses causal inference techniques to assess the effect of lockdown orders on forest-based employment in 11 southern states. Results suggest effects of lockdown orders were limited to the aggregate forest sector and wood product manufacturing, likely due to the rapid designation of the sector as essential and the reduced production of sawmills. The other industries within the sector were not significantly affected, implying that industries designated as essential may be more resilient to short-term shocks. These findings can be used to inform discussions on potential policy responses to future disasters, including how different interventions interact with one another.

Pengaruh Time Interest Earned dan Earning Per Share Terhadap Harga Saham Perusahaan Rokok yang Listing di BEI

Cigarette companies are companies that belong to the primary purchaser items sector. The share prices of cigarette companies have continued to fall since the outbreak of Cocid-19. The lockdown policy has made cigarette companies experience a decline in performance. The observe targets to decide the impact of TIE and EPS on the share prices. The sample taken is a number of 4 companies acquired thru purposive sampling method. Before testing the hypothesis, the regression model is first tested with classical assumptions so that the data is feasible for testing the hypothesis. with a purpose to make sure that the facts are suitable for testing the hypothesis, the regression model is first tested using conventional assumptions. The analytical tools used are Microsoft Excel and SPSS 25 software. The results found that there is no partial effect of TIE on stock prices. Then there is the partial influence of EPS on stock prices. Simultaneously, TIE and EPS have an effect on stock prices.

On the fragility of the Italian economic territories under SARS-COV2 lockdown policies

AbstractWe leverage a granular representation of mobility patterns before and during the first wave of SARS-COV2 in Italy to investigate the economic consequences of various forms of lockdown policies when accounting for mobility restrictions between and within local jurisdictions, i.e. municipalities, provinces and regions. We provide an analytical characterization of the rate of economic losses using a network-based spectral method. The latter treats the spread of contagion of economic losses due to commuting restrictions as a dynamical system stability problem. Our results indicate that the interplay between lower level of smartworking and the polarization of commuting flows to fewer local labor hubs in the South of Italy makes Southern territories extremely important in spreading economic losses. We estimate an economic contraction of total income derived from commuting restrictions in the range of 10–30% depending on the economic assumptions. However, alternative policies proposed during the second wave of SARS-COV2 can pose a greater risk to Northern areas due to their higher degree of mobility between jurisdictions than Southern ones. The direction of economic losses tend to propagate from large to medium-small jurisdictions across all alternative lockdown policies we tested. Our study shows how complex mobility patterns can have unequal consequences to economic losses across the country and call for more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic output.

Changes in economic activity and mental distress among young adults during the COVID-19 pandemic: Differences between the first and second infection waves in the UK.

While infection rates, lockdown policies, and labor market conditions substantially varied across COVID-19 waves, the majority of evidence on young adults' mental health remains focused on initial responses in early 2020. The variability of the relationship between economic activity and mental health over time therefore remains poorly understood in this age group. Using linear mixed models, we investigated the relationship between current activity and changes in activity and mental distress (GHQ-12) among 1,390 young adults aged 16-34 via the UK Household Longitudinal Study COVID-19 survey. The association was explored in the first (from April to July 2020) and second (from September 2020 to March 2021) infection waves. Current activity was defined as "not working", "working <17.5 hours/week", "17.5-35 hours/week", and "> = 35 hours/week". Changes in activity were derived from current and pre-pandemic working hours and divided into four categories: "working with no reduced hours", "working fewer hours", "no longer working", and "did not work before the pandemic". During the first wave, no association reached statistical significance. During the second wave: 1) compared to "currently not working", working 35 or more hours was associated with decreased distress (b = -1.54; 95%CI -2.39, -0.69) and working less than 17.5 hours was not (b = -0.62; 95%CI -1.66, 0.41); 2) compared to "working with no reduced hours compared with before the outbreak", no longer working was associated with increased distress (b = 1.58, 95%CI 0.61, 2.55) and working with reduced hours was not (b = 0.47, 95%CI -0.24, 1.17). Above the mental health inequalities experienced at the start of the pandemic, full-time work-even with variation in work hours-continued to be a protective factor against mental distress among young adults during the second wave in the UK. Stable, full-time work can better support this age group's mental well-being over time.

Dynamic zero-COVID policy and healthcare utilization patterns in China during the Shanghai COVID-19 Omicron outbreak

BackgroundIn April 2022, an outbreak of the SARS-CoV-2 virus Omicron variant in Shanghai precipitated an extensive lockdown. We assessed changes in healthcare utilization during this outbreak and investigated the relationship between the stringency of mitigation strategies and disruptions in healthcare utilization.MethodsUsing provincial-level data from routine health information systems covering all hospitals across Mainland China, we conducted an interrupted time series analysis to examine changes in healthcare utilization during the Shanghai outbreak. Linear regression was used to evaluate the direction and magnitude of the association between the relative changes in the move-out movement index, a proxy for the stringency in population-level mitigation strategies, and the estimated relative changes in healthcare utilization.ResultsOverall, there were 22.9 billion outpatient visits and 1.2 billion discharged inpatients during the study period from January 2016 to May 2022, including 9.1 billion (39.7%) and 0.46 billion (38.2%) in the post-COVID-19 period (January 2020–May 2022), respectively. From March through May 2022, the outbreak resulted in an accumulative loss of 23.5 million (47%) outpatient visits and 0.6 million (55%) discharged inpatients in Shanghai, and a loss of 150.3 million (14%) outpatient visits and 3.6 million (7%) discharged inpatients in other regions. We find that for every 10-percentage point reduction in the relative change of move-out index, a 2.7 (95% CI: 2.0–3.4) percentage point decline in the relative change of outpatient visits, and a 4.3 (95% CI: 3.5–5.2) percentage points decline in the relative change of inpatient discharges.ConclusionsThe Shanghai COVID-19 Omicron outbreak associates with a substantial reduction in outpatient visits and inpatient discharges within Shanghai and other regions in China. The stringency of the COVID-19 lockdown policies associates with more profound reductions in healthcare utilization.

Diversification, vertical integration and economic resilience: evidence from intercity truck flows during COVID-19 in China

Abstract This article examines economic resilience by combining high-frequency truck flows and the lockdown policy shock during COVID-19 in China. We discover that the truck flows in regions with higher levels of diversification and vertical integration see a smaller decrease in response to the COVID-19 shock. Dynamically, such moderating effects of diversification and vertical integration get smaller with the recovery of interregional economic linkages. Diversification and integration also mitigate the negative impact from nonlocal infection cases. The association between the industrial structure attributes and economic resilience is more prominent in regions with lower centrality in the nationwide intercity truck flow network.

Environmental Perceptions of College Students during the COVID-19 Pandemic

In this study, we investigate the environmental perception changes in college students during the COVID-19 pandemic. During the almost worldwide lockdown between March and December 2020, many students experienced dramatic changes in their socioeconomic life that have affected their well-being, ability to learn, and environmental perceptions. Students were sent home from college campuses, many businesses were shut down, and changes were seen in the environment throughout the world. In many places, lockdown policies lead to improved air, water, and land quality, mainly due to fewer vehicle and anthropogenic emissions. An online survey conducted on students enrolled in the University of South Florida and Florida State University demonstrates that most students have become more environmentally conscious after the COVID-19 lockdown, indicating significant perception changes. The students’ demographic backgrounds played a major role in affecting their perception changes. Furthermore, mass media was found to have affected the student’s perceptions, which demonstrates the important role media plays in directing public opinion, especially among college students and millennia. This study also highlights the environmental issues that underrepresented students face and calls for policies to address them on both educational and socioeconomic levels.

Music as Magic

Music as Magic

Quantifying the heterogeneous impact of lockdown policies on different socioeconomic classes during the first COVID-19 wave in Colombia

In the absence of vaccines, the most widespread reaction to curb the COVID-19 pandemic worldwide was the implementation of lockdowns or stay-at-home policies. Despite the reported usefulness of such policies, their efficiency was highly constrained by socioeconomic factors determining their feasibility and their associated outcome in terms of mobility reduction and the subsequent limitation of social activity. Here we investigate the impact of lockdown policies on the mobility patterns of different socioeconomic classes in the three major cities of Colombia during the first wave of the COVID-19 pandemic. In global terms, we find a consistent positive correlation between the reduction in mobility levels and the socioeconomic stratum of the population in the three cities, implying that those with lower incomes were less capable of adopting the aforementioned policies. Our analysis also suggests a strong restructuring of the mobility network of lowest socioeconomic strata during COVID-19 lockdown, increasing their endogenous mixing while hampering their connections with wealthiest areas due to a sharp reduction in long-distance trips.

No One Wants to Hire Us: The intersectional precarity experienced by Venezuelan LGBTQ+ asylum seekers in Brazil during COVID-19

This article presents a case study of how COVID-19 has increased the precarity and risks of labour exploitation for vulnerable populations. Looking at the situation of LGBTQ+ Venezuelan asylum seekers in Brazil during COVID-19, it examines how the challenges they faced were exacerbated during the pandemic and how the Brazilian government’s poor response to COVID-19 and lockdown policies forced LGBTQ+ Venezuelan asylum seekers to take greater risks that exposed them to the virus. Based on 56 surveys with LGBTQ+ Venezuelan asylum seekers in Manaus, Brazil, the article discusses how COVID-19 impacted the livelihoods of LGBTQ+ Venezuelan asylum seekers. Specifically, it demonstrates that transgender and travesti Venezuelan asylum seekers experienced more labour precarity, discrimination, and violence during the pandemic than their cisgender counterparts.

Partisan Return Gap: The Polarized Stock Market in the Time of a Pandemic

Using two proxies for investors’ political affiliation, we document sharp differences in stock returns between firms likely dominated by Democratic investors (blue stocks) and those dominated by Republican investors (red stocks) during the COVID pandemic. Red stocks have 20 basis points higher risk-adjusted returns than blue stocks on COVID news days (Partisan Return Gap). Lockdown policies, COVID cases, industry and firm fundamentals only explain at most 40% of the return gap. Polarized political beliefs about COVID, revealed through people’s social distancing behavior, contribute to about 40% of the return gap beyond the fundamental channel. Our paper provides partisanship as a novel aspect in understanding abnormal stock returns during the pandemic. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The data and e-companion are available at https://doi.org/10.1287/mnsc.2023.4913 .

Government Support During COVID-19 and Corruption

We analyze the allocation and effectiveness of government support in response to the COVID-19 outbreak across a nationally representative sample of firms in 32 countries representing different levels of institutional transparency. The probability of receiving government support is higher for larger firms, firms belonging to business support groups and innovative firms in low corruption countries. In high corruption, countries firms competing against unregistered establishments, with lack of internationally recognized quality certification and no formalized business strategy are more likely to receive government support. Using the panel structure of the data to address reverse causality, selection bias and unobserved heterogeneity, we then find that government support improves firm-level outcomes more strongly in low corruption countries. Among different types of government support, we find wage subsidies to be more effective in high corruption countries while technical assistance for adoption of digital technologies in low corruption countries. In addition, social distancing and lockdown policies do not seem to be as effective in improving firm-level outcomes in high-corruption countries most likely because of weaker capacities to enforce such policies. These results show the importance of enhancing systems of accountability and enforcement procedures that will ensure that fiscal stimulus aid is deployed to benefit those who need it the most.