Large-scale Social Distancing Research Articles

Effect of Social Distancing Caused by Heavy Snowfall on Seasonal Influenza Epidemic in a Local Region in Japan.

The effectiveness of social distancing in controlling influenza epidemics is debatable. We analyzed the local impact of large-scale social distancing caused by heavy snowfall on influenza epidemic. Surveillance data from the National Institute of Infectious Diseases on influenza diagnosed at registered Fukui Prefecture institutions were plotted in epidemic curves. We also compared the number of elementary and junior high school students suspended attendance due to influenza during the 2017/2018 season between the northern and southern Fukui Prefecture territories. Weekly incidence influenza rates and average rates before, during and after school closure caused by heavy snowfall in the northern territory were compared with those of the southern territory using Poisson generalized estimating equations to account for school variability. The northern territory epidemic curve, like the nationwide trend, rapidly declined during the heavy snowfall and formed a second peak 4 weeks later. The southern territory's curve was like the nationwide trend throughout. Weekly incidence rate ratios in schools for the northern versus southern territories varied from 0.23 during heavy snowfall to 2.32, 4 weeks after the snowfall. Cumulative incidence ratio (95% confidence interval) of the northern-to-southern territories was 0.92 (0.74-1.13; P = 0.42). School closure significantly reduced the incidence rate to 0.41 (95% confidence interval, 0.19-0.89; P = 0.02). Although relatively large-scale, simultaneous school closures and social distancing could temporarily suppress seasonal influenza epidemic, they could not impede the cumulative incidence. Large-scale social distancing may contribute to slowing infection spread during seasonal influenza epidemics.

Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic.

The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we characterize collective physical distancing-mobility reductions, minimization of contacts, shortening of contact duration-in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. We define five indicators of users' mobility and proximity to investigate how the emerging collective behavior deviates from typical pre-pandemic patterns during the first nine months of the COVID-19 pandemic. We analyze both the dramatic changes due to the government mandated mitigation policies and the more spontaneous societal adaptation into a new (physically distanced) normal in the fall 2020. Using the indicators here defined we show that: a) during the COVID-19 pandemic, collective physical distancing displayed different phases and was heterogeneous across geographies, b) metropolitan areas displayed stronger reductions in mobility and contacts than rural areas; c) stronger reductions in commuting patterns are observed in geographical areas with a higher share of teleworkable jobs; d) commuting volumes during and after the lockdown period negatively correlate with unemployment rates; and e) increases in contact indicators correlate with future values of new deaths at a lag consistent with epidemiological parameters and surveillance reporting delays. In conclusion, this study demonstrates that the framework and indicators here presented can be used to analyze large-scale social distancing phenomena, paving the way for their use in future pandemics to analyze and monitor the effects of pandemic mitigation plans at the national and international levels.

Media Effect to Risk Perception and Protective Behavior During COVID-19 in Indonesia Partial Least Squares Modeling Analysis

Indonesia has implemented a Large-Scale Social Distancing policy, limiting major public activities to control the COVID-19 pandemic. This study investigates the media content information, the perception of its role, and its contribution to forming the general public’s risk perception and protective behavior during this situation. This study was an observational study using a cross-sectional design. Data was collected through an online semi-structured questionnaire using Google Forms. A total of 522 participants were obtained through snowball sampling for two weeks. The data analyzed used the Partial Least Squares (PLS) technique. The results showed the structural model of media content information and the perception of media’s role in influencing risk perception and protective behavior. Media content information and perception of media’s role directly influence protective behavior significantly. The only perception of the media’s role is that it significantly influences risk perception. Then, risk perception directly influences protective behavior. The structural model of media content information and perception of media’s role in influencing risk perception and protective behavior during COVID-19 when the transition period to the new normal era in Indonesia has been conducted.

Integrating technology in mental healthcare practice: A repeated cross-sectional survey study on professionals' adoption of Digital Mental Health before and during COVID-19.

As a consequence of the outbreak of the COVID-19 global pandemic in the spring of 2020, large-scale social distancing measures were implemented, resulting in the forced adoption of online or digital forms of psychological treatment. This sudden transition to digital care offered a unique opportunity to investigate if and how this experience impacted mental healthcare professionals' perceptions and use of Digital Mental Health tools. The current paper presents findings of a repeated cross-sectional study consisting of three iterations of a national online survey in the Netherlands. This survey contained open and closed questions on professionals' adoption readiness, frequency of use, perceived competency, and perceived value of Digital Mental Health collected in 2019 (before the pandemic), in 2020 (after the first wave), and in 2021 (after the second wave). The inclusion of data gathered prior to the COVID-19 pandemic offers a unique window to assess how professionals' adoption has developed through this transition from voluntary to mandatory use of Digital Mental Health tools. Our study also re-assesses the drivers, barriers, and needs of mental healthcare professionals after having gained experience with Digital Mental Health. In total, 1,039 practitioners completed the surveys (Survey 1: n = 432, Survey 2: n = 363, and Survey 3: n = 244). Results indicate that compared to the period before the pandemic, there was a particularly large increase in use, competency, and perceived value regarding videoconferencing. Small differences were also found for some other basic tools that were crucial to ensure the continuation of care, such as e-mail, text messaging, and online screening, but not for more innovative technologies, such as virtual reality and biofeedback. Many practitioners reported to have gained skills regarding Digital Mental Health and experienced several benefits of it. They expressed the intention to continue with a blended approach, using Digital Mental Health tools in combination with face-to-face care, focused on situations in which they found it to have specific added value, such as when clients are unable to travel. Others were less satisfied with the technology-mediated interactions and remained more reluctant to future use of DMH. Implications for broader implementation of Digital Mental Health and future research are discussed.

Reactive–diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China

The complex dynamics of human mobility, combined with sporadic cases of local outbreaks, make assessing the impact of large-scale social distancing on COVID-19 propagation in China a challenge. In this paper, with the travel big dataset supported by Baidu migration platform, we develop a reactive–diffusion epidemic model on human mobility networks to characterize the spatio-temporal propagation of COVID-19, and a novel time-dependent function is incorporated into the model to describe the effects of human intervention. By applying the system control theory, we discuss both constant and time-varying threshold behavior of proposed model. In the context of population mobility-mediated epidemics in China, we explore the transmission patterns of COVID-19 in city clusters. The results suggest that human intervention significantly inhibits the high correlation between population mobility and infection cases. Furthermore, by simulating different population flow scenarios, we reveal spatial diffusion phenomenon of cases from cities with high infection density to cities with low infection density. Finally, our model exhibits acceptable prediction performance using actual case data. The localized analytical results verify the ability of the PDE model to correctly describe the epidemic propagation and provide new insights for controlling the spread of COVID-19.

A photovoice study on youth’s experience of social distancing during the COVID-19 pandemic

ABSTRACT Objectives The coronavirus disease pandemic and social distancing measures have threatened young people living in an underdeveloped region of Indonesia. Very little is currently known about how youth experience social distancing during the pandemic. This study aimed to explore the lived experience of this group within a large-scale social distancing context. Design Data were collected using a photovoice and participatory video approach with 15 young people in West Timor, Indonesia. Results The results of the study explain motives for maintaining social distancing protocol, self-improvement activities during the quarantine, and changes that occurred in social dynamics. Conclusion Implications of the results are discussed with consideration of risk factors hindering the youth’s well-being.

Considering the temporal interdependence of human mobility and COVID-19 concerning Indonesia's large-scale social distancing policies.

The year 2020 has marked the beginning of a new life in which humans must struggle and adapt to coexist with a new coronavirus, known as COVID-19. Population density is one of the most significant factors affecting the speed of COVID-19’s spread, and it is closely related to human activity and movement. Therefore, many countries have implemented policies that restrict human movement to reduce the risk of transmission. This study aims to identify the temporal dependence between human mobility and virus transmission, indicated by the number of active cases, in the context of large-scale social restriction policies implemented by the Indonesian government. This analysis helps identify which government policies can significantly reduce the number of active COVID-19 cases in Indonesia. We conducted a temporal interdependency analysis using a time-varying Gaussian copula, where the parameter fluctuates throughout the observation. We use the percentage change in human mobility data and the number of active COVID-19 cases in Indonesia from March 28, 2020, to July 9, 2021. The results show that human mobility in public areas significantly influenced the number of active COVID-19 cases. Moreover, the temporal interdependencies between the two variables behaved differently according to the implementation period of large-scale social distancing policies. Among the five types of policies implemented in Indonesia, the policy that had the most significant influence on the number of active COVID-19 cases was several restrictions during the Implementation of Restrictions on Community Activities (Pelaksanaan Pembatasan Kegiatan Masyarakat/PPKM) period. We conclude that the strictness of rules restricting social activities generally affected the number of active COVID-19 cases, especially in the early days of the pandemic. Finally, the government can implement policies that are at least equivalent to the rules in PPKM if, in the future, cases of COVID-19 spike again.

Career practitioners' response to career development concerns in the time of COVID-19.

The COVID‐19 pandemic has had a world‐wide impact on all areas of individuals’ health, including physical, psychological, financial, familial, social, and vocational. In the United States, the unemployment rate rose from 3.5% (5.8 million) to 13.3% (21 million) in May 2020 before dropping to 7.9% in October 2020. Cognitive information processing (CIP)is one career theory that addresses career needs of clients and society. In this article, we examine the impact of COVID‐19 on mental health and wellness, highlight differences for marginalized groups, and demonstrate how CIP theoretical elements may have been impacted by COVID‐19, and provide strategies enhancing client growth in these domains during a time when largescale social and physical distancing is recommended. The CIP‐based differentiated service delivery model is also described as a means for extending and providing access to career services.

HOUSING PREFERENCES OF FULL NEST 1 LIFECYCLE STAGE CUSTOMERS IN RESPONSE TO THE PANDEMIC

The purpose of this study is to define customer preferences for housing, especially customers who belong to the Full Nest 1 group in a family’s life cycle. The subject examined in this study focuses on the interior design and architecture industry in Surabaya, East Java. As a result of COVID-19, there has been a massive impact on the development sector which affected these industries. This is because both industries relied on face-to-face interactions to function. However, after the application of new regulations as a response to the pandemic, several changes are to be made regarding client interaction and project management. The effect of large-scale social distancing has affected operations, and caused a shift in customer’s preferences. This study uses a quantitative approach, in which changes in customer preferences are obtained through interviews with relevant sources in order to narrow down prioritized factors. The data is then processed through factor analysis, and is distributed as a survey to 30 respondents representing the Surabaya region. The data collection process of this research is carried out with a ranking scale and processed using the Conjoint analysis to understand the current priorities of customers in response to the pandemic.

Contribution of robust optimization on handling agricultural processed products supply chain problem during Covid-19 pandemic

This research aims to show how decision sciences can make a significant contribution on handling the supply chain problem during Covid-19 Pandemic. The paper discusses how robust optimization handles uncertain demand in agricultural processed products supply chain problems within two scenarios during the pandemic situation, i.e., the large-scale social distancing and partial social distancing. The study assumes that demand and production capacity are uncertain during a pandemic situation. Robust counterpart methodology is employed to obtain the robust optimal solution. To this end, the uncertain data is assumed to lie within a polyhedral uncertainty set. The result shows that the robust counterpart model is a computationally tractable through linear programming problem. Numerical experiment is presented for the Bandung area with a case on sugar and cooking oil that is the most influential agricultural processed products besides the main staple food of the Indonesian people, rice.

Optimization Model for Agricultural Processed Products Supply Chain Problem in Bandung During Covid-19 Period

Coronavirus disease, commonly called Covid-19, is a virus that causes a pandemic in almost every country globally. One of those countries is Indonesia, which has many big cities with dense populations. This study was conducted in Bandung, the capital of West Java, Indonesia. As a result of the Covid-19 pandemic, Bandung was seriously affected in various ways. One was the disruption in the distribution of the agricultural processed products supply chain, which changes producers and consumers' behaviour. Furthermore, as an effort by the government to break the spread of the virus, health protocols limit the distribution. The purpose of this study is to design an optimization model for the supply chain problem of agricultural processed products in Bandung during the Covid-19 period with the objective function is maximizing product suppliers so that all demands on consumers are fulfilled. The use of Local Food Hub (LFH) is a help in this research as a distribution centre point between the producer zone and the consumer zone. Finally, numerical experiments were carried out in two scenarios, namely Large-scale Social Distancing (LSD) and Partial Social Distancing (PSD). It was found that the optimal distribution solution was obtained if the PSD scenario was applied.

Examining Indonesian Government Strategies in the Aviation Sector Post Covid-19 Pandemic

Aviation is an inseparable part of transportation in any country. The aviation sector is one of the sectors most affected by the Covid-19 pandemic. Indirectly, the spread of the Covid-19 virus can occur in airlines, which are the fast transportation options for passengers. Indonesia has taken a policy to stop its aviation sector from reducing the spread of the Covid-19 virus and prioritize the health of its citizens. The drastically reduced flight frequency without clarity of time impacts many losses to the aviation industry. The Indonesian government plans a gradual recovery effort in the aviation sector through domestic flights. However, there are concerns from the Indonesian people, as airline passengers who will be traveling, regarding their health safety on flights during the large-scale social distancing period during the Covid-19 pandemic. Therefore, this study will discuss the strategies of the Indonesian government in the aviation sector after the Covid-19 pandemic to still prioritize the health of its citizens. This study uses the theory of the foreign policy decision-making process. The method used in this study is a qualitative based research method. This study concludes that the strategies taken by the Indonesian government include reducing 50% of passenger capacity to enforce safe distances, sanitizing aircraft, and for prospective passengers to be required to show a certificate of negative Covid-19 results through a PCR swab test or GeNose C19.

PEMANFAATAN INTERNET BERBASIS SWADAYA MASYARAKAT UNTUK AKTIVITAS PEMBELAJARAN DARING SISWA SEKOLAH

This research is motivated by the occurrence of the COVID-19 pandemic so that the government has implemented large-scale social distancing, one of the areas affected by this application in the field of education. so that learning that was previously carried out in the classroom has now become online learning so that new problems arise in the field. This study aims to determine the use of self-help-based internet on the effectiveness of online learning for school students. The theoretical basis in this research 1. Theory of Online Learning 2. Theory of Self-help 3. Theory of Community Education. The data collection technique used a qualitative approach with the type of case study research and tend of descriptive. The instruments of this research are interviews, observations, and literature studies. This research was conducted at RT 02 RW 08 Sukatani Village, Ngamprah District, West Bandung Regency. Samples were taken of 5 people, namely 1 manager, 2 parents, and 2 students The results showed that the existence of non-government-based internet can increase the efficiency and effectiveness of students in online learning as well as save costs.

Mode Choice Modelling Between Bus and Train Under the New Normal Condition

The passenger movements were limited by the government policies that made new system decisions, namely large-scale social distancing policies. However, over time several regions in Indonesia have begun to end large-scale social distancing called new normal. The new normal condition has undoubtedly changed the pattern of mode choice of the passenger. Little attention has been paid to the travel mode choice under the new normal condition. Therefore, this study aims to understand the travel mode choice model of train and bus, especially in the new normal era. The primary data was collected using the stated preference online-based survey. This study performed a Binomial-Logit-Difference model. From the modelling result, 89% of the passenger will choose the bus if the train's travel fare is IDR 160,000 higher. The probability value will be equal when the ticket fare of the bus is IDR 25,000 higher than train’s travel cost. It indicates that people choose the bus mode because of the travel cost factor. Directions for the future study are presented

Software preference for online learning of science and biology teachers under COVID-19 pandemic

As measure against the rapid spreading of coronavirus disease 2019 (COVID-19) which now has reached global level, Indonesian government established Large-scale Social Distancing (LsSD). As consequence, learning method used in junior and senior high school is substituted from face-to-face learning in class to online distance learning, including for science and biology. This study was conducted to know software preference used by science and biology junior and senior high school teachers for online learning during LsSD measure. A total of 189 science and biology junior and senior high school teachers from various area had given their response via questionnaire. Data was analyzed using quantitative descriptive method. About 57% respondents had never manage online learning before COVID-19 pandemic while the remaining 43% had experience in managing one before, however almost all managed online learning. Non-paid software used the most (81%) among respondents to manage online science/biology learning. Software types used were social networking (64%) especially WhatsApp, learning management system (LMS) (51%) especially Google Classroom, teleconference applications (12%), and assessment software outside of LMSs (15%). Software chosen were mostly non-paid, easily accessed by all people, already familiar among Indonesian, and its interface were easily mastered.

A photovoice study on Indonesian youth’s experience in the ‘new normal’ era

ABSTRACT A shrink in Indonesian economic growth forced the authorities to ease their large-scale social distancing protocol during the coronavirus disease pandemic and introduced the ‘new normal’ protocol starting May 2020. This period is perceived to be not effective in reducing the spread of the virus and left many vulnerable youths exposed to various risk factors that negatively affected their well-being. This study aimed to understand the experience of youth living in the era of ‘new normal’ in Indonesia. Fifteen youth participated in interviews and photovoice session. Thematic resulted in three main themes, which is return to routines, changes in social life, and youth as agents of change. Policymakers are suggested to consider social changes in these youth and empower them to become a beacon in their community during this dark period of a pandemic.

Big Data Analysis of COVID-19 Mitigation Policy in Indonesia: Democratic, Elitist, and Artificial Intelligence

In response to mitigate COVID-19 local transmission, the Indonesian government has launched several regulative policies. This paper aims to analyze whether the decision-making process of those regulative policies is closed to a democratic or elitist approach and whether the government has adopted Artificial Intelligence (AI) in these processes. We focus on large-scale social distancing (PSBB), homecoming restriction in the Eid Mubarak (Mudik), and new-normal life policy case studies. The research has shown that elite roles are quite dominant with little consideration for public consultation and little attention to the use of AI to consider public preferences. The data were collected from two well-known national online media, which are nasional.kompas.com and tempo.co, while the big data mining and analysis have been applied through N-Vivo 12 software. We argue that although the elitist paradigm can be recognized as an efficient approach in the pandemic and emergency, the decision-makers can accommodate public preferences if they can get insights from online media news with the help of AI technology.

Http://jurnal.umsu.ac.id/index.php/interaksi/article/view/4949

Social media Twitter as a means of providing information at this time, during the Covid-19 pandemic, the Government's announcement, in this case, the Twitter account from the Ministry of Health of the Republic of Indonesia, will become a public reference. This study uses the Nvivo12 Plus technology application to obtain data. This study reveals the use of social media Twitter about spreading social media information on Twitter with (hashtag) # COVID19, # Covid19Indonesia, and Twitter accounts from the Ministry of Health of the Republic of Indonesia. Qualitative methods are used to find a social media model that is used as a mass communication channel. The data results show that many intense social media campaigns discuss all the impacts of the Coronavirus (Covid-19) and discussions about selling masks and hand sanitizer at relatively high prices. The Ministry of Health's Twitter account provides information related to prevention and motivates the public always to implement social distancing, physical distancing, large-scale social distancing (PSBB), stay at home, don't return home, stay clean, and always wash their hands with soap and hand sanitizer, and use the face mask

Survival analysis with the Cox Proportional Hazard Method to determine the factors that affect how long the Large-Scale Social Distancing (LSSD) will applied in various areas affected by the covid-19 pandemic

This study will determine the length of time for large-scale social restrictions, in Indonesia is known as the Large-Scale Social Distancing (LSSD), which requires in a region to reduce the number of different covid-19 cases. Associated with the implementation of the LSSD, it turns out that many things can influence the LSSD to be able to run effectively in the community, from internal and external factors. This research was conducted for 15 days from 1 June 2020 to 15 June 2020, to find out the factors that had a significant influence on the effectiveness of LSSD using Cox Proportional Hazard Regression. The dependent variable used in the study is the length of time the LSSD system (Y). From the results can concluded that the socialization variable (X1) has a significant effect to the effectiveness of LSSD.

The COVID-19 Pandemic and Indonesia’s Fintech Markets

This paper investigates the impacts of the COVID-19 pandemic and the large-scale social distancing (PSBB) policy on Indonesia’s financial technology (fintech) markets. It also elaborates the roles that fintech companies can play in the national economic recovery.This paper finds that Indonesia’s fintech markets were relatively resilient during the COVID-19 pandemic. The pandemic did not have significant impacts on Indonesia’s fintech markets, but the PSBB harmed phone banking, mobile banking, and internet banking transaction values as well as peer-to-peer (P2P) fintech lending. Nevertheless, the PSBB increased electronic money transactions. The relatively short PSBB period prevented the restrictions on economic activities from imposing too much damage on the fintech markets.The Indonesian authorities involved the fintech industry as a component of the national economic recovery program (PEN), particularly the pre-employment card (Kartu Prakerja) program. There are still many areas in which the government can utilize the fintech industry for economic recovery, including direct cash transfers to poor households and extensions of subsidized loans for micro, small, and medium enterprises (MSMEs).