scholarly journals Optimal Mitigation Policies in a Pandemic: Social Distancing and Working from Home

Author(s):  
Callum Jones ◽  
Thomas Philippon ◽  
Venky Venkateswaran

Abstract We study an economy’s response to an unexpected epidemic. The spread of the disease can be mitigated by reducing consumption and hours worked in the office. Working from home is subject to learning-by-doing. Private agents’ rational incentives are relatively weak and fatalistic. The planner recognizes infection and congestion externalities and implements front-loaded mitigation. Under our calibration, the planner reduces cumulative fatalities by 48% compared to 24% by private agents, although with a sharper drop in consumption. Our model can replicate key industry and/or occupational-level patterns and explain how large variations in outcomes across regions can stem from small initial differences.

Author(s):  
Randa Diab-Bahman

Once exposed, the COVID-19 pandemic created unprecedented pressure on all sectors causing many temporarily closures and organizations working from home. Daily norms were interrupted and further complicated with the declaration of quarantine curfews worldwide. One major sector which has been greatly impacted is the education sector. Due to the nature of its complicated infrastructure, all stakeholders were heavily affected as the world turned to online learning for solutions. By doing so, many educational institutes were able to continue with their teaching, even with strict social distancing measures in place. Although remote learning is not a new concept in the education sector, it is a new concept in Kuwait. In this chapter, a thorough review is given on the strategy which Kuwait's universities adopted as they prepared for distance learning for the first time throughout the country. Khan's 8-element VLE model will be used as a reference.


2020 ◽  
Vol 55 (3) ◽  
pp. 142-147 ◽  
Author(s):  
Armanda Cetrulo ◽  
Dario Guarascio ◽  
Maria Enrica Virgillito

2020 ◽  
Vol 81 (7) ◽  
pp. 357
Author(s):  
Wendi Kaspar

The preponderance of the articles in July issue of College & Research Libraries deal with topics related to technology. I note this with some irony as, due to social distancing and working from home, much of our work is happening through technology. Heck, it seems like our entire lived experience right now is computer-mediated! There has been a translation of the analog work experience into digital with Zoom meetings and email/chat consultations, not to mention the changes with the day-to-day minutiae with everything from ordering food, clothing, or other necessities (toilet paper?) online to binge-watching and gaming in order to keep from going crazy with shelter-in-place orders to watching YouTube videos to stay in shape (my youngest daughter is now talking about bringing back Jazzercise?!).


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hsien-Yen Chang ◽  
Wenze Tang ◽  
Elham Hatef ◽  
Christopher Kitchen ◽  
Jonathan P. Weiner ◽  
...  

Abstract Background The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage. Methods This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately. Results On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation. Conclusions Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.


2020 ◽  
Author(s):  
Hsien-Yen Chang ◽  
Wenze Tang ◽  
Elham Hatef ◽  
Christopher Kitchen ◽  
Jonathan P. Weiner ◽  
...  

AbstractBackgroundThe spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage.MethodsThis was a comparative interrupted time-series analysis at the county level. We included 2,087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index, derived from the COVID-19 Impact Analysis Platform, to measure the social distancing practice. For the evaluation of implementation, the observation started from Mar 1 St 2020 to one day before lifting; and, for lifting, it ranged from one day after implementation to Jul 5 th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately.ResultsOn both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation.ConclusionsNeighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.


2021 ◽  
Author(s):  
Naiara C.M. Valiati ◽  
Daniel A.M. Villela

SummaryThe perspective of vaccination to protect human population from infection of SARS-CoV-2 virus has great potential to control the pandemic. Nevertheless, vaccine planning requires phased introduction with age groups, health workers, and vulnerable people. We developed a mathematical model capable of capturing the dynamics of the SARS-CoV-2 dissemination aligned with social distancing, isolation measures, and vaccination. The city of Rio de Janeiro provides a case study to analyze possible scenarios including non–pharmaceutical interventions and vaccination in the epidemic scenario. Our results shows that a combination of different policies such as case isolation and social distancing are more effective for mitigating the epidemics. Furthermore, these policies will still be necessary in a phased vaccination program. Therefore, health surveillance activities should be maintained along with vaccination planning in scheduled groups until a large vaccinated coverage is reached.


Author(s):  
Hao Yin ◽  
Zhu Liu ◽  
Daniel M. Kammen

BackgroundCOVID-19 has caused an unprecedented public health crisis and economic shock to the global economy. While many countries were affected, regions with an older population and weaker public health interventions tended to suffer more morbidity and mortality. Here we model and quantify the age-specific incidence of COVID-19 in four pandemic cities under different interventions.MethodsWe developed an age-specific and multiple-stage susceptible-exposed-infected-recovered-hospitalized-quarantined-dead (SEIR-HQD) dynamical systems model expanded from the more basic SEIR model by incorporating location- and age-specific contact matrices to estimate the outcomes of COVID-19. Utilizing latest estimates of epidemiological parameters and demographic data, we model the potential effects of various interventions in four representative cities with different population structures - New York, Los Angeles, Daegu and Nairobi. We compared the effects of different interventions in the age-structure populations specific to each city. These policy options are then applied to determine the potential for effective containment. We model these dynamic policy scenarios to assess the risks of less-stringent social distancing, as has been proposed by those arguing to enhance economic activity over public health and safety. Finally, we explored the health impacts of different policy action timelines to understand the benefits of early interventions.FindingsWe find the spread of COVID-19 to be dramatically different in the regions modeled, with the primary drivers the variation of population age structures, and the dynamics of interactions of the younger demographics, whose higher interaction rates can lead to increasing transmission rates across these communities. A city with younger citizens may also have fewer hospitalized cases and deaths. Our modeling quantifies the value of early interventions, which avoided an additional 5%, 16%, 37% and 43% of the infections in Daegu, Nairobi, New York and Los Angeles, respectively, compared to what has been observed in the four cities. The finding is clear: in the absence of pharmaceutical options, delaying strict social policy interventions has resulted in substantial public health cost. This modeling can, and will, be applied to other cities and regions, and conducted in conjunction with other health insults, such as exposure to air pollution.Critically, we find that school closures, working from home, and reduction in other mobility were most beneficial for younger population (0-19 years old), middle-age (20-59 years old) population and older population (60 years and older), respectively across each city. Specifically, school closure avoided 25%, 18%, 16% and 12% of the infections for the population under 20 years old in Daegu, Los Angeles, New York and Nairobi, respectively. A 50% and 80% population working from home policy avoids 8% and 15% of the infections. Reduction in mobility was more effective than the working from home strategy. Any single social distancing policy if enacted alone can delay the spread of COVID-19 but was unable to totally suppress the infection. Coordinated policy action can be highly effective. Increasing the quarantine rate to 10% of infectious cases was more effective than strict social distancing alone in this study, although together they can suppress 80% of the epidemic. A combination of moderate social distancing and quarantine strategies was able to avoid 99% of the infections.InterpretationModerate social distancing together with high quarantine rates was effective in each of the four cities. COVID-19 caused more deaths and hospitalization in cities with an ageing population than those with a younger population. However, in the cities with a younger population, there is a clear need to implement a social distancing strategy that is even more strict due to the higher transmission rates among younger people. Cities with more older people should prepare more hospital beds and healthcare facilities to save people who are in critical conditions. Cities with ageing population should take targeted action for the elderly to avoid the severe impacts on the vulnerable populations. Increasing quarantine rate is an effective strategy to avoid the substantial infection while also does not influence the economy fiercely. We recommend countries or regions experiencing, or likely to experience the rapid spread of COVID-19, to implement combination of multiple strategies in the early stage of the breakout which can avoid over 90% of infected cases.FundingNational Natural Science Foundation of China, China Postdoctoral Science Foundation, Qiushi Foundation and the Resnick Sustainability Institute at California Institute of Technology, Zaffaroni Family Foundation, the Karsten Family Foundation, the National Science Foundation of the United States.


Author(s):  
Rachel Heath ◽  
Ghazala Mansuri ◽  
Bob Rijkers ◽  
William Seitz ◽  
Dhiraj Sharma

Abstract Using a randomized survey experiment in urban Ghana, this paper demonstrates that the length of the reference period and the interview modality (in-person or over the phone) affect how people respond in labor surveys, with impacts varying markedly by job type. Survey participants report significantly more self-employment spells when the reference period is shorter than the traditional one week, with the impacts concentrated among those in home-based and mobile self-employment. In contrast, the reference period has no impact on the incidence of wage-employment. The wage-employed do report working fewer days and hours when confronted with a shorter reference period. Finally, interviews conducted on the phone yield lower estimates of employment, hours worked, and days worked among the self-employed who are working from home or a mobile location as compared to in-person interviews.


Author(s):  
BAKITGUL E. BORANKULOVA ◽  
◽  
ZOYA G. PROSHINA ◽  

This article aims to collect, group and conduct a linguistic study of coroneologisms - new words and phrases formed during the Covid-19 outbreak, to analyze their translation into Kazakh and determine the potential for their use in the language. The materials for the study were taken from (i) the websites of a national Kazakh newspaper, (ii) an official site of the Ministry of Healthcare of the Republic of Kazakhstan and its instagram, (iii) English-language newspapers, (iv) materials from online Merriam-Webster Dictionary. Continuous sampling was used to select the tokens for the study. Having analyzed new words and expressions related to coronavirus, their usage in English and translation into Kazakh, we can say that some comparatively new words and expressions that describe a specific situation (lockdown, WFH (working from home), social distancing, etc.) belong to particular professional vocabulary and have already been introduced into the dictionaries in many languages...


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