Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Impacts of COVID-19 social distancing policies on water demand: A population dynamics perspective.

ABSTRACT: Social distancing policies (SDPs) implemented in response to the COVID-19 pandemic have led to temporal and spatial shifts in water demand across cities. Water utilities need to understand these demand shifts to respond to potential operational and water-quality issues. Aided by a fixed-effects model of citywide water demand in Austin, Texas, we explore the impacts of various SDPs (e.g., time after the stay home-work safe order, reopening phases) using daily demand data gathered between 2013 and 2020. Our approach uses socio-technical determinants (e.g., climate, water conservation policy) with SDPs to model water demand, while accounting for spatial and temporal effects (e.g., geographic variations, weekday patterns). Results indicate shifts in behavior of residential and nonresidential demands that offset the change at the system scale, demonstrating a spatial redistribution of water demand after the stay home-work safe order. Our results show that some phases of Texas's reopening phases had statistically significant relationships to water demand. While this yielded only marginal net effects on overall demand, it underscores behavioral changes in demand at sub-system spatial scales. Our discussions shed light on SDPs' impacts on water demand. Equipped with our empirical findings, utilities can respond to potential vulnerabilities in their systems, such as water-quality problems that may be related to changes in water pressure in response to demand variations.

SUBMITTER: Bakchan A

PROVIDER: S-EPMC8519786 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Impacts of social distancing policies on mobility and COVID-19 case growth in the US.

Project description:Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Using anonymized and aggregated mobility data from opted-in Google users, we found that state-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 24.5% reduction in mobility the following week, and subsequent shelter-in-place mandates yielded an additional 29.0% reduction. Decreases in mobility were associated with substantial reductions in case growth two to four weeks later. For example, a 10% reduction in mobility was associated with a 17.5% reduction in case growth two weeks later. Given the continued reliance on social distancing policies to limit the spread of COVID-19, these results may be helpful to public health officials trying to balance infection control with the economic and social consequences of these policies.

| S-EPMC8149701 | biostudies-literature

Social Distancing Compliance under COVID-19 Pandemic and Mental Health Impacts: A Population-Based Study.

Project description:The success of public health measures for controlling the coronavirus disease 2019 (COVID-19) pandemic relies on population compliance. We analyzed compliance with social distancing and its associations with mental health. The Hong Kong COVID-19 Health Information Survey was conducted from 9-23 April 2020 on 1501 adults randomly sampled for landline telephone interviews (n = 500) and online surveys (n = 1001). Compliance with social distancing and staying-at-home, stress (Perceived Stress Scale-4), anxiety (General Anxiety Disorders-2), and depressive symptoms (Patient Health Questionnaire-2) were collected. The associations between mental health symptoms and compliance were examined by multivariable regression models. Of the 1501 respondents (52.5% female, 72.3% aged 18-59 years), 74.2%, 72.7%, and 59.7% reported avoiding going out, going to crowded places, and attending social gatherings of more than four people, respectively. Most respondents had stayed-at-home for at least four of the past seven days (58.4%; mean 4.12, Standard Deviation 2.05). Adoption, perceived effectiveness, and perceived compliance with social distancing were associated with lower stress levels and less anxiety and depressive symptoms (all p < 0.01). However, more days stayed-at-home were associated with more depressive symptoms (adjusted Odds Ratio 1.09; 95%Confidence Interval 1.00, 1.18). The long-term psychological impact in relation to social distancing and staying-at-home requires further investigation.

| S-EPMC7560229 | biostudies-literature

State variation in effects of state social distancing policies on COVID-19 cases.

Project description:BackgroundThe novel coronavirus disease 2019 (COVID-19) sickened over 20 million residents in the United States (US) by January 2021. Our objective was to describe state variation in the effect of initial social distancing policies and non-essential business (NEB) closure on infection rates early in 2020.MethodsWe used an interrupted time series study design to estimate the total effect of all state social distancing orders, including NEB closure, shelter-in-place, and stay-at-home orders, on cumulative COVID-19 cases for each state. Data included the daily number of COVID-19 cases and deaths for all 50 states and Washington, DC from the New York Times database (January 21 to May 7, 2020). We predicted cumulative daily cases and deaths using a generalized linear model with a negative binomial distribution and a log link for two models.ResultsSocial distancing was associated with a 15.4% daily reduction (Relative Risk = 0.846; Confidence Interval [CI] = 0.832, 0.859) in COVID-19 cases. After 3 weeks, social distancing prevented nearly 33 million cases nationwide, with about half (16.5 million) of those prevented cases among residents of the Mid-Atlantic census division (New York, New Jersey, Pennsylvania). Eleven states prevented more than 10,000 cases per 100,000 residents within 3 weeks.ConclusionsThe effect of social distancing on the infection rate of COVID-19 in the US varied substantially across states, and effects were largest in states with highest community spread.

| S-EPMC8237534 | biostudies-literature

How effective are social distancing policies? Evidence on the fight against COVID-19.

Project description:To fight the spread of COVID-19, many countries implemented social distancing policies. This is the first paper that examines the effects of the German social distancing policies on behavior and the epidemic's spread. Exploiting the staggered timing of COVID-19 outbreaks in extended event-study models, we find that the policies heavily reduced mobility and contagion. In comparison to a no-social-distancing benchmark, within three weeks, the policies avoided 84% of the potential COVID-19 cases (point estimate: 499.3K) and 66% of the potential fatalities (5.4K). The policies' relative effects were smaller for individuals above 60 and in rural areas.

| S-EPMC8457454 | biostudies-literature

Economic considerations for social distancing and behavioral based policies during an epidemic.

Project description:Public policies intended to induce behavioral change, specifically incentives to reduce interpersonal contacts or to "social distance," increasingly play a prominent role in public disease response strategies as governments plan for and respond to major epidemics. I compare social distancing incentives and outcomes under decentralized, full control social planner, and constrained social planner, without health class specific control, decision making scenarios. Constrained social planner decision making, based on non-health class specific controls, can in some instances make society worse off than decentralized decision making (i.e. no intervention). The oft neglected behavior of recovered and immune individuals is important for welfare and health outcomes.

| S-EPMC3659402 | biostudies-other

Sociocultural impacts of COVID-19: A social representations perspective.

Project description:While COVID-19's public health and economic impacts are evident, its sociocultural impacts are often overlooked. This study takes the case of the Italian destination of Pisa during the early stages of the pandemic - when most infection cases were assumed to be imported from China - and investigates Social Representations of Chinese tourists from an Italian host perspective. Through fifteen interviews with local suppliers, two co-existing Social Representations are identified and expressed through Italian literary metaphors: "the Hen with Golden Eggs" and "the Anointer". This research shows that, while the dominant hegemonic representation is rooted in rationality, the emerging polemic representation is anchored in pre-Enlightenment sociocultural fears. Finally, the uncertain future of post-COVID19 tourism considering potential stereotypes, xenophobia and racism is discussed.

| S-EPMC8634773 | biostudies-literature

A decision analytic approach for social distancing policies during early stages of COVID-19 pandemic.

Project description:The COVID-19 pandemic has become a crucial public health problem in the world that disrupted the lives of millions in many countries including the United States. In this study, we present a decision analytic approach which is an efficient tool to assess the effectiveness of early social distancing measures in communities with different population characteristics. First, we empirically estimate the reproduction numbers for two different states. Then, we develop an age-structured compartmental simulation model for the disease spread to demonstrate the variation in the observed outbreak. Finally, we analyze the computational results and show that early trigger social distancing strategies result in smaller death tolls; however, there are relatively larger second waves. Conversely, late trigger social distancing strategies result in higher initial death tolls but relatively smaller second waves. This study shows that decision analytic tools can help policy makers simulate different social distancing scenarios at the early stages of a global outbreak. Policy makers should expect multiple waves of cases as a result of the social distancing policies implemented when there are no vaccines available for mass immunization and appropriate antiviral treatments.

| S-EPMC8233412 | biostudies-literature

Mental health during the COVID-19 pandemic: Effects of stay-at-home policies, social distancing behavior, and social resources.

Project description:Social distancing is the most visible public health response to the COVID-19 pandemic, but its implications for mental health are unknown. In a nationwide online sample of 435 U.S. adults, conducted in March 2020 as the pandemic accelerated and states implemented stay-at-home orders, we examined whether stay-at-home orders and individuals' personal distancing behavior were associated with symptoms of depression, generalized anxiety disorder (GAD), intrusive thoughts, insomnia, and acute stress. Stay-at-home order status and personal distancing were independently associated with higher symptoms, beyond protective effects of available social resources (social support and social network size). A subsample of 118 participants who had completed symptom measures earlier in the outbreak (February 2020) showed increases in depression and GAD between February and March, and personal distancing behavior was associated with these increases. Findings suggest that there are negative mental health correlates of social distancing, which should be addressed in research, policy, and clinical approaches to the COVID-19 pandemic.

| S-EPMC7439968 | biostudies-literature

Predicting COVID-19 county-level case number trend by combining demographic characteristics and social distancing policies.

Project description:ObjectivePredicting daily trends in the Coronavirus Disease 2019 (COVID-19) case number is important to support individual decisions in taking preventative measures. This study aims to use COVID-19 case number history, demographic characteristics, and social distancing policies both independently/interdependently to predict the daily trend in the rise or fall of county-level cases.Materials and methodsWe extracted 2093 features (5 from the US COVID-19 case number history, 1824 from the demographic characteristics independently/interdependently, and 264 from the social distancing policies independently/interdependently) for 3142 US counties. Using the top selected 200 features, we built 4 machine learning models: Logistic Regression, Naïve Bayes, Multi-Layer Perceptron, and Random Forest, along with 4 Ensemble methods: Average, Product, Minimum, and Maximum, and compared their performances.ResultsThe Ensemble Average method had the highest area-under the receiver operator characteristic curve (AUC) of 0.692. The top ranked features were all interdependent features.ConclusionThe findings of this study suggest the predictive power of diverse features, especially when combined, in predicting county-level trends of COVID-19 cases and can be helpful to individuals in making their daily decisions. Our results may guide future studies to consider more features interdependently from conventionally distinct data sources in county-level predictive models. Our code is available at: https://doi.org/10.5281/zenodo.6332944.

| S-EPMC9278037 | biostudies-literature

COVID-19 and crime: Analysis of crime dynamics amidst social distancing protocols.

Project description:In response to the pandemic in early 2020, cities implemented states of emergency and stay at home orders to reduce virus spread. Changes in social dynamics due to local restrictions impacted human behavior and led to a shift in crime dynamics. We analyze shifts in crime types by comparing crimes before the implementation of stay at home orders and the time period shortly after these orders were put in place across three cities. We find consistent changes across Chicago, Baltimore, and Baton Rouge with significant declines in total crimes during the time period immediately following stay at home orders. The starkest differences occurred in Chicago, but in all three cities the crime types contributing to these declines were related to property crime and statutory crime rather than interpersonal crimes.

| S-EPMC8016314 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data