Relaxation Of Social Distancing Research Articles

Process evaluation of a university residence-based SARS-CoV-2 testing programme in the UK

Abstract Background Regular testing for SARS-CoV-2 is an important strategy for controlling virus outbreaks on university campuses during the COVID-19 pandemic but testing participation can be low. The Residence-Based Testing Participation Pilot (RB-TPP) was a novel 4-week intervention implemented at two student residences on a UK university campus, aiming to increase asymptomatic testing frequency and normalise university life through relaxed social restrictions onsite. Methods Mixed-methods process evaluation determined whether RB-TPP was implemented as planned and identified implementation barriers and facilitators. Data were collected from meeting records, university students (online survey: n = 152; focus groups: n = 30), and staff (interviews, n = 13). Barriers and facilitators to implementation were mapped to the ‘Capability, Opportunity, Motivation-Behaviour’ (COM-B) behaviour change framework. Results Uptake was high (n = 464 students opted-in; 98% of those living onsite). Implementation was broadly as planned, with adjustments due to national escalation of the COVID-19 Delta variant. Majority engaged in testing (88%); 46% (52% of testers) were fully compliant with pre-determined testing frequency. Most felt positively towards relaxed social distancing (97.9%). Implementation was facilitated by convenience and efficiency of testing and reduced negative impacts of isolation through opportunities for students to socialise. Barriers to implementation were mixed-messages about the rules, ambivalent attitudes, and lack of adherence to COVID-19 protective measures in the minority. Conclusions This is the first process evaluation of the implementation of asymptomatic SARS-CoV-2 testing in university residences. Testing participation increased and student mental wellbeing improved. Rapid adaptions to the changing pandemic context generated complexity and challenge. Findings have global relevance for outbreak prevention and management strategies in higher education settings. Key messages • Delivery of asymptomatic SARS-CoV-2 testing and relaxation of social distancing within residences led to high rates of testing participation and benefits for student mental wellbeing. • This is the first process evaluation of the implementation of asymptomatic SARS-CoV-2 testing in university residences with global relevance for outbreak prevention in higher education settings.

The COVID-19 Pandemic, Stay-at-Home Orders, and Gun Violence: A Story of Two Cities

This study examines the impact of the pandemic on gun violence in Philadelphia and Washington DC. Interrupted time-series analysis is used to examine weekly data from January 2017 to March 2021. Robust diagnostic checks confirm the validity of the fitted models. There were significant increases in gun violence during the pandemic, especially in the staged relaxation of social distancing. The timing of the increases in gun violence varies by location and fatality. Criminal justice agencies should place more attention and reallocate resources on gun violence in a timely manner in the volatile state of the nation. Finally, this study concludes with a discussion of the findings, limitations, and implications for future research.

Relationship between COVID-19 cases and vaccination rates in New York State counties.

Context: The presence of new viral variants, in combination with the relaxation of social distancing and other preventative measures, has led to a spike in COVID-19 cases in the United States. The development of COVID-19 vaccinations may reduce the impact of these viral variants on case rates in the population. Objective: To determine the impact of COVID-19 vaccination rates on cases/100k population in each New York State (NYS) county. Study Design: Cross-sectional analysis of COVID-19 cases/100k population per NYS county, frozen at a single snapshot in time. Descriptive statistics and bivariate correlations were conducted to determine vaccination rates across 62 NYS counties, and linear regression was used to examine the effect of vaccination rates on cases/100k, controlling for size of county population. Dataset & Setting: Vaccination rates per county shared by the NYS Department of Health using data reported to the NYS Immunization Information System and the New York City Citywide Immunization Registry. COVID-19 case rates per county available through the John Hopkins University website. Population: NYS residents across 62 counties on March 31st, 2021. Outcome Measures: COVID-19 vaccination rates across counties at a single point in time were compared with cases/100k population. Results: Percentages with 1 dose and with 2 doses are highly correlated (r=.935, p<.001) with one another, and county population size was strongly correlated with cases per 100k (r=.715, p<.001). Both the 1 dose and 2 dose rates were negatively correlated with cases per 100k population, although not significantly. However, the two-dose vaccination rate was a significant negative predictor of cases per 100k population in NYS Counties (β= -.866, p=.031), with each percentage point of completed vaccination nearly equating to one case less in the daily count, when controlling for county population size (β =2.732, p<.001). Conclusion: While COVID-19 variants may impact vaccine effectiveness, current vaccination efforts are helping forestall some cases in NYS. Widespread vaccination is still an important goal. Primary care providers, public officials, and public health scientists should continue to urgently promote and support vaccination efforts.

COVID-19 and Gun Violence: Keeping Unknown Shocks and Volatility in Perspective.

The current study estimates the varying effects of the pandemic on gun violence by social distancing type, fatality, and location. Interrupted time series analyses are used to examine weekly crime data from 2016 to 2020 in New York City. Box-Cox power transformation and GARCH techniques are used to address the problems of non-normality and heteroscedasticity in the models. There were significant increases in fatal and non-fatal shootings during the relaxation of social distancing. The impact of the BLM protests and depolicing is significant for non-fatal shootings. The pandemic led to greater increases in gun violence in The Bronx, Brooklyn, Manhattan, and Queens, as opposed to Staten Island. In addition, there is some evidence of increases in the volatility of gun violence during the pandemic. High volatility implies crime rates are in severe flux, which then leads to greater uncertainty and fear for public safety. This paper surfaces useful information for guiding policy and practice.

A BEHAVIORAL CHANGE MODEL TO ASSESS VACCINATION-INDUCED RELAXATION OF SOCIAL DISTANCING DURING AN EPIDEMIC

The success of mass vaccination campaigns may be jeopardized by human risky behaviors. For example, high level of vaccination coverage may induce early relaxation of social distancing. In this paper, we focus on the mutual influence between the decline in prevalence, due to the rise in the overall immunization coverage, and the consequent decrease in the compliance to social distancing measures. We consider an epidemic model where both the vaccination rate and the disease transmission rate are influenced by human behavior, which in turn depends on the current and past information about the spread of the disease. We highlight the impact of the information-related parameters on the transient and asymptotic behavior of the system that is on the early stage of the epidemic and its final outcome. Among the main results, we evidence that sustained oscillations may be triggered by the behavioral memory in the prevalence-dependent vaccination rate. However, the relaxation of social distancing may induce a switch from a cyclic regime to damped oscillations.

Evolution of resistance to COVID-19 vaccination with dynamic social distancing.

The greatest hope for a return to normalcy following the COVID-19 pandemic is worldwide vaccination. Yet, a relaxation of social distancing that allows increased transmissibility, coupled with selection pressure due to vaccination, will probably lead to the emergence of vaccine resistance. We analyse the evolutionary dynamics of COVID-19 in the presence of dynamic contact reduction and in response to vaccination. We use infection and vaccination data from six different countries. We show that under slow vaccination, resistance is very likely to appear even if social distancing is maintained. Under fast vaccination, the emergence of mutants can be prevented if social distancing is maintained during vaccination. We analyse multiple human factors that affect the evolutionary potential of the virus, including the extent of dynamic social distancing, vaccination campaigns, vaccine design, boosters and vaccine hesitancy. We provide guidelines for policies that aim to minimize the probability of emergence of vaccine-resistant variants.

Exploring violent crimes in Chicago during the COVID-19 pandemic: do location, crime type, and social distancing type matter?

ABSTRACT The current study estimates the impact of the SAH order on violent crimes across public and residential locations: assault, battery, homicide, robbery, and sexual assault. Using interrupted time series analyses, it analyzes weekly crime data in Chicago, Illinois, from 2017 to 2020. The SAH order caused significant decreases in battery and sexual assault across public and residential locations. It also decreased assault in public locations only. Such decreases in assault, battery, and sexual assault were greater under the SAH order when social distancing was strictly enforced, as opposed to during the relaxation of social distancing. On the other hand, there were significant increases in homicide across public and residential locations. Robbery increased in public locations only. There were greater increases in homicide and robbery during the relaxation of social distancing, as opposed to under the SAH order. The study ultimately indicates that the impacts of the containment measures are conditional upon the offense location, type of crime, and level of social distancing being mandated. From a public policy perspective, it is important to allocate staffing and resources for law enforcement accordingly during the enduring pandemic.

Modeling serological testing to inform relaxation of social distancing for COVID-19 control

Serological testing remains a passive component of the public health response to the COVID-19 pandemic. Using a transmission model, we examine how serological testing could have enabled seropositive individuals to increase their relative levels of social interaction while offsetting transmission risks. We simulate widespread serological testing in New York City, South Florida, and Washington Puget Sound and assume seropositive individuals partially restore their social contacts. Compared to no intervention, our model suggests that widespread serological testing starting in late 2020 would have averted approximately 3300 deaths in New York City, 1400 deaths in South Florida and 11,000 deaths in Washington State by June 2021. In all sites, serological testing blunted subsequent waves of transmission. Findings demonstrate the potential benefit of widespread serological testing, had it been implemented in the pre-vaccine era, and remain relevant now amid the potential for emergence of new variants.

A generalised SEIRD model with implicit social distancing mechanism: A Bayesian approach for the identification of the spread of COVID-19 with applications in Brazil and Rio de Janeiro state

ABSTRACT We develop a generalized Susceptible--Exposed--Infected--Removed--Dead (SEIRD) model considering social distancing measures to describe the COVID-19 spread in Brazil. We assume uncertain scenarios with limited testing capacity, lack of reliable data, under-reporting of cases, and restricted testing policy. A Bayesian framework is proposed for the identification of model parameters and uncertainty quantification of the model outcomes. We identify through sensitivity analysis (SA) that the model parameter related to social distancing measures is one of the most influential. Different relaxation strategies of social distancing measures are then investigated to determine which are viable and less hazardous to the population. The scenario of abrupt social distancing relaxation implemented after the peak of positively diagnosed cases can prolong the epidemic. A more severe scenario occurs if a social distancing relaxation policy is implemented prior to the evidence of epidemiological control, indicating the importance of the appropriate choice of when to start the relaxation.

Epidemiological modelling of the health and economic effects of COVID-19 control in Australia’s second wave

BackgroundWe investigated the public health and economy outcomes of different levels of social distancing to control a ‘second wave’ outbreak in Australia and identify implications for public health management of COVID-19.MethodsIndividual-based and compartment models were used to simulate the effects of different social distancing and detection strategies on Australian COVID-19 infections and the economy from March to July 2020. These models were used to evaluate the effects of different social distancing levels and the early relaxation of suppression measures, in terms of public health and economy outcomes.ResultsThe models, fitted to observations up to July 2020, yielded projections consistent with subsequent cases and showed that better public health outcomes and lower economy costs occur when social distancing measures are more stringent, implemented earlier and implemented for a sufficiently long duration. Early relaxation of suppression results in worse public health outcomes and higher economy costs.ConclusionsBetter public health outcomes (reduced COVID-19 fatalities) are positively associated with lower economy costs and higher levels of social distancing; achieving zero community transmission lowers both public health and economy costs compared to allowing community transmission to continue; and early relaxation of social distancing increases both public health and economy costs.

Covid-19 in the Northeast of Brazil: from lockdown to the relaxation of social distancing measures

Even in the period when the Covid-19 pandemic was on the rise in the Northeast of Brazil, the relaxation of social distancing measures was introduced. The scope of the study is to assess, in the light of the epidemiological-sanitary situation in the region, the suitability of relaxation of social distancing measures. Based on the WHO guidelines for relaxation of social distancing, operational indicators were created and analyzed for each guideline in the context of the Northeast. To analyze the behavior of the epidemic, according to selected indicators, Joinpoint trend analysis techniques, heat maps, rate ratios and time trends between capitals and the state interior were compared. The weekly growth peak of the epidemic occurred in May-July 2020 (epidemiological weeks 19 to 31). In most capitals, there was no simultaneous downward trend in the number of cases and deaths in the 14 days prior to flexibilization. In all states the number of tests performed was insufficient. In epidemiological week 24, the state percentages of ICU/Covid-19 bed occupancy were close to or above 70%. The epidemiological situation of the nine Northeastern state capitals analyzed here did not meet criteria and parameters recommended by the World Health Organization for the relaxation of social distancing measures.

On the Transmission Dynamics of SARS-CoV-2 in a Temperate Climate.

An epidemiological model, which describes the transmission dynamics of SARS-CoV-2 under specific consideration of the incubation period including the population with subclinical infections and being infective is presented. The COVID-19 epidemic in Greece was explored through a Monte Carlo uncertainty analysis framework, and the optimal values for the parameters that determined the transmission dynamics could be obtained before, during, and after the interventions to control the epidemic. The dynamic change of the fraction of asymptomatic individuals was shown. The analysis of the modelling results at the intra-annual climatic scale allowed for in depth investigation of the transmission dynamics of SARS-CoV-2 and the significance and relative importance of the model parameters. Moreover, the analysis at this scale incorporated the exploration of the forecast horizon and its variability. Three discrete peaks were found in the transmission rates throughout the investigated period (15 February–15 December 2020). Two of them corresponded to the timing of the spring and autumn epidemic waves while the third one occurred in mid-summer, implying that relaxation of social distancing and increased mobility may have a strong effect on rekindling the epidemic dynamics offsetting positive effects from factors such as decreased household crowding and increased environmental ultraviolet radiation. In addition, the epidemiological state was found to constitute a significant indicator of the forecast reliability horizon, spanning from as low as few days to more than four weeks. Embedding the model in an ensemble framework may extend the predictability horizon. Therefore, it may contribute to the accuracy of health risk assessment and inform public health decision making of more efficient control measures.

Relaxation of social distancing restrictions: Model estimated impact on COVID-19 epidemic in Manitoba, Canada.

The unprecedented worldwide social distancing response to COVID-19 resulted in a quick reversal of escalating case numbers. Recently, local governments globally have begun to relax social distancing regulations. Using the situation in Manitoba, Canada as an example, we estimated the impact that social distancing relaxation may have on the pandemic. We fit a mathematical model to empirically estimated numbers of people infected, recovered, and died from COVID-19 in Manitoba. We then explored the impact of social distancing relaxation on: (a) time until near elimination of COVID-19 (< one case per million), (b) time until peak prevalence, (c) proportion of the population infected within one year, (d) peak prevalence, and (e) deaths within one year. Assuming a closed population, near elimination of COVID-19 in Manitoba could have been achieved in 4-6 months (by July or August) if there were no relaxation of social distancing. Relaxing to 15% of pre-COVID effective contacts may extend the local epidemic for more than two years (median 2.1). Relaxation to 50% of pre-COVID effective contacts may result in a peak prevalence of 31-38% of the population, within 3-4 months of initial relaxation. Slight relaxation of social distancing may immensely impact the pandemic duration and expected peak prevalence. Only holding the course with respect to social distancing may have resulted in near elimination before Fall of 2020; relaxing social distancing to 15% of pre-COVID-19 contacts will flatten the epidemic curve but greatly extend the duration of the pandemic.

From Spanish Flu to Syndemic COVID-19: long-standing sanitarian vulnerability of Manaus, warnings from the Brazilian rainforest gateway.

A second deadlier wave of COVID-19 and the causes of the recent public health collapse of Manaus are compared with the Spanish flu events in that city, and Brazil. Historic sanitarian problems, and its hub position in the Brazilian airway network are combined drivers of deadly events related to COVID-19. These drivers were amplified by misleading governance, highly transmissible variants, and relaxation of social distancing. Several of these same factors may also have contributed to the dramatically severe outbreak of H1N1 in 1918, which caused the death of 10% of the population in seven months. We modelled Manaus parameters for the present pandemic and confirmed that lack of a proper social distancing might select the most transmissible variants. We succeeded to reproduce a first severe wave followed by a second stronger wave. The model also predicted that outbreaks may last for up to five and half years, slowing down gradually before the disease disappear. We validated the model by adjusting it to the Spanish Flu data for the city, and confirmed the pattern experienced by that time, of a first stronger wave in October-November 1918, followed by a second less intense wave in February-March 1919.

The Patterns of Stationary Activities during COVID-19 Distancing Relaxation: The elevated pedestrian network of Mong Kok, Hong Kong

COVID-19 is expected to impact the low-income groups' use of public space and related quality of life beyond the current pandemic outbreak. To what extent may the current pandemic affect the use of public space once some restrictions will be lifted? This study focuses on the migrant domestic workers’ spatio-temporal changes in the patterns of public space use during social distancing relaxation period in Hong Kong. The findings highlight increase of individual leisure activities, decrease of density around informal food-production and of gathering group, comparatively to the pre-pandemic situation. Keywords: Covid-19, public space, migrant domestic workers, behavioural mapping eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5i15.2461.

Modelling the Potential Impact of Social Distancing on the COVID-19 Epidemic in South Africa.

The novel coronavirus (COVID-19) pandemic continues to be a global health problem whose impact has been significantly felt in South Africa. With the global spread increasing and infecting millions, containment efforts by countries have largely focused on lockdowns and social distancing to minimise contact between persons. Social distancing has been touted as the best form of response in managing a rapid increase in the number of infected cases. In this paper, we present a deterministic model to describe the impact of social distancing on the transmission dynamics of COVID-19 in South Africa. The model is fitted to data from March 5 to April 13, 2020, on the cumulative number of infected cases, and a scenario analysis on different levels of social distancing is presented. The model shows that with the levels of social distancing under the initial lockdown level between March 26 and April 13, 2020, there would be a projected continued rise in the number of infected cases. The model also looks at the impact of relaxing the social distancing measures after the initial announcement of the lockdown. It is shown that relaxation of social distancing by 2% can result in a 23% rise in the number of cumulative cases whilst an increase in the level of social distancing by 2% would reduce the number of cumulative cases by about 18%. The model results accurately predicted the number of cases after the initial lockdown level was relaxed towards the end of April 2020. These results have implications on the management and policy direction in the early phase of the epidemic.

Coronavirus Disease 2019 (COVID-19) Transmission in the United States Before Versus After Relaxation of Statewide Social Distancing Measures.

BackgroundWeeks after issuing social distancing orders to suppress severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and reduce growth in cases of severe coronavirus disease 2019 (COVID-19), all US states and the District of Columbia partially or fully relaxed these measures.Methods We identified all statewide social distancing measures that were implemented and/or relaxed in the United States between 10 March and 15 July 2020, triangulating data from state government and third-party sources. Using segmented linear regression, we estimated the extent to which relaxation of social distancing affected epidemic control, as indicated by the time-varying, state-specific effective reproduction number (Rt).ResultsIn the 8 weeks prior to relaxation, mean Rt declined by 0.012 units per day (95% confidence interval [CI], −.013 to −.012), and 46/51 jurisdictions achieved Rt < 1.0 by the date of relaxation. After relaxation of social distancing, Rt reversed course and began increasing by 0.007 units per day (95% CI, .006–.007), reaching a mean Rt of 1.16. Eight weeks later, the mean Rt was 1.16 and only 9/51 jurisdictions were maintaining an Rt < 1.0. Parallel models showed similar reversals in the growth of COVID-19 cases and deaths. Indicators often used to motivate relaxation at the time of relaxation (eg, test positivity rate <5%) predicted greater postrelaxation epidemic growth.ConclusionsWe detected an immediate and significant reversal in SARS-CoV-2 epidemic suppression after relaxation of social distancing measures across the United States. Premature relaxation of social distancing measures undermined the country’s ability to control the disease burden associated with COVID-19.

Mathematical Parameters of the COVID-19 Epidemic in Brazil and Evaluation of the Impact of Different Public Health Measures.

A SIRU-type epidemic model is employed for the prediction of the COVID-19 epidemy evolution in Brazil, and analyze the influence of public health measures on simulating the control of this infectious disease. The proposed model allows for a time variable functional form of both the transmission rate and the fraction of asymptomatic infectious individuals that become reported symptomatic individuals, to reflect public health interventions, towards the epidemy control. An exponential analytical behavior for the accumulated reported cases evolution is assumed at the onset of the epidemy, for explicitly estimating initial conditions, while a Bayesian inference approach is adopted for the estimation of parameters by employing the direct problem model with the data from the first phase of the epidemy evolution, represented by the time series for the reported cases of infected individuals. The evolution of the COVID-19 epidemy in China is considered for validation purposes, by taking the first part of the dataset of accumulated reported infectious individuals to estimate the related parameters, and retaining the rest of the evolution data for direct comparison with the predicted results. Then, the available data on reported cases in Brazil from 15 February until 29 March, is used for estimating parameters and then predicting the first phase of the epidemy evolution from these initial conditions. The data for the reported cases in Brazil from 30 March until 23 April are reserved for validation of the model. Then, public health interventions are simulated, aimed at evaluating the effects on the disease spreading, by acting on both the transmission rate and the fraction of the total number of the symptomatic infectious individuals, considering time variable exponential behaviors for these two parameters. This first constructed model provides fairly accurate predictions up to day 65 below 5% relative deviation, when the data starts detaching from the theoretical curve. From the simulated public health intervention measures through five different scenarios, it was observed that a combination of careful control of the social distancing relaxation and improved sanitary habits, together with more intensive testing for isolation of symptomatic cases, is essential to achieve the overall control of the disease and avoid a second more strict social distancing intervention. Finally, the full dataset available by the completion of the present work is employed in redefining the model to yield updated epidemy evolution estimates.

Timing social distancing to avert unmanageable COVID-19 hospital surges.

Following the April 16, 2020 release of the Opening Up America Again guidelines for relaxing coronavirus disease 2019 (COVID-19) social distancing policies, local leaders are concerned about future pandemic waves and lack robust strategies for tracking and suppressing transmission. Here, we present a strategy for triggering short-term shelter-in-place orders when hospital admissions surpass a threshold. We use stochastic optimization to derive triggers that ensure hospital surges will not exceed local capacity and lockdowns are as short as possible. For example, Austin, Texas-the fastest-growing large city in the United States-has adopted a COVID-19 response strategy based on this method. Assuming that the relaxation of social distancing increases the risk of infection sixfold, the optimal strategy will trigger a total of 135 d (90% prediction interval: 126 d to 141 d) of sheltering, allow schools to open in the fall, and result in an expected 2,929 deaths (90% prediction interval: 2,837 to 3,026) by September 2021, which is 29% of the annual mortality rate. In the months ahead, policy makers are likely to face difficult choices, and the extent of public restraint and cocooning of vulnerable populations may save or cost thousands of lives.

The COVID-19 Pandemic-Can open access modeling give us better answers more quickly?

The COVID-19 Pandemic-Can open access modeling give us better answers more quickly?