COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.
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PROVIDER: S-EPMC7654910 | BioStudies |
REPOSITORIES: biostudies
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Project description:The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, which is home to University of Michigan and Eastern Michigan University, and in close proximity to Detroit, MI, a major epicenter of the epidemic in Michigan. We apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons or long term care facilities). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases of COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. We find that delaying reopening does not reduce the magnitude of the second peak of cases, but only delays it. Reducing levels of casual contact, on the other hand, both delays and lowers the second peak. Through simulations and sensitivity analyses, we explore mechanisms driving the magnitude and timing of a second wave of infections upon re-opening. We find that the most significant factors are workplace and casual contacts and protective measures taken by infected individuals who have sought care. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.
| S-EPMC7455546 | BioStudies
Project description:Marissa Renardy and Denise Kirschner University of Michigan Medical School The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. These heteroge- neous patterns are also observed within individual states, with patches of concentrated outbreaks. Data is being generated daily at all of these spatial scales, and answers to questions regarded re- opening strategies are desperately needed. Mathematical modeling is useful in exactly these cases, and using modeling at a county scale may be valuable to further predict disease dynamics for the purposes of public health interventions. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, the home to University of Michigan, Eastern Michigan University, and Google, as well as serving as a sister city to Detroit, MI where there has been a serious outbreak. Here, we apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact net- works based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We also perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases on COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. Through simulations and sensitivity analyses, we explore mechanisms driving magnitude and timing of a second wave of infections upon re-opening. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.
| S-EPMC7359538 | BioStudies
Project description:To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.
| S-EPMC7796058 | BioStudies
Project description:In early 2020 many countries closed schools to mitigate the spread of SARS-CoV-2. Since then, governments have sought to relax the closures, engendering a need to understand associated risks. Using address records, we construct a network of schools in England connected through pupils who share households. We evaluate the risk of transmission between schools under different reopening scenarios. We show that whilst reopening select year-groups causes low risk of large-scale transmission, reopening secondary schools could result in outbreaks affecting up to 2.5 million households if unmitigated, highlighting the importance of careful monitoring and within-school infection control to avoid further school closures or other restrictions.
| S-EPMC8007691 | BioStudies
Project description:<h4>Background</h4>Significant uncertainty has existed about the safety of reopening college and university campuses before the COVID-19 pandemic is better controlled. Moreover, little is known about the effects that on-campus students may have on local higher-risk communities.<h4>Objective</h4>We aimed to estimate the range of potential community and campus COVID-19 exposures, infections, and mortality under various university reopening plans and uncertainties.<h4>Methods</h4>We developed campus-only, community-only, and campus × community epidemic differential equations and agent-based models, with inputs estimated via published and grey literature, expert opinion, and parameter search algorithms. Campus opening plans (spanning fully open, hybrid, and fully virtual approaches) were identified from websites and publications. Additional student and community exposures, infections, and mortality over 16-week semesters were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outliers. Sensitivity analyses were conducted to inform potential effective interventions.<h4>Results</h4>Predicted 16-week campus and additional community exposures, infections, and mortality for the base case with no precautions (or negligible compliance) varied significantly from their medians (4- to 10-fold). Over 5% of on-campus students were infected after a mean of 76 (SD 17) days, with the greatest increase (first inflection point) occurring on average on day 84 (SD 10.2 days) of the semester and with total additional community exposures, infections, and mortality ranging from 1-187, 13-820, and 1-21 per 10,000 residents, respectively. Reopening precautions reduced infections by 24%-26% and mortality by 36%-50% in both populations. Beyond campus and community reproductive numbers, sensitivity analysis indicated no dominant factors that interventions could primarily target to reduce the magnitude and variability in outcomes, suggesting the importance of comprehensive public health measures and surveillance.<h4>Conclusions</h4>Community and campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Public health implications include the need for effective surveillance and flexible campus operations.
| S-EPMC8030657 | BioStudies
Project description:<h4>Background</h4>The novel coronavirus disease 2019 (COVID-19) infected over 5 million United States (US) residents resulting in more than 180,000 deaths by August 2020. To mitigate transmission, most states ordered shelter-in-place orders in March and reopening strategies varied.<h4>Objective</h4>To estimate excess COVID-19 cases and deaths after reopening compared with trends prior to reopening for two groups of states: (1) states with an evidence-based reopening strategy, defined as reopening indoor dining after implementing a statewide mask mandate, and (2) states reopening indoor dining rooms before implementing a statewide mask mandate.<h4>Design</h4>Interrupted time series quasi-experimental study design applied to publicly available secondary data.<h4>Participants</h4>Fifty United States and the District of Columbia.<h4>Interventions</h4>Reopening indoor dining rooms before or after implementing a statewide mask mandate.<h4>Main measures</h4>Outcomes included daily cumulative COVID-19 cases and deaths for each state.<h4>Key results</h4>On average, the number of excess cases per 100,000 residents in states reopening without masks is ten times the number in states reopening with masks after 8 weeks (643.1 cases; 95% confidence interval (CI)?=?406.9, 879.2 and 62.9 cases; CI?=?12.6, 113.1, respectively). Excess cases after 6 weeks could have been reduced by 90% from 576,371 to 63,062 and excess deaths reduced by 80% from 22,851 to 4858 had states implemented mask mandates prior to reopening. Over 50,000 excess deaths were prevented within 6 weeks in 13 states that implemented mask mandates prior to reopening.<h4>Conclusions</h4>Additional mitigation measures such as mask use counteract the potential growth in COVID-19 cases and deaths due to reopening businesses. This study contributes to the growing evidence that mask usage is essential for mitigating community transmission of COVID-19. States should delay further reopening until mask mandates are fully implemented, and enforcement by local businesses will be critical for preventing potential future closures.
| S-EPMC7537575 | BioStudies
Project description:School closures due to the COVID-19 global pandemic are likely to have a range of negative consequences spanning the domains of child development, education and health, in addition to the widening of inequalities and inequities. Research is required to improve understanding of the impact of school closures on the education, health and wellbeing of pupils and school staff, the challenges posed during face-to-face reopening and importantly to identify how the impacts of these challenges can be addressed going forward to inform emerging policy and practice. This qualitative study aimed to reflect on the perspectives and experiences of primary school staff (pupils aged 3-11) in Wales regarding school closures and the initial face-to-face reopening of schools and to identify recommendations for the future. A total of 208 school staff completed a national online survey through the HAPPEN primary school network, consisting of questions about school closures (March to June 2020), the phased face-to-face reopening of schools (June to July 2020) and a return to face-to-face education. Thematic analysis of survey responses highlighted that primary school staff perceive that gaps in learning, health and wellbeing have increased and inequalities have widened during school closures. Findings from this study identified five recommendations; (i) prioritise the health and wellbeing of pupils and staff; (ii) focus on enabling parental engagement and support; (iii) improve digital competence amongst pupils, teachers and parents; (iv) consider opportunities for smaller class sizes and additional staffing; and (v) improve the mechanism of communication between schools and families, and between government and schools.
| S-EPMC8639092 | BioStudies
Project description:Purpose To determine the nature and prevalence of workers' concerns regarding workplaces reopening during the pandemic. To identify characteristics of workers and industries where particular concerns are more common. Method Prospective cohort study of 1063 employed Australian adults, enrolled at the start of the pandemic. Data on attitudes to workplaces reopening were collected 1 July-30 September 2020. The frequency of concerns describes infection risk and changes to work and impact on home life. Regression models examined associations between demographic and industry factors with reopening concerns. Results More than four in five (82.4%) of workers reported concerns about workplace infection risk. Just over half (53.4%) reported concerns about impacts to work and home life. Concerns were more prevalent for workers reporting psychological distress, financial stress, and among those exclusively working from home. Concerns regarding infection risk were common for workers in health care (IRR 1.16, 95% CI [1.01, 1.33]), retail (IRR 1.31, 95% CI [1.06, 1.61]), and accommodation/food service industries (IRR 1.25, 95% CI [1.01, 1.55]). Concerns regarding changes to work and home life were more common for female workers (IRR 1.24, 95% CI [1.07, 1.43]), and partners/spouses with dependent children (IRR 1.44, 95% CI [1.16, 1.79]). Conclusion Concerns of COVID-19 infection in the workplace are common. Many workers are also concerned about changes to their work and home life. The prevalence of concerns is related to the nature of work and responsibilities at home. Actions that reduce risk of workplace transmission, coupled with effective communication of infection controls, may alleviate worker concerns whilst recognising workers' family and social circumstances.
| S-EPMC8204924 | BioStudies
Project description:The COVID-19 pandemic required significant public health interventions from local governments. Although nonpharmaceutical interventions often were implemented as decision rules, few studies evaluated the robustness of those reopening plans under a wide range of uncertainties. This paper uses the Robust Decision Making approach to stress-test 78 alternative reopening strategies, using California as an example. This study uniquely considers a wide range of uncertainties and demonstrates that seemingly sensible reopening plans can lead to both unnecessary COVID-19 deaths and days of interventions. We find that plans using fixed COVID-19 case thresholds might be less effective than strategies with time-varying reopening thresholds. While we use California as an example, our results are particularly relevant for jurisdictions where vaccination roll-out has been slower. The approach used in this paper could also prove useful for other public health policy problems in which policymakers need to make robust decisions in the face of deep uncertainty.
| S-EPMC8547648 | BioStudies
Project description:<h4>Background</h4>To suppress the COVID-19 outbreak, the Norwegian government closed all schools on March 13, 2020. The kindergartens reopened on April 20, and the schools on April 27 and May 11 of 2020. The effect of these measures is largely unknown since the role of children in the spread of the SARS-CoV-2 virus is still unclear. There are only a few studies of school closures as a separate intervention to other social distancing measures, and little research exists on the effect of school opening during a pandemic.<h4>Objective</h4>This study aimed to model the effect of opening kindergartens and the schools in Norway in terms of a change in the reproduction number (R). A secondary objective was to assess if we can use the estimated R after school openings to infer the rates of transmission between children in schools.<h4>Methods</h4>We used an individual-based model (IBM) to assess the reopening of kindergartens and schools in two Norwegian cities, Oslo, the Norwegian capital, with a population of approximately 680 000, and Tromsø, which is the largest city in Northern Norway, with a population of approximately 75 000. The model uses demographic information and detailed data about the schools in both cities. We carried out an ensemble study to obtain robust results in spite of the considerable uncertainty that remains about the transmission of SARS-CoV-2.<h4>Results</h4>We found that reopening of Norwegian kindergartens and schools are associated with a change in R of 0.10 (95%CI 0.04-0.16) and 0.14 (95%CI 0.01-0.25) in the two cities under investigation if the in-school transmission rates for the SARS-CoV-2 virus are equal to what has previously been estimated for influenza pandemics.<h4>Conclusion</h4>We found only a limited effect of reopening schools on the reproduction number, and we expect the same to hold true in other countries where nonpharmaceutical interventions have suppressed the pandemic. Consequently, current R-estimates are insufficiently accurate for determining the transmission rates in schools. For countries that have closed schools, planned interventions, such as the opening of selected schools, can be useful to infer general knowledge about children-to-children transmission of SARS-CoV-2.
| S-EPMC7906341 | BioStudies