scholarly journals The impact of phased university reopenings on mitigating the spread of COVID-19: a modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lior Rennert ◽  
Corey A. Kalbaugh ◽  
Christopher McMahan ◽  
Lu Shi ◽  
Christopher C. Colenda

Abstract Background Several American universities have experienced COVID-19 outbreaks, risking the health of their students, employees, and local communities. Such large outbreaks have drained university resources and forced several institutions to shift to remote learning and send students home, further contributing to community disease spread. Many of these outbreaks can be attributed to the large numbers of active infections returning to campus, alongside high-density social events that typically take place at the semester start. In the absence of effective mitigation measures (e.g., high-frequency testing), a phased return of students to campus is a practical intervention to minimize the student population size and density early in the semester, reduce outbreaks, preserve institutional resources, and ultimately help mitigate disease spread in communities. Methods We develop dynamic compartmental SARS-CoV-2 transmission models to assess the impact of a phased reopening, in conjunction with pre-arrival testing, on minimizing on-campus outbreaks and preserving university resources (measured by isolation bed capacity). We assumed an on-campus population of N = 7500, 40% of infected students require isolation, 10 day isolation period, pre-arrival testing removes 90% of incoming infections, and that phased reopening returns one-third of the student population to campus each month. We vary the disease reproductive number (Rt) between 1.5 and 3.5 to represent the effectiveness of alternative mitigation strategies throughout the semester. Results Compared to pre-arrival testing only or neither intervention, phased reopening with pre-arrival testing reduced peak active infections by 3 and 22% (Rt = 1.5), 22 and 29% (Rt = 2.5), 41 and 45% (Rt = 3.5), and 54 and 58% (improving Rt), respectively. Required isolation bed capacity decreased between 20 and 57% for values of Rt ≥ 2.5. Conclusion Unless highly effective mitigation measures are in place, a reopening with pre-arrival testing substantially reduces peak number of active infections throughout the semester and preserves university resources compared to the simultaneous return of all students to campus. Phased reopenings allow institutions to ensure sufficient resources are in place, improve disease mitigation strategies, or if needed, preemptively move online before the return of additional students to campus, thus preventing unnecessary harm to students, institutional faculty and staff, and local communities.

2020 ◽  
Author(s):  
Lior Rennert ◽  
Corey Kalbaugh ◽  
Christopher McMahan ◽  
Lu Shi ◽  
Christopher C Colenda

Introduction: Recent outbreaks of COVID-19 in universities across the United States highlight the difficulties in containing the spread of COVID-19 on college campuses. While research has shown that mitigation strategies such as frequent student testing, contact tracing, and isolation of confirmed and suspected cases can detect early outbreaks, such mitigation strategies may have limited effectiveness if large outbreaks occur. A phased reopening is a practical intervention to limit early outbreaks, conserve institutional resources, and ensure proper safety protocols are in place before the return of additional students to campus. Methods: We develop dynamic compartmental transmission models of SARS-CoV-2 to assess the impact of a phased reopening and pre-arrival testing on minimizing outbreaks (measured by daily infections) and conserving university resources (measured by isolation bed capacity). We assume that one-third of the student population returns to campus each month as part of the phased reopening, and that pre-arrival testing removes 90% of infections at the semester start. We assume an on-campus population of N = 7500, an active COVID-19 prevalence of 2% at baseline, and that 60% of infected students require isolation for an average period of 11 days. We vary the reproductive number (Rt) between 1.25 and 4 to represent the effectiveness of alternative mitigation strategies throughout the semester, where Rt is constant or improving throughout the semester (ranging from 4 to 1.25). Results: Compared to pre-arrival testing only or neither intervention, phased reopening with pre-arrival testing reduced peak daily infections by 6% and 18% (Rt=1.25), 44% and 48% (Rt=2.5), 63% and 64% (Rt=4), and 72% and 74% (improving Rt), respectively, and reduced the proportion of on-campus beds needed for isolation from 10%-25% to 5%-9% across different values of Rt. Conclusion: Phased reopening with pre-arrival testing substantially reduces the peak number of daily infections throughout the semester and conserves university resources compared to strategies involving the simultaneous return of all students to campus. Phased reopenings allow institutions to improve safety protocols, adjust for factors that drive outbreaks, and if needed, preemptively move online before the return of additional students to campus, thus preventing unnecessary harm to students, institutional faculty and staff, and local communities.


2020 ◽  
Vol 63 (1) ◽  
Author(s):  
Dominik A. Moser ◽  
Jennifer Glaus ◽  
Sophia Frangou ◽  
Daniel S. Schechter

Abstract Background. The pandemic caused by coronavirus disease 2019 (COVID-19) has forced governments to implement strict social mitigation strategies to reduce the morbidity and mortality from acute infections. These strategies, however, carry a significant risk for mental health, which can lead to increased short-term and long-term mortality and is currently not included in modeling the impact of the pandemic. Methods. We used years of life lost (YLL) as the main outcome measure, applied to Switzerland as an example. We focused on suicide, depression, alcohol use disorder, childhood trauma due to domestic violence, changes in marital status, and social isolation, as these are known to increase YLL in the context of imposed restriction in social contact and freedom of movement. We stipulated a minimum duration of mitigation of 3 months based on current public health plans. Results. The study projects that the average person would suffer 0.205 YLL due to psychosocial consequence of COVID-19 mitigation measures. However, this loss would be entirely borne by 2.1% of the population, who will suffer an average of 9.79 YLL. Conclusions. The results presented here are likely to underestimate the true impact of the mitigation strategies on YLL. However, they highlight the need for public health models to expand their scope in order to provide better estimates of the risks and benefits of mitigation.


2021 ◽  
Author(s):  
Salihu Sabiu Musa ◽  
Xueying Wang ◽  
Shi Zhao ◽  
Shudong Li ◽  
Nafiu Hussaini ◽  
...  

Abstract Background: The COVID-19 pandemic has had a considerable impact on global health and economics. The impact in African countries has not been investigated through fitting epidemic models to the reported COVID-19 deaths.Method: We downloaded data for the twelve most affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying basic reproductive number (R0(t)) and infection attack rate (IAR). We developed a simple epidemic model and fitted the model to reported COVID-19 deaths in twelve African countries using iterated filtering and allowing a flexible transmission rate.Results: We observed high heterogeneity in the case-fatality rate across countries, which may be due to different reporting or testing efforts. South Africa, Tunisia, and Libya were affected most strongly, exhibiting a relatively higher(R0(t)) and infection attack rate.Conclusion: To effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvements in socioeconomic well-being, healthcare systems, the water supply, and awareness campaigns).


2020 ◽  
Author(s):  
Maria Jardim Beira ◽  
Anant Kumar ◽  
Lilia Perfeito ◽  
Joana Goncalves-Sa ◽  
Pedro Jose Sebastiao

Accurate models are fundamental to understand the dynamics of the COVID-19 pandemic and to evaluate different mitigation strategies. Here, we present a multi-compartmental model that fits the epidemiological data for eleven countries, despite the reduced number of fitting parameters. This model consistently explains the data for the daily infected, recovered, and dead over the first six months of the pandemic. The good quality of the fits makes it possible to explore different scenarios and evaluate the impact of both individual and collective behaviors and government- level decisions to mitigate the epidemic. We identify robust alternatives to lockdown, such as self- protection measures, and massive testing. Furthermore, communication and risk perception are fundamental to modulate the success of different strategies. The fitting/simulation tool is publicly available for use and test of other models, allowing for comparisons between different underlying assumptions, mitigation measures, and policy recommendations.


2021 ◽  
Author(s):  
M. J. Woodhouse ◽  
W. P. Aspinall ◽  
R. S. J. Sparks ◽  

1.AbstractThe SARS-CoV-2 epidemic has had major impacts on children’s education, with schools required to implement infection control measures that have led to long periods of absence and classroom closures. We develop an agent-based epidemiological model of SARS-CoV-2 transmission that is applied to model infection within school classrooms, with a contact model constructed using random networks informed by structured expert judgement. Mitigation strategies to control infection are modelled to allow analysis of their effectiveness in supressing infection outbreaks and in limiting pupil absence. The model is applied to re-examine Covid-19 in schools in the UK in autumn 2020, and to forecast infection levels in autumn 2021 when the more infectious Delta-variant is dominant and school transmission is likely to play a major role in a new wave of the epidemic. Our results indicate that testing-based surveillance of infections in the classroom population with isolation of positive cases is a more effective mitigation measure than bubble quarantine both for reducing transmission in schools and for avoiding pupil absence, even accounting for insensitivity of self-administered tests. Bubble quarantine results in large numbers of pupils absent from school, with only modest impact of classroom infection. However, maintaining a reduced contact rate within the classroom has a major beneficial impact for managing Covid-19 in school settings.


2019 ◽  
Vol 26 (2) ◽  
pp. 75-80
Author(s):  
Kuldeep Singh Dogra ◽  
◽  
Sushmita Uniyal ◽  
Kumar Ambrish ◽  
◽  
...  

Indian Western Himalaya has a rich plant diversity/ bio-resources due to the large variations in the altitude (300 to 6000 ms) and climatic conditions from tropical, temperate to alpine. The paper sheds light on the issues and challenges of climate change in the Western Himalaya; its impact on the plant diversity (wild plants, crops, fruits); loss of plant diversity and livelihood of the local communities; impact on the phenology of plant species; possible mitigation strategies to combat the impact of climate change. The Western Himalayan region has a rich diversity of plant diversity or bio resources. These bio resources (wild plants, crops, fruits) have been used by the local communities in the form of traditional medicines and foods from pre-historic periods or since the settlement of human communities in this region. These communities used these bio-resources as a source of income by their cultivation and selling in the markets. They are also involved in the traditional agriculture and horticulture practices and for that dependent on the climatic conditions (rate of precipitation, temperature, humidity) throughout the year. Hence stable environment conditions a pre requisite for better production and productivity. But in the last 100 years an increased in the temperature on earth brought large variation in the climate of Himalayan region too. The extreme climatic conditions will make Himalayan ecosystem more fragile, less productive and more prone towards disasters or natural calamities. Long term planning is required to understand the impact of climate change in the Western Himalaya along with some new strategies to mitigate its impact.


Author(s):  
Danilo Franco ◽  
Claudia Gonzalez ◽  
Leyda E Abrego ◽  
Jean P Carrera ◽  
Yamilka Diaz ◽  
...  

Background With more than 50000 accumulated cases, Panama has one of the highest incidences of SARS-CoV-2 in Central America, despite the fast implementation of disease control strategies. We investigated the early transmission patterns of the virus and the outcomes of mitigation measures in the country. Methods We collected information from epidemiological surveillance, including contact tracing, and genetic data from SARS-CoV-2 whole genomes, of the first five weeks of the outbreak. These data were used to estimate the exponential growth rate, doubling time and the time-varying effective reproductive number (Rt) using date of symptom onset in a Bayesian framework. The time of most recent ancestor for the introduced and circulating lineages was estimated by Bayesian analysis. Findings A total of 4210 subjects were SARS-CoV-2 positive during the period evaluated, of them we sequenced 313 cases, detecting the circulation of 10 SARS-CoV-2 lineages. Whole genomes analysis identified the local transmission of one cryptic lineage as early as 2 weeks before it was detected by surveillance systems. Analysis of transmission dynamics showed that lockdown reduced Rt and increased the doubling time, however, these measures did not stop the circulation of this lineage in the country. Interpretation These results demonstrate the value of epidemiological modeling and genome surveillance to assess mitigation strategies. At the same time, an active search for cryptic transmission clusters is crucial to interrupt local transmission of SARS-CoV-2 in a region.


Author(s):  
Dominik A. Moser ◽  
Jennifer Glaus ◽  
Sophia Frangou ◽  
Daniel S. Schechter

BackgroundThe pandemic caused by COVID-19 has forced governments to implement strict social mitigation strategies to reduce the morbidity and mortality from acute infections. These strategies however carry a significant risk for mental health which can lead to increased short-term and long-term mortality and is currently not included in modelling the impact of the pandemic.MethodsWe used years of life lost (YLL) as the main outcome measure as applied to Switzerland as an exemplar. We focused on suicide, depression, alcohol use disorder, childhood trauma due to domestic violence, changes in marital status and social isolation as these are known to increase YLL in the context of imposed restriction in social contact and freedom of movement. We stipulated a minimum duration of mitigation of 3 months based on current public health plans.ResultsThe study projects that the average person would suffer 0.205 YLL due to psychosocial consequence of COVID-19 mitigation measures. However, this loss would be entirely borne by 2.1% of the population, who will suffer an average 9.79 YLL.ConclusionsThe results presented here are likely to underestimate the true impact of the mitigation strategies on YLL. However, they highlight the need for public health models to expand their scope in order to provide better estimates of the risks and benefits of mitigation.


Author(s):  
Nickolas Dreher ◽  
Zachary Spiera ◽  
Fiona M. McAuley ◽  
Lindsey Kuohn ◽  
John R. Durbin ◽  
...  

AbstractBackgroundPolicymakers have employed various non-pharmaceutical interventions (NPIs) such as stay-at-home orders and school closures to limit the spread of Coronavirus disease (COVID-19). However, these measures are not without cost, and careful analysis is critical to quantify their impact on disease spread and guide future initiatives. This study aims to measure the impact of NPIs on the effective reproductive number (Rt) and other COVID-19 outcomes in U.S. states.MethodsIn order to standardize the stage of disease spread in each state, this study analyzes the weeks immediately after each state reached 500 cases. The primary outcomes were average Rt in the week following 500 cases and doubling time from 500 to 1000 cases. Linear and logistic regressions were performed in R to assess the impact of various NPIs while controlling for population density, GDP, and certain health metrics. This analysis was repeated for deaths with doubling time from 50 to 100 deaths and included several healthcare infrastructure control variables.ResultsStates that had a stay-at-home order in place at the time of their 500th case are associated with lower average Rt the following week compared to states without a stay-at-home order (p < 0.001) and are significantly less likely to have an Rt>1 (OR 0.07, 95% CI 0.01 to 0.37, p = 0.004). These states also experienced a significantly longer doubling time from 500 to 1000 cases (HR 0.35, 95% CI 0.17 to 0.72, p = 0.004). States in the highest quartile of average time spent at home were also slower to reach 1000 cases than those in the lowest quartile (HR 0.18, 95% CI 0.06 to 0.53, p = 0.002).DiscussionFew studies have analyzed the effect of statewide stay-at-home orders, school closures, and other social distancing measures in the U.S., which has faced the largest COVID-19 case burden. States with stay-at-home orders have a 93% decrease in the odds of having a positive Rt at a standardized point in disease burden. States that plan to scale back such measures should carefully monitor transmission metrics.


2021 ◽  
Author(s):  
Mohammed Alser ◽  
Jeremie S. Kim ◽  
Nour Almadhoun Alserr ◽  
Stefan W. Tell ◽  
Onur Mutlu

AbstractMotivationEarly detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests performed in every country, simulating the COVID-19 spread along with the potential effect of each mitigation strategy currently remains one of the most effective ways in managing the healthcare system and guiding policy-makers. We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region and provides suggestions on what strength the upcoming mitigation measure should be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a geographical region by simulating the average number of new infections caused by an infected person considering the effect of external factors, such as environmental conditions (e.g., climate, temperature, humidity) and mitigation measures.ResultsUsing Switzerland as a case study, COVIDHunter estimates that the policy-makers need to keep the current mitigation measures for at least 30 days to prevent demand from quickly exceeding existing hospital capacity. Relaxing the mitigation measures by 50% for 30 days increases both the daily capacity need for hospital beds and daily number of deaths exponentially by an average of 23.8 ×, who may occupy ICU beds and ventilators for a period of time. Unlike existing models, the COVIDHunter model accurately monitors and predicts the daily number of cases, hospitalizations, and deaths due to COVID-19. Our model is flexible to configure and simple to modify for modeling different scenarios under different environmental conditions and mitigation measures.Availabilityhttps://github.com/CMU-SAFARI/[email protected], [email protected] informationSupplementary data is available at Bioinformatics online.


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