scholarly journals Reopening universities during the COVID-19 pandemic: A testing strategy to minimize active cases and delay outbreaks

2020 ◽  
Author(s):  
Lior Rennert ◽  
Corey A. Kalbaugh ◽  
Lu Shi ◽  
Christopher McMahan
Keyword(s):  
Mitigation Strategies ◽  
Disease Spread ◽  
Contact Tracing ◽  
Infectious Period ◽  
University Campuses ◽  
Effective Strategies ◽  
Viral Spread ◽  
Extreme Risk ◽  
The Impact ◽  
Maximum Occupancy

AbstractBackgroundUniversity campuses present an ideal environment for viral spread and are therefore at extreme risk of serving as a hotbed for a COVID-19 outbreak. While active surveillance throughout the semester such as widespread testing, contact tracing, and case isolation, may assist in detecting and preventing early outbreaks, these strategies will not be sufficient should a larger outbreak occur. It is therefore necessary to limit the initial number of active cases at the start of the semester. We examine the impact of pre-semester NAT testing on disease spread in a university setting.MethodsWe implement simple dynamic transmission models of SARS-CoV-2 infection to explore the effects of pre-semester testing strategies on the number of active infections and occupied isolation beds throughout the semester. We assume an infectious period of 3 days and vary R0 to represent the effectiveness of disease mitigation strategies throughout the semester. We assume the prevalence of active cases at the beginning of the semester is 5%. The sensitivity of the NAT test is set at 90%.ResultsIf no pre-semester screening is mandated, the peak number of active infections occurs in under 10 days and the size of the peak is substantial, ranging from 5,000 active infections when effective mitigation strategies (R0 = 1.25) are implemented to over 15,000 active infections for less effective strategies (R0 = 3). When one NAT test is mandated within one week of campus arrival, effective (R0 = 1.25) and less effective (R0 = 3) mitigation strategies delay the onset of the peak to 40 days and 17 days, respectively, and result in peak size ranging from 1,000 to over 15,000 active infections. When two NAT tests are mandated, effective (R0 = 1.25) and less effective (R0 = 3) mitigation strategies delay the onset of the peak through the end of fall semester and 20 days, respectively, and result in peak size ranging from less than 1,000 to over 15,000 active infections. If maximum occupancy of isolation beds is set to 2% of the student population, then isolation beds would only be available for a range of 1 in 2 confirmed cases (R0 = 1.25) to 1 in 40 confirmed cases (R0 = 3) before maximum occupancy is reached.ConclusionEven with highly effective mitigation strategies throughout the semester, inadequate pre-semester testing will lead to early and large surges of the disease and result in universities quickly reaching their isolation bed capacity. We therefore recommend NAT testing within one week of campus return. While this strategy is sufficient for delaying the timing of the outbreak, pre-semester testing would need to be implemented in conjunction with effective mitigation strategies to reduce the outbreak size.

scholarly journals Assessment of Glyphosate Impact on the Agrofood Ecosystem

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 405
Author(s):  
Yaxin Sang ◽  
Juan-Carlos Mejuto ◽  
Jianbo Xiao ◽  
Jesus Simal-Gandara
Keyword(s):  
Risk Assessment ◽  
Food Production ◽  
Mitigation Strategies ◽  
Regulatory Control ◽  
Biological Pest Control ◽  
Effective Strategies ◽  
Nutrition Security ◽  
Production And Consumption ◽  
Food And Nutrition ◽  
The Impact

Agro-industries should adopt effective strategies to use agrochemicals such as glyphosate herbicides cautiously in order to protect public health. This entails careful testing and risk assessment of available choices, and also educating farmers and users with mitigation strategies in ecosystem protection and sustainable development. The key to success in this endeavour is using scientific research on biological pest control, organic farming and regulatory control, etc., for new developments in food production and safety, and for environmental protection. Education and research is of paramount importance for food and nutrition security in the shadow of climate change, and their consequences in food production and consumption safety and sustainability. This review, therefore, diagnoses on the use of glyphosate and the associated development of glyphosate-resistant weeds. It also deals with the risk assessment on human health of glyphosate formulations through environment and dietary exposures based on the impact of glyphosate and its metabolite AMPA—(aminomethyl)phosphonic acid—on water and food. All this to setup further conclusions and recommendations on the regulated use of glyphosate and how to mitigate the adverse effects.

scholarly journals How detection ranges and usage stops impact digital contact tracing effectiveness for COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Konstantin D. Pandl ◽  
Scott Thiebes ◽  
Manuel Schmidt-Kraepelin ◽  
Ali Sunyaev
Keyword(s):  
Disease Spread ◽  
Contact Tracing ◽  
Behavioral Characteristics ◽  
Policy Makers ◽  
Detection Range ◽  
Proximity Detection ◽  
Effective Technology ◽  
Different Types ◽  
Effectiveness And Efficiency ◽  
The Impact

AbstractTo combat the COVID-19 pandemic, many countries around the globe have adopted digital contact tracing apps. Various technologies exist to trace contacts that are potentially prone to different types of tracing errors. Here, we study the impact of different proximity detection ranges on the effectiveness and efficiency of digital contact tracing apps. Furthermore, we study a usage stop effect induced by a false positive quarantine. Our results reveal that policy makers should adjust digital contact tracing apps to the behavioral characteristics of a society. Based on this, the proximity detection range should at least cover the range of a disease spread, and be much wider in certain cases. The widely used Bluetooth Low Energy protocol may not necessarily be the most effective technology for contact tracing.

scholarly journals The timing of COVID-19 transmission

2020 ◽  
Author(s):  
Luca Ferretti ◽  
Alice Ledda ◽  
Chris Wymant ◽  
Lele Zhao ◽  
Virginia Ledda ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Large Fraction ◽  
Critical Factor ◽  
Contact Tracing ◽  
Infectious Period ◽  
Contextual Risk ◽  
Home Testing ◽  
Behavioural Factors ◽  
The Right ◽  
The Impact

The timing of SARS-CoV-2 transmission is a critical factor to understand the epidemic trajectory and the impact of isolation, contact tracing and other non- pharmaceutical interventions on the spread of COVID-19 epidemics. We examined the distribution of transmission events with respect to exposure and onset of symptoms. We show that for symptomatic individuals, the timing of transmission of SARS-CoV-2 is more strongly linked to the onset of clinical symptoms of COVID-19 than to the time since infection. We found that it was approximately centered and symmetric around the onset of symptoms, with three quarters of events occurring in the window from 2-3 days before to 2-3 days after. However, we caution against overinterpretation of the right tail of the distribution, due to its dependence on behavioural factors and interventions. We also found that the pre-symptomatic infectious period extended further back in time for individuals with longer incubation periods. This strongly suggests that information about when a case was infected should be collected where possible, in order to assess how far into the past their contacts should be traced. Overall, the fraction of transmission from strictly pre-symptomatic infections was high (41%; 95%CI 31-50%), which limits the efficacy of symptom-based interventions, and the large fraction of transmissions (35%; 95%CI 26-45%) that occur on the same day or the day after onset of symptoms underlines the critical importance of individuals distancing themselves from others as soon as they notice any symptoms, even if they are mild. Rapid or at-home testing and contextual risk information would greatly facilitate efficient early isolation.

scholarly journals Real-Time Digital Contact Tracing: Development of a System to Control COVID-19 Outbreaks in Nursing Homes and Long-Term Care Facilities

10.2196/20828 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e20828 ◽  
Author(s):  
Gerald Wilmink ◽  
Ilyssa Summer ◽  
David Marsyla ◽  
Subhashree Sukhu ◽  
Jeffrey Grote ◽  
...  
Keyword(s):  
Nursing Homes ◽  
Compartmental Model ◽  
Long Term Care ◽  
Contact Tracing ◽  
Term Care ◽  
Viral Spread ◽  
Effective Manner ◽  
Conventional Methods ◽  
The Impact

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread rapidly in nursing homes and long-term care (LTC) facilities. Symptoms-based screening and manual contact tracing have limitations that render them ineffective for containing the viral spread in LTC facilities. Symptoms-based screening alone cannot identify asymptomatic people who are infected, and the viral spread is too fast in confined living quarters to be contained by slow manual contact tracing processes. Objective We describe the development of a digital contact tracing system that LTC facilities can use to rapidly identify and contain asymptomatic and symptomatic SARS-CoV-2 infected contacts. A compartmental model was also developed to simulate disease transmission dynamics and to assess system performance versus conventional methods. Methods We developed a compartmental model parameterized specifically to assess the coronavirus disease (COVID-19) transmission in LTC facilities. The model was used to quantify the impact of asymptomatic transmission and to assess the performance of several intervention groups to control outbreaks: no intervention, symptom mapping, polymerase chain reaction testing, and manual and digital contact tracing. Results Our digital contact tracing system allows users to rapidly identify and then isolate close contacts, store and track infection data in a respiratory line listing tool, and identify contaminated rooms. Our simulation results indicate that the speed and efficiency of digital contact tracing contributed to superior control performance, yielding up to 52% fewer cases than conventional methods. Conclusions Digital contact tracing systems show promise as an effective tool to control COVID-19 outbreaks in LTC facilities. As facilities prepare to relax restrictions and reopen to outside visitors, such tools will allow them to do so in a surgical, cost-effective manner that controls outbreaks while safely giving residents back the life they once had before this pandemic hit.

scholarly journals A modelling framework to assess the likely effectiveness of facemasks in combination with ‘lock-down’ in managing the COVID-19 pandemic

2020 ◽  
Vol 476 (2238) ◽  
pp. 20200376 ◽  
Author(s):  
Richard O. J. H. Stutt ◽  
Renata Retkute ◽  
Michael Bradley ◽  
Christopher A. Gilligan ◽  
John Colvin
Keyword(s):  
Mobile Apps ◽  
Population Level ◽  
Disease Spread ◽  
Contact Tracing ◽  
Lower Efficiency ◽  
The Public ◽  
Molecular Tests ◽  
Modelling Framework ◽  
Increased Risk ◽  
The Impact

COVID-19 is characterized by an infectious pre-symptomatic period, when newly infected individuals can unwittingly infect others. We are interested in what benefits facemasks could offer as a non-pharmaceutical intervention, especially in the settings where high-technology interventions, such as contact tracing using mobile apps or rapid case detection via molecular tests, are not sustainable. Here, we report the results of two mathematical models and show that facemask use by the public could make a major contribution to reducing the impact of the COVID-19 pandemic. Our intention is to provide a simple modelling framework to examine the dynamics of COVID-19 epidemics when facemasks are worn by the public, with or without imposed ‘lock-down’ periods. Our results are illustrated for a number of plausible values for parameter ranges describing epidemiological processes and mechanistic properties of facemasks, in the absence of current measurements for these values. We show that, when facemasks are used by the public all the time (not just from when symptoms first appear), the effective reproduction number, R e , can be decreased below 1, leading to the mitigation of epidemic spread. Under certain conditions, when lock-down periods are implemented in combination with 100% facemask use, there is vastly less disease spread, secondary and tertiary waves are flattened and the epidemic is brought under control. The effect occurs even when it is assumed that facemasks are only 50% effective at capturing exhaled virus inoculum with an equal or lower efficiency on inhalation. Facemask use by the public has been suggested to be ineffective because wearers may touch their faces more often, thus increasing the probability of contracting COVID-19. For completeness, our models show that facemask adoption provides population-level benefits, even in circumstances where wearers are placed at increased risk. At the time of writing, facemask use by the public has not been recommended in many countries, but a recommendation for wearing face-coverings has just been announced for Scotland. Even if facemask use began after the start of the first lock-down period, our results show that benefits could still accrue by reducing the risk of the occurrence of further COVID-19 waves. We examine the effects of different rates of facemask adoption without lock-down periods and show that, even at lower levels of adoption, benefits accrue to the facemask wearers. These analyses may explain why some countries, where adoption of facemask use by the public is around 100%, have experienced significantly lower rates of COVID-19 spread and associated deaths. We conclude that facemask use by the public, when used in combination with physical distancing or periods of lock-down, may provide an acceptable way of managing the COVID-19 pandemic and re-opening economic activity. These results are relevant to the developed as well as the developing world, where large numbers of people are resource poor, but fabrication of home-made, effective facemasks is possible. A key message from our analyses to aid the widespread adoption of facemasks would be: ‘my mask protects you, your mask protects me’.

scholarly journals Real-Time Digital Contact Tracing: Development of a System to Control COVID-19 Outbreaks in Nursing Homes and Long-Term Care Facilities (Preprint)

2020 ◽  
Author(s):  
Gerald Wilmink ◽  
Ilyssa Summer ◽  
David Marsyla ◽  
Subhashree Sukhu ◽  
Jeffrey Grote ◽  
...  
Keyword(s):  
Nursing Homes ◽  
Compartmental Model ◽  
Long Term Care ◽  
Contact Tracing ◽  
Term Care ◽  
Viral Spread ◽  
Effective Manner ◽  
Conventional Methods ◽  
The Impact

BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread rapidly in nursing homes and long-term care (LTC) facilities. Symptoms-based screening and manual contact tracing have limitations that render them ineffective for containing the viral spread in LTC facilities. Symptoms-based screening alone cannot identify asymptomatic people who are infected, and the viral spread is too fast in confined living quarters to be contained by slow manual contact tracing processes. OBJECTIVE We describe the development of a digital contact tracing system that LTC facilities can use to rapidly identify and contain asymptomatic and symptomatic SARS-CoV-2 infected contacts. A compartmental model was also developed to simulate disease transmission dynamics and to assess system performance versus conventional methods. METHODS We developed a compartmental model parameterized specifically to assess the coronavirus disease (COVID-19) transmission in LTC facilities. The model was used to quantify the impact of asymptomatic transmission and to assess the performance of several intervention groups to control outbreaks: no intervention, symptom mapping, polymerase chain reaction testing, and manual and digital contact tracing. RESULTS Our digital contact tracing system allows users to rapidly identify and then isolate close contacts, store and track infection data in a respiratory line listing tool, and identify contaminated rooms. Our simulation results indicate that the speed and efficiency of digital contact tracing contributed to superior control performance, yielding up to 52% fewer cases than conventional methods. CONCLUSIONS Digital contact tracing systems show promise as an effective tool to control COVID-19 outbreaks in LTC facilities. As facilities prepare to relax restrictions and reopen to outside visitors, such tools will allow them to do so in a surgical, cost-effective manner that controls outbreaks while safely giving residents back the life they once had before this pandemic hit.

scholarly journals Modelling the impact of reducing control measures on the COVID-19 pandemic in a low transmission setting

2020 ◽  
Author(s):  
Nick Scott ◽  
Anna Palmer ◽  
Dominic Delport ◽  
Romesh Abeysuriya ◽  
Robyn Stuart ◽  
...  
Keyword(s):  
Public Transport ◽  
Risk Mitigation ◽  
Mitigation Strategies ◽  
Control Measures ◽  
Contact Tracing ◽  
Policy Changes ◽  
Low Transmission ◽  
Transmission Setting ◽  
Community Transmission ◽  
The Impact

AbstractAimsWe assessed COVID-19 epidemic risks associated with relaxing a set of physical distancing restrictions in the state of Victoria, Australia – a setting with low community transmission – in line with a national framework that aims to balance sequential policy relaxations with longer-term public health and economic need.MethodsAn agent-based model, Covasim, was calibrated to the local COVID-19 epidemiological and policy environment. Contact networks were modelled to capture transmission risks in households, schools and workplaces, and a variety of community spaces (e.g. public transport, parks, bars, cafes/restaurants) and activities (e.g. community or professional sports, large events). Policy changes that could prevent or reduce transmission in specific locations (e.g. opening/closing businesses) were modelled in the context of interventions that included testing, contact tracing (including via a smartphone app), and quarantine.ResultsPolicy changes leading to the gathering of large, unstructured groups with unknown individuals (e.g. bars opening, increased public transport use) posed the greatest risk, while policy changes leading to smaller, structured gatherings with known individuals (e.g. small social gatherings) posed least risk. In the model, epidemic impact following some policy changes took more than two months to occur. Model outcomes support continuation of working from home policies to reduce public transport use, and risk mitigation strategies in the context of social venues opening, such as >30% population-uptake of a contact-tracing app, physical distancing policies within venues reducing transmissibility by >40%, or patron identification records being kept to enable >60% contact tracing.ConclusionsIn a low transmission setting, care should be taken to avoid lifting sequential COVID-19 policy restrictions within short time periods, as it could take more than two months to detect the consequences of any changes. These findings have implications for other settings with low community transmission where governments are beginning to lift restrictions.

scholarly journals On the fallibility of simulation models in informing pandemic responses

2020 ◽  
Author(s):  
Deepti Gurdasani ◽  
Hisham Ziauddeen
Keyword(s):  
Simulation Models ◽  
Control Measures ◽  
Disease Spread ◽  
Contact Tracing ◽  
Parameter Estimates ◽  
Case Detection ◽  
Rapid Testing ◽  
Real World Data ◽  
Effective Strategies ◽  
Reliable Parameter

In the early stages of pandemics, mathematical models can provide invaluable insights into transmission dynamics, help predict disease spread, and evaluate control measures. However models are only valid within the limits of the parameters examined. As reliable parameter estimates are rarely available early in a new pandemic, best-guess estimates are used, which need to be constantly reviewed as new real-world data emerge. Estimating how sensitive the model is to changes in its parameters can provide useful information about validity when parameters are uncertain. Interpreting models without considering these factors can lead to flawed inferences, which can have far reaching effects when they inform public health policy. We illustrate this, here, using an example from the Hellewell et al. model published in Lancet Global Health, 2020. This model suggested that case detection and contact tracing was unlikely to be an effective strategy for pandemic control, and is likely to have informed UK government strategy to cease testing and contact tracing on the 12th March 2020. We show that this model is very sensitive to the parameter of delay between case detection and isolation. We demonstrate that when the delay scenario parameter is changed to a median of 1 day, which is very plausible in the context of current rapid testing, this model predicts a >80% probability of controlling the epidemic within 12 weeks, with relatively modest contact tracing. These results suggest that rapid testing, contact tracing and isolation could be effective strategies to control transmission.

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
Keyword(s):  
Local Communities ◽  
Student Population ◽  
Mitigation Strategies ◽  
Disease Spread ◽  
Mitigation Measures ◽  
Reproductive Number ◽  
Social Events ◽  
Large Numbers ◽  
Bed Capacity ◽  
The Impact

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.

scholarly journals A network modelling approach to assess non-pharmaceutical disease controls in a worker population: An application to SARS-CoV-2

2021 ◽  
Vol 17 (6) ◽  
pp. e1009058
Author(s):  
Edward M. Hill ◽  
Benjamin D. Atkins ◽  
Matt J. Keeling ◽  
Louise Dyson ◽  
Michael J. Tildesley
Keyword(s):  
Network Model ◽  
Significant Proportion ◽  
Disease Spread ◽  
Contact Tracing ◽  
Work Patterns ◽  
General Role ◽  
Work From Home ◽  
Working Practices ◽  
The Impact

As part of a concerted pandemic response to protect public health, businesses can enact non-pharmaceutical controls to minimise exposure to pathogens in workplaces and premises open to the public. Amendments to working practices can lead to the amount, duration and/or proximity of interactions being changed, ultimately altering the dynamics of disease spread. These modifications could be specific to the type of business being operated. We use a data-driven approach to parameterise an individual-based network model for transmission of SARS-CoV-2 amongst the working population, stratified into work sectors. The network is comprised of layered contacts to consider the risk of spread in multiple encounter settings (workplaces, households, social and other). We analyse several interventions targeted towards working practices: mandating a fraction of the population to work from home; using temporally asynchronous work patterns; and introducing measures to create ‘COVID-secure’ workplaces. We also assess the general role of adherence to (or effectiveness of) isolation and test and trace measures and demonstrate the impact of all these interventions across a variety of relevant metrics. The progress of the epidemic can be significantly hindered by instructing a significant proportion of the workforce to work from home. Furthermore, if required to be present at the workplace, asynchronous work patterns can help to reduce infections when compared with scenarios where all workers work on the same days, particularly for longer working weeks. When assessing COVID-secure workplace measures, we found that smaller work teams and a greater reduction in transmission risk reduced the probability of large, prolonged outbreaks. Finally, following isolation guidance and engaging with contact tracing without other measures is an effective tool to curb transmission, but is highly sensitive to adherence levels. In the absence of sufficient adherence to non-pharmaceutical interventions, our results indicate a high likelihood of SARS-CoV-2 spreading widely throughout a worker population. Given the heterogeneity of demographic attributes across worker roles, in addition to the individual nature of controls such as contact tracing, we demonstrate the utility of a network model approach to investigate workplace-targeted intervention strategies and the role of test, trace and isolation in tackling disease spread.

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