scholarly journals Modelling the impact of presemester testing on COVID-19 outbreaks in university campuses

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e042578
Author(s):  
Lior Rennert ◽  
Corey Andrew Kalbaugh ◽  
Lu Shi ◽  
Christopher McMahan
Keyword(s):  
Disease Transmission ◽  
Mitigation Strategies ◽  
National Guidelines ◽  
University Campuses ◽  
Transmission Rates ◽  
Peak Number ◽  
Bed Capacity ◽  
The Impact ◽  
Transmission Models ◽  
Outbreak Size

ObjectivesUniversities are exploring strategies to mitigate the spread of COVID-19 prior to reopening their campuses. National guidelines do not currently recommend testing students prior to campus arrival. However, the impact of presemester testing has not been studied.DesignDynamic SARS-CoV-2 transmission models are used to explore the effects of three presemester testing interventions.InterventionsTesting of students 0, 1 and 2 times prior to campus arrival.Primary outcomesNumber of active infections and time until isolation bed capacity is reached.SettingWe set on-campus and off-campus populations to 7500 and 17 500 students, respectively. We assumed 2% prevalence of active cases at the semester start, and that one-third of infected students will be detected and isolated throughout the semester. Isolation bed capacity was set at 500. We varied disease transmission rates (R0=1.5, 2, 3, 4) to represent the effectiveness of mitigation strategies throughout the semester.ResultsWithout presemester screening, peak number of active infections ranged from 4114 under effective mitigation strategies (R0=1.5) to 10 481 under ineffective mitigation strategies (R0=4), and exhausted isolation bed capacity within 10 (R0=4) to 25 days (R0=1.5). Mandating at least one test prior to campus arrival delayed the timing and reduced the size of the peak, while delaying the time until isolation bed capacity was reached. Testing twice in conjunction with effective mitigation strategies (R0=1.5) was the only scenario that did not exhaust isolation bed capacity during the semester.ConclusionsPresemester screening is necessary to avert early and large surges of active COVID-19 infections. Therefore, we recommend testing within 1 week prior to and on campus return. While this strategy is sufficient for delaying the timing of the peak outbreak, presemester testing would need to be implemented in conjunction with effective mitigation strategies to significantly reduce outbreak size and preserve isolation bed capacity.

scholarly journals The urgent need for phased university reopenings to mitigate the spread of COVID-19 and conserve institutional resources: A modeling study

2020 ◽  
Author(s):  
Lior Rennert ◽  
Corey Kalbaugh ◽  
Christopher McMahan ◽  
Lu Shi ◽  
Christopher C Colenda
Keyword(s):  
The United States ◽  
College Campuses ◽  
Mitigation Strategies ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Limited Effectiveness ◽  
Institutional Resources ◽  
Bed Capacity ◽  
The Impact ◽  
Transmission Models

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.

scholarly journals The effectiveness of interventions to reduce COVID-19 transmission in a large urban jail

2020 ◽  
Author(s):  
Giovanni S. P. Malloy ◽  
Lisa Puglisi ◽  
Margaret L. Brandeau ◽  
Tyler D. Harvey ◽  
Emily A. Wang
Keyword(s):  
Single Cells ◽  
Mitigation Strategies ◽  
Policy Implications ◽  
Transmission Model ◽  
Stochastic Dynamic ◽  
National Guidelines ◽  
Reproduction Ratio ◽  
Dynamic Transmission Model ◽  
Correctional Settings ◽  
The Impact

ABSTRACTObjectivesTo estimate the impact of various mitigation strategies on COVID-19 transmission in a U.S. jail beyond those offered in national guidelines.MethodsWe developed a stochastic dynamic transmission model of COVID-19 in one large urban U.S. jail among staff and incarcerated individuals. We divided the outbreak into four intervention phases: the start of the outbreak, depopulation of the jail, increased proportion of people in single cells, and asymptomatic testing. We used the next generation method to estimate the basic reproduction ratio, R0, in each phase. We estimated the fraction of new cases, hospitalizations, and deaths averted by these interventions along with the standard measures of sanitization, masking, and social distancing interventions.ResultsFor the first outbreak phase, the estimated R0 was 8.23 (95% CrI: 5.01-12.90), and for the subsequent phases, R0,phase 2 = 3.58 (95% CrI: 2.46-5.08), R0,phase 3 = 1.72 (95% CrI: 1.41-2.12), and R0,phase 4 = 0.45 (95% CrI: 0.32-0.59). In total, the jail’s interventions prevented approximately 83% of projected cases and hospitalizations and 89% of deaths over 83 days.ConclusionsDepopulation, single celling, and asymptomatic testing within jails can be effective strategies to mitigate COVID-19 transmission in addition to standard public health measures.Policy ImplicationsDecision-makers should prioritize reductions in the jail population, single celling, and testing asymptomatic populations, as additional measures to manage COVID-19 within correctional settings.

scholarly journals Fluid fragmentation shapes rain-induced foliar disease transmission

2015 ◽  
Vol 12 (104) ◽  
pp. 20141092 ◽  
Author(s):  
T. Gilet ◽  
L. Bourouiba
Keyword(s):  
Disease Transmission ◽  
Scaling Laws ◽  
Sessile Drop ◽  
Early Stage ◽  
Leaf Size ◽  
Pathogen Transmission ◽  
Mitigation Strategies ◽  
Plant Diseases ◽  
Foliar Disease ◽  
The Impact

Plant diseases represent a growing threat to the global food supply. The factors contributing to pathogen transmission from plant to plant remain poorly understood. Statistical correlations between rainfalls and plant disease outbreaks were reported; however, the detailed mechanisms linking the two were relegated to a black box. In this combined experimental and theoretical study, we focus on the impact dynamics of raindrops on infected leaves, one drop at a time. We find that the deposition range of most of the pathogen-bearing droplets is constrained by a hydrodynamical condition and we quantify the effect of leaf size and compliance on such constraint. Moreover, we identify and characterize two dominant fluid fragmentation scenarios as responsible for the dispersal of most pathogen-bearing droplets emitted from infected leaves: (i) the crescent-moon ejection is driven by the direct interaction between the impacting raindrop and the contaminated sessile drop and (ii) the inertial detachment is driven by the motion imparted to the leaf by the raindrop, leading to catapult-like droplet ejections. We find that at first, decreasing leaf size or increasing compliance reduces the range of pathogen-bearing droplets and the subsequent epidemic onset efficiency. However, this conclusion only applies for the crescent moon ejection. Above a certain compliance threshold a more effective mechanism of contaminated fluid ejection, the inertial detachment, emerges. This compliance threshold is determined by the ratio between the leaf velocity and the characteristic velocity of fluid fragmentation. The inertial detachment mechanism enhances the range of deposition of the larger contaminated droplets and suggests a change in epidemic onset pattern and a more efficient potential of infection of neighbouring plants. Dimensionless parameters and scaling laws are provided to rationalize our observations. Our results link for the first time the mechanical properties of foliage with the onset dynamics of foliar epidemics through the lens of fluid fragmentation. We discuss how the reported findings can inform the design of mitigation strategies acting at the early stage of a foliar disease outbreak.

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 Effectiveness of interventions to reduce COVID-19 transmission in a large urban jail: a model-based analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e042898
Author(s):  
Giovanni S P Malloy ◽  
Lisa Puglisi ◽  
Margaret L Brandeau ◽  
Tyler D Harvey ◽  
Emily A Wang
Keyword(s):  
Single Cells ◽  
Credible Interval ◽  
Mitigation Strategies ◽  
Transmission Model ◽  
Secondary Outcome ◽  
Stochastic Dynamic ◽  
National Guidelines ◽  
Reproduction Ratio ◽  
Dynamic Transmission Model ◽  
The Impact

ObjectivesWe aim to estimate the impact of various mitigation strategies on COVID-19 transmission in a US jail beyond those offered in national guidelines.DesignWe developed a stochastic dynamic transmission model of COVID-19.SettingOne anonymous large urban US jail.ParticipantsSeveral thousand staff and incarcerated individuals.InterventionsThere were four intervention phases during the outbreak: the start of the outbreak, depopulation of the jail, increased proportion of people in single cells and asymptomatic testing. These interventions were implemented incrementally and in concert with one another.Primary and secondary outcome measuresThe basic reproduction ratio, R0, in each phase, as estimated using the next generation method. The fraction of new cases, hospitalisations and deaths averted by these interventions (along with the standard measures of sanitisation, masking and social distancing interventions).ResultsFor the first outbreak phase, the estimated R0 was 8.44 (95% credible interval (CrI): 5.00 to 13.10), and for the subsequent phases, R0,phase 2=3.64 (95% CrI: 2.43 to 5.11), R0,phase 3=1.72 (95% CrI: 1.40 to 2.12) and R0,phase 4=0.58 (95% CrI: 0.43 to 0.75). In total, the jail’s interventions prevented approximately 83% of projected cases, hospitalisations and deaths over 83 days.ConclusionsDepopulation, single celling and asymptomatic testing within jails can be effective strategies to mitigate COVID-19 transmission in addition to standard public health measures. Decision makers should prioritise reductions in the jail population, single celling and testing asymptomatic populations as additional measures to manage COVID-19 within correctional settings.

scholarly journals Towards a unified generic framework to define and observe contacts between livestock and wildlife: a systematic review

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10221
Author(s):  
Sonny A. Bacigalupo ◽  
Linda K. Dixon ◽  
Simon Gubbins ◽  
Adam J. Kucharski ◽  
Julian A. Drewe
Keyword(s):  
Systematic Review ◽  
Disease Transmission ◽  
Mitigation Strategies ◽  
Unified Framework ◽  
Vector Borne Diseases ◽  
Observation Methods ◽  
Contact Data ◽  
Vector Borne ◽  
Definition Of ◽  
Transmission Models

Wild animals are the source of many pathogens of livestock and humans. Concerns about the potential transmission of economically important and zoonotic diseases from wildlife have led to increased surveillance at the livestock-wildlife interface. Knowledge of the types, frequency and duration of contacts between livestock and wildlife is necessary to identify risk factors for disease transmission and to design possible mitigation strategies. Observing the behaviour of many wildlife species is challenging due to their cryptic nature and avoidance of humans, meaning there are relatively few studies in this area. Further, a consensus on the definition of what constitutes a ‘contact’ between wildlife and livestock is lacking. A systematic review was conducted to investigate which livestock-wildlife contacts have been studied and why, as well as the methods used to observe each species. Over 30,000 publications were screened, of which 122 fulfilled specific criteria for inclusion in the analysis. The majority of studies examined cattle contacts with badgers or with deer; studies involving wild pig contacts with cattle or with domestic pigs were the next most frequent. There was a range of observational methods including motion-activated cameras and global positioning system collars. As a result of the wide variation and lack of consensus in the definitions of direct and indirect contacts, we developed a unified framework to define livestock-wildlife contacts that is sufficiently flexible to be applied to most wildlife and livestock species for non-vector-borne diseases. We hope this framework will help standardise the collection and reporting of contact data; a valuable step towards being able to compare the efficacy of wildlife-livestock observation methods. In doing so, it may aid the development of better disease transmission models and improve the design and effectiveness of interventions to reduce or prevent disease transmission.

scholarly journals Delays in lymphatic filariasis elimination programmes due to COVID-19, and possible mitigation strategies

2021 ◽  
Author(s):  
Joaquín M Prada ◽  
Wilma A Stolk ◽  
Emma L Davis ◽  
Panayiota Touloupou ◽  
Swarnali Sharma ◽  
...  
Keyword(s):  
Mathematical Modelling ◽  
Lymphatic Filariasis ◽  
Mass Drug Administration ◽  
Neglected Tropical Diseases ◽  
Mitigation Strategies ◽  
Tropical Diseases ◽  
Short Delay ◽  
Catch Up ◽  
The Impact ◽  
Transmission Models

Abstract Background In view of the current global coronavirus disease 2019 pandemic, mass drug administration interventions for neglected tropical diseases, including lymphatic filariasis (LF), have been halted. We used mathematical modelling to estimate the impact of delaying or cancelling treatment rounds and explore possible mitigation strategies. Methods We used three established LF transmission models to simulate infection trends in settings with annual treatment rounds and programme delays in 2020 of 6, 12, 18 or 24 months. We then evaluated the impact of various mitigation strategies upon resuming activities. Results The delay in achieving the elimination goals is on average similar to the number of years the treatment rounds are missed. Enhanced interventions implemented for as little as 1 y can allow catch-up on the progress lost and, if maintained throughout the programme, can lead to acceleration of up to 3 y. Conclusions In general, a short delay in the programme does not cause a major delay in achieving the goals. Impact is strongest in high-endemicity areas. Mitigation strategies such as biannual treatment or increased coverage are key to minimizing the impact of the disruption once the programme resumes and lead to potential acceleration should these enhanced strategies be maintained.

scholarly journals Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1084
Author(s):  
Constantino Caetano ◽  
Maria Luísa Morgado ◽  
Paula Patrício ◽  
João F. Pereira ◽  
Baltazar Nunes
Keyword(s):  
Disease Transmission ◽  
Physical Distance ◽  
Mitigation Strategies ◽  
Distance Measures ◽  
The Past ◽  
Contact Patterns ◽  
Age Structured ◽  
The Impact ◽  
Number Of Individuals ◽  
Second Wave

In this paper, we present an age-structured SEIR model that uses contact patterns to reflect the physical distance measures implemented in Portugal to control the COVID-19 pandemic. By using these matrices and proper estimates for the parameters in the model, we were able to ascertain the impact of mitigation strategies employed in the past. Results show that the March 2020 lockdown had an impact on disease transmission, bringing the effective reproduction number (R(t)) below 1. We estimate that there was an increase in the transmission after the initial lift of the measures on 6 May 2020 that resulted in a second wave that was curbed by the October and November measures. December 2020 saw an increase in the transmission reaching an R(t) = 1.45 in early January 2021. Simulations indicate that the lockdown imposed on the 15 January 2021 might reduce the intensive care unit (ICU) demand to below 200 cases in early April if it lasts at least 2 months. As it stands, the model was capable of projecting the number of individuals in each infection phase for each age group and moment in time.

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 Predicting the impact of disruptions in lymphatic filariasis elimination programmes due to the outbreak of coronavirus disease (COVID-19) and possible mitigation strategies

2020 ◽  
Author(s):  
Joaquín M. Prada ◽  
Wilma A. Stolk ◽  
Emma L. Davis ◽  
Panayiota Touloupou ◽  
Swarnali Sharma ◽  
...  
Keyword(s):  
Mathematical Modelling ◽  
Lymphatic Filariasis ◽  
Neglected Tropical Diseases ◽  
Mitigation Strategies ◽  
Tropical Diseases ◽  
Short Delay ◽  
Catch Up ◽  
One Year ◽  
The Impact ◽  
Transmission Models

AbstractBackgroundIn view of the current global COVID-19 pandemic, mass drug administration interventions for neglected tropical diseases, including lymphatic filariasis, have been halted. We used mathematical modelling to estimate the impact of delaying or cancelling treatment rounds and explore possible mitigation strategies.MethodsWe used three established lymphatic filariasis transmission models to simulate infection trends in settings with annual treatment rounds and programme delays in 2020 of 6, 12, 18 or 24 months. We then evaluated the impact of various mitigation strategies upon resuming activities.ResultsThe delay in achieving the elimination goals is on average similar to the number of years the treatment rounds are missed. Enhanced interventions implemented for as little as one year can allow catch-up on the progress lost, and if maintained throughout the programme can lead to acceleration of up to 3 years.ConclusionsIn general, a short delay in the programme does not cause major delay in achieving the goals. Impact is strongest in high endemicity areas. Mitigation strategies such as biannual treatment or increased coverage are key to minimizing the impact of the disruption once the programme resumes; and lead to potential acceleration, should these enhanced strategies be maintained.

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