scholarly journals A Dynamic Transmission Model to Evaluate the Effectiveness of Infection Control Strategies

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Karim Khader ◽  
Alun Thomas ◽  
W. Charles Huskins ◽  
Molly Leecaster ◽  
Yue Zhang ◽  
...  
Keyword(s):  
Transmission Rate ◽  
Control Strategies ◽  
Transmission Model ◽  
Modeling Framework ◽  
Intervention Effect ◽  
Rigorous Evaluation ◽  
Alternative Intervention ◽  
Statistical Framework ◽  
Dynamic Transmission Model ◽  
Original Analysis

Abstract Background The advancement of knowledge about control of antibiotic resistance depends on the rigorous evaluation of alternative intervention strategies. The STAR*ICU trial examined the effects of active surveillance and expanded barrier precautions on acquisition of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) in intensive care units. We report a reanalyses of the STAR*ICU trial using a Bayesian transmission modeling framework. Methods The data included admission and discharge times and surveillance test times and results. Markov chain Monte Carlo stochastic integration was used to estimate the transmission rate, importation, false negativity, and clearance separately for MRSA and VRE. The primary outcome was the intervention effect, which when less than (or greater than) zero, indicated a decreased (or increased) transmission rate attributable to the intervention. Results The transmission rate increased in both arms from pre- to postintervention (by 20% and 26% for MRSA and VRE). The estimated intervention effect was 0.00 (95% confidence interval [CI], −0.57 to 0.56) for MRSA and 0.05 (95% CI, −0.39 to 0.48) for VRE. Compared with MRSA, VRE had a higher transmission rate (preintervention, 0.0069 vs 0.0039; postintervention, 0.0087 vs 0.0046), higher importation probability (0.22 vs 0.17), and a lower clearance rate per colonized patient-day (0.016 vs 0.035). Conclusions Transmission rates in the 2 treatment arms were statistically indistinguishable from the pre- to postintervention phase, consistent with the original analysis of the STAR*ICU trial. Our statistical framework was able to disentangle transmission from importation and account for imperfect testing. Epidemiological differences between VRE and MRSA were revealed.

scholarly journals A modeling study to inform screening and testing interventions for the control of SARS-CoV-2 on university campuses

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ben Lopman ◽  
Carol Y. Liu ◽  
Adrien Le Guillou ◽  
Andreas Handel ◽  
Timothy L. Lash ◽  
...  
Keyword(s):  
Cumulative Incidence ◽  
Disease Surveillance ◽  
Control Strategies ◽  
Transmission Model ◽  
University Administrators ◽  
Modeling Framework ◽  
University Campuses ◽  
School Year ◽  
Flexible Modeling ◽  
Resource Requirements

AbstractUniversity administrators face decisions about how to safely return and maintain students, staff and faculty on campus throughout the 2020–21 school year. We developed a susceptible-exposed-infectious-recovered (SEIR) deterministic compartmental transmission model of SARS-CoV-2 among university students, staff, and faculty. Our goals were to inform planning at our own university, Emory University, a medium-sized university with around 15,000 students and 15,000 faculty and staff, and to provide a flexible modeling framework to inform the planning efforts at similar academic institutions. Control strategies of isolation and quarantine are initiated by screening (regardless of symptoms) or testing (of symptomatic individuals). We explored a range of screening and testing frequencies and performed a probabilistic sensitivity analysis. We found that among students, monthly and weekly screening can reduce cumulative incidence by 59% and 87%, respectively, while testing with a 2-, 4- and 7-day delay between onset of infectiousness and testing results in an 84%, 74% and 55% reduction in cumulative incidence. Smaller reductions were observed among staff and faculty. Community-introduction of SARS-CoV-2 onto campus may be controlled with testing, isolation, contract tracing and quarantine. Screening would need to be performed at least weekly to have substantial reductions beyond disease surveillance. This model can also inform resource requirements of diagnostic capacity and isolation/quarantine facilities associated with different strategies.

scholarly journals A model of COVID-19 transmission and control on university campuses

2020 ◽  
Author(s):  
Ben Lopman ◽  
Carol Y. Liu ◽  
Adrien Le Guillou ◽  
Timothy L. Lash ◽  
Alexander P. Isakov ◽  
...  
Keyword(s):  
Cumulative Incidence ◽  
Control Strategies ◽  
Transmission Model ◽  
Reproductive Number ◽  
Medium Size ◽  
University Administrators ◽  
Screening Assay ◽  
Modeling Framework ◽  
University Campuses ◽  
Resource Requirements

AbstractIn response to the COVID-19 pandemic, institutions of higher education in almost every nation closed in the first half of 2020. University administrators are now facing decisions about how to safely return students, staff and faculty to campus. To provide a framework to evaluate various strategies, we developed a susceptible-exposed-infectious-recovered (SEIR) type of deterministic compartmental transmission model of SARS-CoV-2 among students, staff and faculty. Our goals were to support the immediate pandemic planning at our own university, and to provide a flexible modeling framework to inform the planning efforts at similar academic institutions. We parameterized the model for our institution, Emory University, a medium-size private university in Atlanta, Georgia. Control strategies of isolation and quarantine are initiated by screening (regardless of symptoms) or testing (of symptomatic individuals). We explore a range of screening and testing frequencies and perform a probabilistic sensitivity analysis of input parameters. We find that monthly and weekly screening can reduce cumulative incidence by 42% and 80% in students, respectively, while testing with a 2-, 4- and 7-day delay results in an 88%, 79% and 67% reduction in cumulative incidence in students over the semester, respectively. Similar reductions are observed among staff and faculty. A testing strategy requires far fewer diagnostic assays to be implemented than a screening assay. Our intervention model is conservative in that we assume a fairly high reproductive number that is not reduced through social distancing measures. We find that community-introduction of SARS-CoV-2 infection onto campus can be controlled with effective testing, isolation, contract tracing and quarantine, but that cases, hospitalization, and (in some scenarios) deaths may still occur. In addition to estimating health impacts, this model can help to predict the resource requirements in terms of diagnostic capacity and isolation/quarantine facilities associated with different strategies.

scholarly journals Assessment of control strategies against Clonorchis sinensis infection based on a multi-group dynamic transmission model

2020 ◽  
Vol 14 (3) ◽  
pp. e0008152
Author(s):  
Xiao-Hong Huang ◽  
Men-Bao Qian ◽  
Guang-Hu Zhu ◽  
Yue-Yi Fang ◽  
Yuan-Tao Hao ◽  
...  
Keyword(s):  
Control Strategies ◽  
Clonorchis Sinensis ◽  
Transmission Model ◽  
Group Dynamic ◽  
Dynamic Transmission Model

scholarly journals Estimating asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan: a mathematical modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xi Huo ◽  
Jing Chen ◽  
Shigui Ruan
Keyword(s):  
Disease Transmission ◽  
Large Scale ◽  
Transmission Rate ◽  
Control Strategies ◽  
Herd Immunity ◽  
Antibody Prevalence ◽  
Screening Process ◽  
Vaccination Programs ◽  
Prevalence Level ◽  
The Impact

Abstract Background The COVID-19 outbreak in Wuhan started in December 2019 and was under control by the end of March 2020 with a total of 50,006 confirmed cases by the implementation of a series of nonpharmaceutical interventions (NPIs) including unprecedented lockdown of the city. This study analyzes the complete outbreak data from Wuhan, assesses the impact of these public health interventions, and estimates the asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan. Methods By taking different stages of the outbreak into account, we developed a time-dependent compartmental model to describe the dynamics of disease transmission and case detection and reporting. Model coefficients were parameterized by using the reported cases and following key events and escalated control strategies. Then the model was used to calibrate the complete outbreak data by using the Monte Carlo Markov Chain (MCMC) method. Finally we used the model to estimate asymptomatic and undetected cases and approximate the overall antibody prevalence level. Results We found that the transmission rate between Jan 24 and Feb 1, 2020, was twice as large as that before the lockdown on Jan 23 and 67.6% (95% CI [0.584,0.759]) of detectable infections occurred during this period. Based on the reported estimates that around 20% of infections were asymptomatic and their transmission ability was about 70% of symptomatic ones, we estimated that there were about 14,448 asymptomatic and undetected cases (95% CI [12,364,23,254]), which yields an estimate of a total of 64,454 infected cases (95% CI [62,370,73,260]), and the overall antibody prevalence level in the population of Wuhan was 0.745% (95% CI [0.693%,0.814%]) by March 31, 2020. Conclusions We conclude that the control of the COVID-19 outbreak in Wuhan was achieved via the enforcement of a combination of multiple NPIs: the lockdown on Jan 23, the stay-at-home order on Feb 2, the massive isolation of all symptomatic individuals via newly constructed special shelter hospitals on Feb 6, and the large scale screening process on Feb 18. Our results indicate that the population in Wuhan is far away from establishing herd immunity and provide insights for other affected countries and regions in designing control strategies and planing vaccination programs.

The Effect of Thermo-Hydrodynamics on Manual Automotive Transmissions Gear Rattle

2009 ◽  
Author(s):  
Miguel De la Cruz ◽  
Stephanos Theodossiades ◽  
Homer Rahnejat ◽  
Patrick Kelly
Keyword(s):  
Contact Stiffness ◽  
Hertzian Contact ◽  
Transmission Model ◽  
System Response ◽  
Common Denominator ◽  
Gear Teeth ◽  
Lubricated Contacts ◽  
Engine Order ◽  
Dynamic Transmission Model ◽  
Gear Rattle

Manual transmission gear rattle is the result of repetitive impacts of gear meshing teeth within their backlash. This NVH phenomenon is a major industrial concern and can occur under various loaded or unloaded conditions. It fundamentally differs from other transient NVH phenomena, such as clonk or thud, which are due to impulsive actions. However, they all have their lowest common denominator in the action of contact/impact forces through lubricated contacts. Various forms of rattle have, therefore, been defined: idle rattle, drive rattle, creep rattle and over-run rattle. This paper presents a dynamic transmission model for creep rattle conditions (engaged gear at low engine RPM). The model takes into account the lubricated impact force between a gear teeth pair during a meshing cycle as well as the friction between their flanks. Hertzian contact conditions are applied to the gear pair along the torque path. Additionally, isoviscous hydrodynamic regime of lubrication is assumed for unselected (loose gear pairs) with lightly loaded impact conditions. The highly non-linear impacts induce a range of system response frequencies. These include engine order harmonics, harmonics of meshing frequency and natural frequencies related to contact stiffness. The last of these are dependent on the contact geometry and lubricant rheology. The analysis includes lubricant viscosity variation due to generated contact pressures as well as temperature. For loose gears, subject to oscillations on their retaining bearings, bearing friction is also considered.

scholarly journals Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England

Science ◽  
2021 ◽  
Vol 372 (6538) ◽  
pp. eabg3055 ◽  
Author(s):  
Nicholas G. Davies ◽  
Sam Abbott ◽  
Rosanna C. Barnard ◽  
Christopher I. Jarvis ◽  
Adam J. Kucharski ◽  
...  
Keyword(s):  
United States ◽  
Severe Acute Respiratory Syndrome ◽  
Reproduction Number ◽  
The United States ◽  
Control Measures ◽  
Transmission Model ◽  
Educational Institutions ◽  
Stringent Control ◽  
Dynamic Transmission Model ◽  
Credible Intervals

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.

scholarly journals 1839. Contact Precautions’ Effects on MRSA Transmission in Department of Veterans Affairs Hospitals

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S46-S46
Author(s):  
Karim Khader ◽  
Alun Thomas ◽  
Lindsay D Visnovsky ◽  
Damon Toth ◽  
Lindsay T Keegan ◽  
...  
Keyword(s):  
Transmission Rate ◽  
Veterans Affairs ◽  
Transmission Model ◽  
Clinical Test ◽  
Department Of Veterans Affairs ◽  
Median Percentage ◽  
Contact Precautions ◽  
Day Range ◽  
Clinical Culture ◽  
Acute Care Facilities

Abstract Background In 2007, the Department of Veterans Affairs (VA) implemented the methicillin-resistant Staphylococcus aureus (MRSA) Prevention Initiative nationally in acute care facilities (ACFs). The initiative included universal nasal surveillance for MRSA colonization and implementation of contact precautions (CP) for identified carriers for the duration of their stay. Despite subsequent declines in MRSA infection rates in the VA, debate on CP efficacy continues, due to limited and inconclusive direct evidence. This study estimated CP impact on MRSA transmission in the VA. Methods We analyzed 1 year of data from 36 VA ACFs in 2014 using a Bayesian transmission model. The data included admission, discharge, and surveillance and clinical test results for MRSA. Per the MRSA Prevention Initiative protocol that placed known carriers on CP, we assumed patients were on CP starting 12 hours after a positive surveillance test, 24 hours after a positive clinical culture, or at admission if the patient had a positive test within 365 days prior to admission. Our model produced estimates of ward-specific transmission rate, surveillance test sensitivity, importation probability, and the CP effect parameter (CPe). For CPe < 1, CP reduced transmission. Additionally, we combined the estimates of CPe using a random-effects model with inverse variance weights to derive pooled estimates and corresponding standard errors. Results Facility size varied with a median daily census of 70 patients per day (range: 44–111). During the study period, 144,386 individuals were admitted into one of 36 ACFs, for 215,207 total admissions. The median percentage of admissions requiring contact precautions was 11.0% (range: 6.4%–16.1%). The estimated CPe was less than one in each of the 36 facilities with a median of 0.43 (range: 0.25–0.68). Our pooled estimate of CPe across all facilities was 0.47 (95% CI; 0.40, 0.55). Conclusion We found evidence of reduced MRSA transmission from patients on CP. This result was statistically significant in 5 of the 36 facilities and our pooled estimate suggests contact precautions could reduce the transmission rate by half. Further work is needed to account for imperfect compliance with CP, and for patients on CP for other reasons. Disclosures All Authors: No reported Disclosures.

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