4 results on '"Chin, Elizabeth T."'
Search Results
2. Routine asymptomatic testing strategies for airline travel during the COVID-19 pandemic: a simulation study.
- Author
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Kiang, Mathew V, Chin, Elizabeth T, Huynh, Benjamin Q, Chapman, Lloyd AC, Rodríguez-Barraquer, Isabel, Greenhouse, Bryan, Rutherford, George W, Bibbins-Domingo, Kirsten, Havlir, Diane, Basu, Sanjay, and Lo, Nathan C
- Subjects
Humans ,Diagnostic Tests ,Routine ,Carrier State ,Travel ,Aircraft ,Computer Simulation ,Asymptomatic Infections ,Pandemics ,COVID-19 ,SARS-CoV-2 ,COVID-19 Testing ,Prevention ,Biodefense ,Emerging Infectious Diseases ,Vaccine Related ,Infectious Diseases ,Infection ,Good Health and Well Being ,Clinical Sciences ,Medical Microbiology ,Public Health and Health Services ,Microbiology - Abstract
BackgroundRoutine viral testing strategies for SARS-CoV-2 infection might facilitate safe airline travel during the COVID-19 pandemic and mitigate global spread of the virus. However, the effectiveness of these test-and-travel strategies to reduce passenger risk of SARS-CoV-2 infection and population-level transmission remains unknown.MethodsIn this simulation study, we developed a microsimulation of SARS-CoV-2 transmission in a cohort of 100 000 US domestic airline travellers using publicly available data on COVID-19 clinical cases and published natural history parameters to assign individuals one of five health states of susceptible to infection, latent period, early infection, late infection, or recovered. We estimated a per-day risk of infection with SARS-CoV-2 corresponding to a daily incidence of 150 infections per 100 000 people. We assessed five testing strategies: (1) anterior nasal PCR test within 3 days of departure, (2) PCR within 3 days of departure and 5 days after arrival, (3) rapid antigen test on the day of travel (assuming 90% of the sensitivity of PCR during active infection), (4) rapid antigen test on the day of travel and PCR test 5 days after arrival, and (5) PCR test 5 days after arrival. Strategies 2 and 4 included a 5-day quarantine after arrival. The travel period was defined as 3 days before travel to 2 weeks after travel. Under each scenario, individuals who tested positive before travel were not permitted to travel. The primary study outcome was cumulative number of infectious days in the cohort over the travel period without isolation or quarantine (population-level transmission risk), and the key secondary outcome was the number of infectious people detected on the day of travel (passenger risk of infection).FindingsWe estimated that in a cohort of 100 000 airline travellers, in a scenario with no testing or screening, there would be 8357 (95% uncertainty interval 6144-12831) infectious days with 649 (505-950) actively infectious passengers on the day of travel. The pre-travel PCR test reduced the number of infectious days from 8357 to 5401 (3917-8677), a reduction of 36% (29-41) compared with the base case, and identified 569 (88% [76-92]) of 649 actively infectious travellers on the day of flight; the addition of post-travel quarantine and PCR reduced the number of infectious days to 2520 days (1849-4158), a reduction of 70% (64-75) compared with the base case. The rapid antigen test on the day of travel reduced the number of infectious days to 5674 (4126-9081), a reduction of 32% (26-38) compared with the base case, and identified 560 (86% [83-89]) actively infectious travellers; the addition of post-travel quarantine and PCR reduced the number of infectious days to 3124 (2356-495), a reduction of 63% (58-66) compared with the base case. The post-travel PCR alone reduced the number of infectious days to 4851 (3714-7679), a reduction of 42% (35-49) compared with the base case.InterpretationRoutine asymptomatic testing for SARS-CoV-2 before travel can be an effective strategy to reduce passenger risk of infection during travel, although abbreviated quarantine with post-travel testing is probably needed to reduce population-level transmission due to importation of infection when travelling from a high to low incidence setting.FundingUniversity of California, San Francisco.
- Published
- 2021
3. Pathologic gene network rewiring implicates PPP1R3A as a central regulator in pressure overload heart failure
- Author
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Cordero, Pablo, Parikh, Victoria N, Chin, Elizabeth T, Erbilgin, Ayca, Gloudemans, Michael J, Shang, Ching, Huang, Yong, Chang, Alex C, Smith, Kevin S, Dewey, Frederick, Zaleta, Kathia, Morley, Michael, Brandimarto, Jeff, Glazer, Nicole, Waggott, Daryl, Pavlovic, Aleksandra, Zhao, Mingming, Moravec, Christine S, Tang, WH Wilson, Skreen, Jamie, Malloy, Christine, Hannenhalli, Sridhar, Li, Hongzhe, Ritter, Scott, Li, Mingyao, Bernstein, Daniel, Connolly, Andrew, Hakonarson, Hakon, Lusis, Aldons J, Margulies, Kenneth B, Depaoli-Roach, Anna A, Montgomery, Stephen B, Wheeler, Matthew T, Cappola, Thomas, and Ashley, Euan A
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Biological Sciences ,Genetics ,Human Genome ,Biotechnology ,Heart Disease ,Cardiovascular ,2.1 Biological and endogenous factors ,Aetiology ,Good Health and Well Being ,Animals ,Benzeneacetamides ,Cells ,Cultured ,Datasets as Topic ,Disease Models ,Animal ,Female ,Gene Expression Profiling ,Gene Expression Regulation ,Gene Knockdown Techniques ,Gene Regulatory Networks ,Genome-Wide Association Study ,Heart Failure ,Humans ,Male ,Metabolic Networks and Pathways ,Mice ,Mice ,Knockout ,Middle Aged ,Myocytes ,Cardiac ,Phosphoprotein Phosphatases ,Primary Cell Culture ,Pyridines ,Quantitative Trait Loci ,Rats ,Rats ,Sprague-Dawley ,Sequence Analysis ,RNA - Abstract
Heart failure is a leading cause of mortality, yet our understanding of the genetic interactions underlying this disease remains incomplete. Here, we harvest 1352 healthy and failing human hearts directly from transplant center operating rooms, and obtain genome-wide genotyping and gene expression measurements for a subset of 313. We build failing and non-failing cardiac regulatory gene networks, revealing important regulators and cardiac expression quantitative trait loci (eQTLs). PPP1R3A emerges as a regulator whose network connectivity changes significantly between health and disease. RNA sequencing after PPP1R3A knockdown validates network-based predictions, and highlights metabolic pathway regulation associated with increased cardiomyocyte size and perturbed respiratory metabolism. Mice lacking PPP1R3A are protected against pressure-overload heart failure. We present a global gene interaction map of the human heart failure transition, identify previously unreported cardiac eQTLs, and demonstrate the discovery potential of disease-specific networks through the description of PPP1R3A as a central regulator in heart failure.
- Published
- 2019
4. The Household Secondary Attack Rate of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): A Rapid Review
- Author
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Fung, Hannah F, Martinez, Leonardo, Alarid-Escudero, Fernando, Salomon, Joshua A, Studdert, David M, Andrews, Jason R, Goldhaber-Fiebert, Jeremy D, Chin, Elizabeth T, Claypool, Anneke L, Fernandez, Mariana, Gracia, Valeria, Luviano, Andrea, Rosales, Regina Isabel Medina, Reitsma, Marissa, and Ryckman, Theresa
- Subjects
Microbiology (medical) ,medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,medicine.disease_cause ,household transmission ,Major Article ,medicine ,Humans ,Transmission risks and rates ,Aged ,Coronavirus ,Family Characteristics ,Motivation ,SARS-CoV-2 ,Transmission (medicine) ,business.industry ,Incidence ,Incidence (epidemiology) ,Public health ,COVID-19 ,Confidence interval ,AcademicSubjects/MED00290 ,secondary attack ,Infectious Diseases ,business ,testing frequency ,Demography - Abstract
BackgroundAlthough much of the public health effort to combat coronavirus disease 2019 (COVID-19) has focused on disease control strategies in public settings, transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within households remains an important problem. The nature and determinants of household transmission are poorly understood.MethodsTo address this gap, we gathered and analyzed data from 22 published and prepublished studies from 10 countries (20 291 household contacts) that were available through 2 September 2020. Our goal was to combine estimates of the SARS-CoV-2 household secondary attack rate (SAR) and to explore variation in estimates of the household SAR.ResultsThe overall pooled random-effects estimate of the household SAR was 17.1% (95% confidence interval [CI], 13.7–21.2%). In study-level, random-effects meta-regressions stratified by testing frequency (1 test, 2 tests, >2 tests), SAR estimates were 9.2% (95% CI, 6.7–12.3%), 17.5% (95% CI, 13.9–21.8%), and 21.3% (95% CI, 13.8–31.3%), respectively. Household SARs tended to be higher among older adult contacts and among contacts of symptomatic cases.ConclusionsThese findings suggest that SARs reported using a single follow-up test may be underestimated, and that testing household contacts of COVID-19 cases on multiple occasions may increase the yield for identifying secondary cases.
- Published
- 2020
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