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Kissler, Stephen

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Kissler

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Kissler, Stephen
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    Densely sampled viral trajectories suggest longer duration of acute infection with B.1.1.7 variant relative to non-B.1.1.7 SARS-CoV-2
    (2021-02-16) Kissler, Stephen; Fauver, Joseph R.; Mack, Christina; Tai, Caroline G.; Breban, Mallery I.; Watkins, Anne E.; Samant, Radhika M.; Anderson, Deverick J.; Ho, David D.; Grubaugh, Nathan D.; Grad, Yonatan
    To test whether acute infection with B.1.1.7 is associated with higher or more sustained nasopharyngeal viral concentrations, we assessed longitudinal PCR tests performed in a cohort of 65 individuals infected with SARS-CoV-2 undergoing daily surveillance testing, including seven in fected with B.1.1.7. For individuals infected with B.1.1.7, the mean duration of the proliferation phase was 5.3 days (90% credible interval [2.7, 7.8]), the mean duration of the clearance phase was 8.0 days [6.1, 9.9], and the mean overall duration of infection (proliferation plus clearance) was 13.3 days [10.1, 16.5]. These compare to a mean proliferation phase of 2.0 days [0.7, 3.3], a mean clearance phase of 6.2 days [5.1, 7.1], and a mean duration of infection of 8.2 days [6.5, 9.7] for non-B.1.1.7 virus. The peak viral concentration for B.1.1.7 was 19.0 Ct [15.8, 22.0] compared to 20.2 Ct [19.0, 21.4] for non-B.1.1.7. This converts to 8.5 log10 RNA copies/ml [7.6, 9.4] for B.1.1.7 and 8.2 log10 RNA copies/ml [7.8, 8.5] for non-B.1.1.7. These data offer evidence that SARS-CoV-2 variant B.1.1.7 may cause longer infections with similar peak viral concentration compared to non-B.1.1.7 SARS-CoV-2. This extended duration may contribute to B.1.1.7 SARS CoV-2’s increased transmissibility.
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    Reductions in commuting mobility predict geographic differences in SARS-CoV-2 prevalence in New York City
    (2020) Kissler, Stephen; Kishore, Nishant; Prabhu, Malavika; Goffman, Dena; Beilin, Yaakov; Landau, Ruth; Gyamfi-Bannerman, Cynthia; Bateman, Brian; Katz, Daniel; Gal, Jonathan; Bianco, Angela; Stone, Joanne; Larremore, Daniel; Buckee, Caroline; Grad, Yonatan
    Importance: New York City is the epicenter of the SARS-CoV-2 pandemic in the United States. Mortality and hospitalizations have differed substantially between different neighborhoods. Mitigation efforts in the coming months will require knowing the extent of geographic variation in SARS-CoV-2 prevalence and understanding the drivers of these differences. Objective: To estimate the prevalence of SARS-CoV-2 infection by New York City borough between March 22nd and May 3rd, 2020, and to associate variation in prevalence with antecedent reductions in mobility, defined as aggregated daily physical movements into and out of each borough. Design: Observational study of universal SARS-CoV-2 test results obtained from women hospitalized for delivery. Setting: Four New York-Presbyterian hospital campuses and two Mount Sinai hospital campuses in New York City. Participants: 1,746 women with New York City ZIP codes hospitalized for delivery. Exposures: Infection with SARS-CoV-2. Main outcomes: Population prevalence of SARS-CoV-2 by borough and correlation with the reduction in daily commuting-style movements into and out of each borough. Results: The estimated population prevalence of SARS-CoV-2 ranged from 11.3% (95% credible interval 8.9%, 13.9%) in Manhattan to 26.0% (95% credible interval 15.3%, 38.9%) in South Queens, with an estimated city-wide prevalence of 15.6% (95% credible interval 13.9%, 17.4%). The peak city-wide prevalence was during the week of March 30th, though temporal trends in prevalence varied substantially between boroughs. Population revalence was lowest in boroughs with the greatest reductions in morning commutes out of and evening commutes into the borough (Pearson R = –0.88, 95% credible interval –0.52, –0.99). Conclusions and relevance: Reductions in between-borough mobility predict geographic differences in the prevalence of SARS-CoV-2 infection in New York City. Large parts of the city may remain at risk for substantial SARS-CoV-2 outbreaks. Widespread testing should be conducted to identify geographic disparities in prevalence and assess the risk of future outbreaks.
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    Combining fine-scale social contact data with epidemic modelling reveals interactions between contact tracing, quarantine, testing and physical distancing for controlling COVID-19
    (Cold Spring Harbor Laboratory, 2020-05-27) Firth, Josh A; Hellewell, Joel; Klepac, Petra; Kissler, Stephen; Kucharski, Adam J; Spurgin, Lewis G.
    Case isolation and contact tracing can contribute to the control of COVID-19 outbreaks1,2. However, it remains unclear how real-world networks could influence the effectiveness and efficiency of such approaches. To address this issue, we simulated control strategies for SARS-CoV-2 in a real-world social network generated from high resolution GPS data3,4. We found that tracing contacts-of-contacts reduced the size of simulated outbreaks more than tracing of only contacts, but resulted in almost half of the local population being quarantined at a single point in time. Testing and releasing non-infectious individuals led to increases in outbreak size, suggesting that contact tracing and quarantine may be most effective when it acts as a ‘local lockdown’ when contact rates are high. Finally, we estimated that combining physical distancing with contact tracing could enable epidemic control while reducing the number of quarantined individuals. Our findings therefore suggest that targeted tracing and quarantine strategies may be most efficient when combined with other control measures such as physical distancing.
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    Social distancing strategies for curbing the COVID-19 epidemic
    (2020-03) Kissler, Stephen; Tedijanto, Christine; Lipsitch, Marc; Grad, Yonatan
    The SARS-CoV-2 pandemic is straining healthcare resources worldwide, prompting social distancing measures to reduce transmission intensity. The amount of social distancing needed to curb the SARS-CoV-2 epidemic in the context of seasonally varying transmission remains unclear. Using a mathematical model, we assessed that one-time interventions will be insufficient to maintain COVID-19 prevalence within the critical care capacity of the United States. Seasonal variation in transmission will facilitate epidemic control during the summer months but could lead to an intense resurgence in the autumn. Intermittent distancing measures can maintain control of the epidemic, but without other interventions, these measures may be necessary into 2022. Increasing critical care capacity could reduce the duration of the SARS-CoV-2 epidemic while ensuring that critically ill patients receive appropriate care.
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    Drug addiction mutations unveil a repressive methylation ceiling in EZH2-mutant lymphoma
    (Research Square Platform LLC, 2022-07-22) Kwok, Hui Si; Freedy, Allyson M.; Siegenfeld, Allison P.; Morriss, Julia; Waterbury, Amanda L.; Kissler, Stephen; Liau, Brian B.
    Drug addiction, a phenomenon where cancer cells paradoxically depend on continuous drug treatment for survival, has uncovered cell signaling mechanisms and cancer co-dependencies. Here, we discover mutations that confer drug addiction to inhibitors of the transcriptional repressor Polycomb Repressive Complex 2 (PRC2) in diffuse large B-cell lymphoma (DLBCL). Drug addiction is mediated by hypermorphic mutations in the CXC domain of the catalytic subunit EZH2, which maintain H3K27me3 levels even in the presence of PRC2 inhibitors. Drug discontinuation leads to overspreading of H3K27me3, surpassing a repressive methylation ceiling compatible with lymphoma cell survival. Exploiting this vulnerability, we show that inhibition of SETD2 induces H3K27me3 spreading and blocks lymphoma growth. Collectively, our findings demonstrate that fundamental constraints on chromatin landscapes can yield biphasic dependencies in epigenetic signaling in cancer cells. More broadly, we highlight how approaches to identify drug addiction mutations can be leveraged to discover cancer vulnerabilities and cell signaling thresholds.
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    Viral dynamics and duration of PCR positivity of the SARS-CoV-2 Omicron variant
    (2022) Hay, James; Kissler, Stephen; Fauver, Joseph R.; Mack, Christina; Tai, Caroline G.; Samant, Radhika M.; Connelly, Sarah; Anderson, Deverick J.; Khullar, Gaurav; MacKay, Matthew; Patel, Miral; Kelly, Shannan; Manhertz, April; Eiter, Isaac; Salgado, Daisy; Baker, Tim; Dudley, Joel T.; Mason, Christopher E.; Ho, David D.; Grubaugh, Nathan D.; Grad, Yonatan
    Background: The Omicron SARS-CoV-2 variant is responsible for a major wave of COVID-19, with record case counts reflecting high transmissibility and escape from prior immunity. Defining the time course of Omicron viral proliferation and clearance is crucial to inform isolation protocols aiming to minimize disease spread. Methods: We obtained longitudinal, quantitative RT-qPCR test results using combined anterior nares and oropharyngeal samples (n = 10,324) collected between July 5th, 2021 and January 10th, 2022 from the National Basketball Association’s (NBA) occu-pational health program. We quantified the fraction of tests with PCR cycle threshold (Ct) values <30, chosen as a proxy for potential infectivity and antigen test positivity, on each day after first detection of suspected and confirmed Omicron infections, stratified by individuals detected under frequent testing protocols and those detected due to symptom onset or concern for contact with an infected individual. We quantified the du-ration of viral proliferation, clearance rate, and peak viral concentration for individuals with acute Omicron and Delta variant SARS-CoV-2 infections. Results: A total of 97 infections were confirmed or suspected to be from the Omicron variant and 107 from the Delta variant. Of 27 Omicron-infected individuals testing posi-tive ≤1 day after a previous negative or inconclusive test, 52.0% (13/25) were PCR positive with Ct values <30 at day 5, 25.0% (6/24) at day 6, and 13.0% (3/23) on day 7 post detection. Of 70 Omicron-infected individuals detected ≥2 days after a previous negative or inconclusive test, 39.1% (25/64) were PCR positive with Ct values <30 at day 5, 33.3% (21/63) at day 6, and 22.2% (14/63) on day 7 post detection. Overall, Omicron infections featured a mean duration of 9.87 days (95% CI 8.83-10.9) relative to 10.9 days (95% CI 9.41-12.4) for Delta infections. The peak viral RNA based on Ct values was lower for Omicron infections than for Delta infections (Ct 23.3, 95% CI 22.4-24.3 for Omicron; Ct 20.5, 95% CI 19.2-21.8 for Delta) and the clearance phase was shorter for Omicron infections (5.35 days, 95% CI 4.78-6.00 for Omicron; 6.23 days, 95% CI 5.43-7.17 for Delta), though the rate of clearance was similar (3.13 Ct/day, 95% CI 2.75-3.54 for Omicron; 3.15 Ct/day, 95% CI 2.69-3.64 for Delta). Conclusions: While Omicron infections feature lower peak viral RNA and a shorter clearance phase than Delta infections on average, it is unclear to what extent these differences are attributable to more immunity in this largely vaccinated population or intrinsic characteristics of the Omicron variant. Further, these results suggest that Omi-cron’s infectiousness may not be explained by higher viral load measured in the nose and mouth by RT-PCR. The substantial fraction of individuals with Ct values <30 at days 5 of infection, particularly in those detected due to symptom onset or concern for contact with an infected individual, underscores the heterogeneity of the infectious pe-riod, with implications for isolation policies.
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    Surveillance to maintain the sensitivity of genotype-based antibiotic resistance diagnostics
    (Public Library of Science (PLoS), 2019-11-12) Hicks, Allison; Kissler, Stephen; Lipsitch, Marc; Grad, Yonatan
    The sensitivity of genotype-based diagnostics that predict antimicrobial susceptibility is limited by the extent to which they detect genes and alleles that lead to resistance. As novel resistance variants are expected to emerge, such sensitivity is expected to decline unless the new variants are detected and incorporated into the diagnostic. Here, we present a mathematical framework to define how many diagnostic failures may be expected under varying surveillance regimes and thus quantify the surveillance needed to maintain the sensitivity of genotype-based diagnostics.
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    Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period
    (2020-03-06) Kissler, Stephen; Tedijanto, Christine; Goldstein, Edward; Grad, Yonatan; Lipsitch, Marc
    There is an urgent need to project how transmission of the novel betacoronavirus SARS-CoV-2 will unfold in coming years. These dynamics will depend on seasonality, the duration of immunity, and the strength of cross-immunity to/from the other human coronaviruses. Using data from the United States, we measured how these factors affect transmission of human betacoronaviruses HCoV-OC43 and HCoV-HKU1. We then built a mathematical model to simulate transmission of SARS-CoV-2 through the year 2025. We project that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after an initial pandemic wave. We summarize the full range of plausible transmission scenarios and identify key data still needed to distinguish between them, most importantly longitudinal serological studies to determine the duration of immunity to SARS-CoV-2.
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    Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys
    (2020) Larremore, Daniel B.; Fosdick, Bailey K.; Bubar, Kate M.; Zhang, Sam; Kissler, Stephen; Metcalf, C. Jessica E.; Buckee, Caroline; Grad, Yonatan
    Establishing how many people have already been infected by SARS-CoV-2 is an urgent priority for controlling the COVID-19 pandemic. Patchy virological testing has hampered interpretation of confirmed case counts, and unknown rates of asymptomatic and mild infections make it challenging to develop evidence-based public health policies. Serological tests that identify past infection can be used to estimate cumulative incidence, but the relative accuracy and robustness of various sampling strategies has been unclear. Here, we used a flexible framework that integrates uncertainty from test characteristics, sample size, and heterogeneity in seroprevalence across tested subpopulations to compare estimates from sampling schemes. Using the same framework and making the assumption that serological positivity indicates immune protection, we propagated these estimates and uncertainty through dynamical models to assess the uncertainty in the epidemiological parameters needed to evaluate public health interventions. We examined the relative accuracy of convenience samples versus structured surveys to estimate population seroprevalence, and found that sampling schemes informed by demographics and contact networks outperform uniform sampling. The framework can be adapted to optimize the design of serological surveys given particular test characteristics and capacity, population demography, sampling strategy, and modeling approach, and can be tailored to support decision-making around introducing or removing interventions.