Person: Kennedy-Shaffer, Lee
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Kennedy-Shaffer
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Lee
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Kennedy-Shaffer, Lee
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Publication Perfect as the Enemy of the Good: Using Low-Sensitivity Tests to Mitigate SARS-CoV-2 Outbreaks(2020) Kennedy-Shaffer, Lee; Baym, Michael; Hanage, WilliamPreventing future infection waves of COVID-19 will depend on effective and efficient contact tracing. SARS-CoV-2 transmission appears to be characterized by high individual variation and a large role of superspreading events. Taking this into account can improve the cost-benefit tradeoffs of contact tracing. In particular, an individual who is known to have transmitted the infection once is more likely to have transmitted to other individuals. We propose a strategy of identifying transmission events, making use of the variability in secondary case numbers. A rapid, high-specificity test with only 50% sensitivity can still identify the vast majority of these transmission events. This strategy can lead to the isolation of a large proportion of infected individuals while drastically reducing the isolation of uninfected contacts.Publication How to detect and reduce potential sources of biases in epidemiologic studies of SARS-CoV-2(2020-11-10) Accorsi, Emma; Qiu, Xueting; Rumpler, Eva; Kennedy-Shaffer, Lee; Kahn, Rebecca; Joshi, Keya; Goldstein, Edward; Stensrud, Mats J.; Niehus, Rene; Cevik, Muge; Lipsitch, MarcIn response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.Publication Estimating epidemiologic dynamics from single cross-sectional viral load distributions(2020-09-26) Hay, James; Kennedy-Shaffer, Lee; Kanjilal, Sanjat; Lipsitch, Marc; Mina, MichaelVirologic testing for SARS-CoV-2 has been central to the COVID-19 pandemic response, but interpreting changes in incidence and fraction of positive tests towards understanding the epidemic trajectory is confounded by changes in testing practices. Here, we show that the distribution of viral loads, in the form of Cycle thresholds (Ct), from positive surveillance samples at a single point in time can provide accurate estimation of an epidemic’s trajectory, subverting the need for repeated case count measurements which are frequently obscured by changes in testing capacity. We identify a relationship between the population-level cross-sectional distribution of Ct values and the growth rate of the epidemic, demonstrating how the skewness and median of detectable Ct values change purely as a mathematical epidemiologic rule without any change in individual level viral load kinetics or testing. Although at the individual level measurement variation can complicate interpretation of Ct values for clinical use, we show that population-level properties reflect underlying epidemic dynamics. In support of these theoretical findings, we observe a strong relationship between the time-varying effective reproductive number, R(t), and the distribution of Cts among positive surveillance specimens, including median and skewness, measured in Massachusetts over time. We use the observed relationships to derive a novel method that allows accurate inference of epidemic growth rate using the distribution of Ct values observed at a single cross-section in time, which, unlike estimates based on case counts, is less susceptible to biases from delays in test results and from changing testing practices. Our findings suggest that instead of discarding individual Ct values from positive specimens, incorporation of viral loads into public health data streams offers a new approach for real-time resource allocation and assessment of outbreak mitigation strategies, even where repeat incidence data is not available. Ct values or similar viral load data should be regularly reported to public health officials by testing centers and incorporated into monitoring programs.