Person:
Reich, Nicholas

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Job Title
Assistant Professor Department of Biostatistics, School of Public Health and Health Sciences
Last Name
Reich
First Name
Nicholas
Discipline
Biostatistics
Public Health
Expertise
Biostatistics
Cluster-randomized trials
Infectious disease modeling
Introduction
I am an Assistant Professor of Biostatistics in the Division of Biostatistics and Epidemiology at the University of Massachusetts School of Public Health and Health Sciences in Amherst, MA. Motivated by global health problems, my research unites principles of statistics with the practice of epidemiology. Active areas of research include developing time series and survival models for the spread of infectious disease, developing statistical methods for the analysis of disease surveillance data, and optimizing design and analysis strategies for cluster-randomized studies.
I graduated in 2001 from Carleton College with a B.A. in English. Before enrolling in the Biostatistics Ph.D. program at Johns Hopkins, I worked for Harper's Magazine in New York City, San Francisco's City Carshare, and the Framingham Heart Study.
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Now showing 1 - 2 of 2
  • Publication
    Evaluating Epidemic Forecasts in an Interval Format
    (2021-01-01) Bracher, Johannes; Ray, Evan L.; Gneiting, Tilmann; Reich, Nicholas G.
    For practical reasons, many forecasts of case, hospitalization, and death counts in the context of the current Coronavirus Disease 2019 (COVID-19) pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction. Author summary During the COVID-19 pandemic, model-based probabilistic forecasts of case, hospitalization, and death numbers can help to improve situational awareness and guide public health interventions. The COVID-19 Forecast Hub (https://covid19forecasthub.org/) collects such forecasts from numerous national and international groups. Systematic and statistically sound evaluation of forecasts is an important prerequisite to revise and improve models and to combine different forecasts into ensemble predictions. We provide an intuitive introduction to scoring methods, which are suitable for the interval/quantile-based format used in the Forecast Hub, and compare them to other commonly used performance measures.
  • Publication
    A cell–ECM screening method to predict breast cancer metastasis
    (2015-01-01) Barney, Lauren E.; Dandley, E. C.; Jansen, Lauren; Reich, Nicholas G.; Mercurio, A. M.; Peyton, Shelly
    Breast cancer preferentially spreads to the bone, brain, liver, and lung. The clinical patterns of this tissue-specific spread (tropism) cannot be explained by blood flow alone, yet our understanding of what mediates tropism to these physically and chemically diverse tissues is limited. While the micro- environment has been recognized as a critical factor in governing metastatic colonization, the role of the extracellular matrix (ECM) in mediating tropism has not been thoroughly explored. We created a simple biomaterial platform with systematic control over the ECM protein density and composition to determine if integrin binding governs how metastatic cells differentiate between secondary tissue sites. Instead of examining individual behaviors, we compiled large patterns of phenotypes associated with adhesion to and migration on these controlled ECMs. In combining this novel analysis with a simple biomaterial platform, we created an in vitro fingerprint that is predictive of in vivo metastasis. This rapid biomaterial screen also provided information on how b1, a2, and a6 integrins might mediate metastasis in patients, providing insights beyond a purely genetic analysis. We propose that this approach of screening many cell–ECM interactions, across many different heterogeneous cell lines, is predictive of in vivo behavior, and is much simpler, faster, and more economical than complex 3D environments or mouse models. We also propose that when specifically applied toward the question of tissue tropism in breast cancer, it can be used to provide insight into certain integrin subunits as therapeutic targets.