Welcome to the lab website for Nicholas Reich

Biostatistics and Public Health

Our team develops statistical methods and analytical tools to help people make sense of data. Building on close collaborative relationships with public health practitioners from across the world -- from Denver to San Juan to Bangkok -- we use modern statistical and machine learning tools to gain insights into complex disease systems.

Data Science

We build software packages, maintain code repositories, develop SQL databases, create interactive data visualizations, and run computationally intensive simulation studies. We use the R programming language for most of our work, but also use some C, C++, Perl, and Python.

Check out our work on GitHub.

Infectious Disease Epidemiology

Our team develops models for understanding complex and dynamic systems of infectious disease. We have developed real-time forecasts of dengue fever in Thailand, estimated the duration of cross-protection between serotypes of dengue, and predicted the start of the flu season using hospital data.

From the blog...

Using the DELPHI API to access infectious disease data

This week I attended a workshop at the CDC about last year’s FluSight challenge, a competition that scores weekly real-time predictions about the course of the influenza season. They are planning another round this year and are hoping to increase the number of teams particiating. Stay tuned to this site for more info.

At the workshop, I learned about DELPHI’s real-time epidemiological data API. The API is linked to various data sources on influenza and dengue, including US CDC flu data, Google Flu Trends, and Wikipedia data. There is some documentation and minimal examples, and this post documents a more robust and complete example for using the API via R. I’ll note that the CDC’s influenza data, can also be accessed via the cdcfluview R package, which I’m not going to discuss here and I will focus here on accessing some of the other data sources. Here’s a teaser of this data that you can also interactively explore on the DELPHI EpiVis website:

Posted on 01 September, 2016

Five College DataFest recap: tips for next year

Another Five College ASA DataFest has long come and gone, and I’ve been meaning to write a recap for a while. Now in its third year in the Pioneer Valley in Western Massachusetts, the number of registrants doubled from last year, from 70 to 140. All Five Colleges (Amherst, Hampshire, Mt. Holyoke, Smith, and UMass-Amherst) sent multiple teams, and there were a few teams with a mix of students from different schools.

Team “Beta than U” from UMass-Amherst took home one of the Best in Group awards. From left to right: Laura Bowles, Vincent Lee, Harley Jean, Bianca Agustin, and Stephanie Crowley.

Posted on 04 April, 2016

Evidence for Ebola active monitoring policies

Sheri Fink published this nice piece in the New York Times yesterday on the legal issues surrounding state-imposed quarantines on travelers returning from countries with widespread Ebola transmission. In addition to the toll these policies have had on the individuals who have been put under quarantine, I took away from this article that there is still a need for better data on and communication about the risks of travelers being infected with Ebola. As it happens, this is the topic of my talk today at the Epidemics5 conference.

Posted on 03 December, 2015

ASTMH 2015 Presentation: Real-time prediction of dengue fever in Thailand

Last week, I had the honor of presenting at the 64th Annual Meeting of the American Society of Tropical Medicine & Hygiene (ASTMH) in the well-attended Dengue: Epidemiology session. This presentation covers our work with the Thai Ministry of Public Health and Johns Hopkins University in building an infrastructure for making real-time dengue hemorrhagic fever case predictions and evaluating the performance of our predictions thus far.

You can find the slides for the presentation here. After the jump, I’ll provide a slide-by-slide summary. To view the paper associated with this work, you can check it out on arXiv.

Posted on 17 November, 2015

Strange bedfellows: methods for predicting the NBA and flu

FiveThirtyEight’s new CARMELO prediction alorithm, that projects the future careers of every NBA player, has similarity with prediction methods in other fields.

Posted on 12 October, 2015

Contributing to STAN

In a feat of focused coding jujitsu, Krzysztof successfully put together a pull-request to the base development version of STAN.

Posted on 10 September, 2015

Dengue forecasting competition

The lab participated in the Dengue Forecasting Project, hosted by various federal government agencies.

Posted on 17 August, 2015

Summer activities

The Reich Lab has had a busy summer!

Posted on 16 August, 2015