Research

Our team is working to develop statistical methods and tools that can improve real-time infectious disease forecasting efforts for a variety of diseases, including dengue fever and influenza. We focus on building robust and interpretable models that can be used by public health officials to drive policy decisions. For example, in a collaboration with the Ministry of Public Health in Thailand, our team has built statistical forecast models to predict outbreaks of dengue fever in real-time since early 2014 for each of the 77 provinces in Thailand. From a methodological perspective we work on developing ensemble methodology (fusing together forecasts from multiple models) and on forecasting methods that can account for reporting delays in surveillance data.


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Code and manuscript for adaptively weighted ensembles via stacking
The code and data required to make annual predictions for Thailand
Kernel conditional density estimation with flexible kernel specifications.
Code and submissions for 2017-2018 CDC flu prediction contest
Guidelines and forecasts for a collaborative U.S. influenza forecasting project.
Code and public data for "Challenges in real-time prediction of infectious disease: a case study of dengue in Thailand"

In multi-pathogen infectious disease systems, complex immunological interactions between multiple strains of disease govern the evolutionary and epidemiological dynamics of disease. Understanding these interactions plays a vital role in clinical and public health decision-making. Our work combines multiple data streams from complex disease systems with modern statistical and computational methodologies to find evidence of complex interactions within the system. For example, our research was the first to use population-level data to explicitly estimate the duration of temporary immunity experienced by individuals after an infection with one of the four serotypes of dengue fever.


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Modeling of costs and risks of active monitoring during pandemics.
Code and modeled data for JRSI paper on estimating cross-protection between dengue serotypes.
An R package for analyzing censored and under-reported surveillance data.
Inference with Particle Filtering
We are using particle filtering to fit complex models of disease transmission to better estimate parameters governing pathogen interactions, e.g. cross-protection or enhancement between dengue serotypes.

We build open-source software motivated by our research projects with the hope that you and others will find these tools useful. From D3 visualization platforms to SQL database utilities to R and python packages, we develop a diverse set of software that help us do better research. In addition to the projects listed above, lab members have also contributed to the development of Stan and the ForecastFramework R package.


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Interactive visualization for time series forecasts
Database summary diff reporting tool
MMWR weeks for Python
Implementation of the ALERT algorithm for detecting the onset of influenza season.
the clusterPower package for R: simulating power and sample size calculations for cluster-randomized trials
An R package for analyzing censored and under-reported surveillance data.

Cluster-randomized trials are a type of clinical trial where clusters of individuals are randomized instead of individuals. In our work on several high-profile cluster-randomized clinical trials – including the ResPECT Study (funded by CDC and VA), the SCRUB Trial, and a telehealth collaborative care trial for HIV patients (funded by VA) – we have developed simulation methods for calculating power for cluster-randomized and cluster-randomized crossover trials. These methods are available as an R software package, clusterPower.


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the clusterPower package for R: simulating power and sample size calculations for cluster-randomized trials
The ResPECT Study
A multi-site cluster-randomized trial to evaluate the comparative effectiveness of health care workers wearing surgical masks or N95 respirators to prevent respiratory infections during influenza season.