Research

Disease forecasting

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. 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. These forecasts are based on over 2 million unique reported case records of dengue fever in Thailand since 1968.

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Statistical Methods for Pathogen Interactions

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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Cluster-randomized Trial Design

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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Estimating Incubation Period Distributions

The incubation period -- the length of time between infection with a pathogen and the onset of symptoms -- plays a vital role in the prevention and control of infectious disease. Estimating the incubation period can be challenging because often both the time of infection and the time of onset are not observed exactly. Our work has developed robust statistical methods to estimate the full duration of the incubation period. Recent work focuses on characterizing the uncertainty in incubation period estimates in ways that could assist in creating evidence-based quarantine policies for infectious diseases, such as Ebola or influenza.

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