Online Program Home
My Program

Abstract Details

Activity Number: 375
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #320780
Title: Nonparametric Prediction of Infectious Disease Incidence with Kernel Conditional Density Estimation
Author(s): Evan Ray* and Krzysztof Sakrejda and Stephen A. Lauer and Nicholas G. Reich
Companies: University of Massachusetts and University of Massachusetts - Amherst and University of Massachusetts - Amherst and University of Massachusetts - Amherst
Keywords: kernel conditional density estimation ; infectious disease ; prediction

Prediction of infectious disease dynamics is important to public health officials planning resource allocation and interventions. We develop methods for predicting disease incidence using kernel conditional density estimation (KCDE). We introduce several novel ideas in our formulation of KCDE. First, we use a discretized multivariate Gaussian kernel function which allows us to estimate the distribution of count data while using a fully parameterized bandwidth matrix. Second, we use low-pass filtered observations of lagged incidence as conditioning variables to mitigate the effects of noise that obscures the short-term trend in incidence. Third, we use periodic transformations of the observation time as conditioning variables to capture seasonality. We estimate the bandwidth and filtering parameters using cross-validation. We apply the method to prediction of influenza in the United States and Dengue fever in San Juan, Puerto Rico, and demonstrate that our contributions yield improvements in the log score of the predictive distribution relative to a naive application of KCDE and to a baseline seasonal ARIMA model.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association