Time series data involving counts are frequently encountered in many biomedical and public health applications. For example, in disease surveillance, the occurrence of rare infections over time is often monitored by public health officials, and the time series data collected can be used for the purpose of monitoring changes in disease activity. For rare diseases with low infection rates, the observed counts typically contain a high frequency of zeros (zero-inflated), but the counts can also be very large (overdispersed) during an outbreak period. Failure to account for zero-inflation and overdispersion in the data may result in misleading inference and the detection of spurious associations.
Objective
The purpose of this study is to develop novel statistical methods to analyze zero-inflated and overdispersed time series consisting of count data.