Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion) Page 1 Bayesian Analysis (2019) 14, Number 1, pp. 261–312 Dynamic …
Rapidly detecting the beginning of influenza outbreaks helps health authorities to reduce their impact. Accounting for the spatial distribution of the data can greatly improve the …
A Corberán-Vallet - Statistical Methods in Medical Research, 2012 - journals.sagepub.com
Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of …
Reliable surveillance models are an important tool in public health because they aid in mitigating disease outbreaks, identify where and when disease outbreaks occur, and predict …
Civil unrest is a complicated, multifaceted social phenomenon that is difficult to forecast. Relevant data for predicting future protests consist of a massive set of heterogeneous …
J Zou, AF Karr, G Datta, J Lynch, S Grannis - BMC Medical Informatics and …, 2014 - Springer
Background For researchers and public health agencies, the complexity of high– dimensional spatio–temporal data in surveillance for large reporting networks presents …
DB Neill - Handbook of Scan Statistics, 2024 - Springer
In this chapter we describe Bayesian scan statistics, a class of methods which build both on the prior literature on scan statistics and on Bayesian approaches to cluster detection and …
F Vial, S Thommen, L Held - Epidemiology & Infection, 2015 - cambridge.org
Syndromic surveillance (SyS) systems currently exploit various sources of health-related data, most of which are collected for purposes other than surveillance (eg economic) …
J Zou, AF Karr, D Banks, MJ Heaton… - … Analysis and Data …, 2012 - Wiley Online Library
Early and accurate detection of outbreaks is one of the most important objectives of syndromic surveillance systems. We propose a general Bayesian framework for syndromic …