TY - JOUR
AU - Kim, Joanne
AU - Lawson, Andrew B.
AU - Neelon, Brian
AU - Korte, Jeffrey E
AU - Eberth, Jan M
AU - Chowell, Gerardo
PY - 2023
DA - July
TI - Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis
JO - BMC Medical Research Methodology
VL - 23
DO - https://doi.org/10.1186/s12874-023-01987-5
AB - BACKGROUND: COVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance.
PB - Springer Nature
UR - https://www.research.ed.ac.uk/en/publications/652b3e91-bdee-4989-8785-3067bdb698a2
KW - Coronavirus (COVID-19)
ER