@article{Kim_J-2023_151863,
title = {Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis},
author = {Kim, J. and Lawson, A. and Neelon, B. and Korte, J. and Eberth, J. and Chowell, G.},
month = {jul},
year = {2023},
abstract = {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.},
volume = {23},
journal = {BMC Medical Research Methodology},
publisher = {Springer Nature},
url = {https://doi.org/10.1186/s12874-023-01987-5},
}
