Author: Justin D Silverman; Alex D Washburne
Title: Using ILI surveillance to estimate state-specific case detection rates and forecast SARS-CoV-2 spread in the United States Document date: 2020_4_3
ID: 17oac3bg_32
Snippet: We identified ILI surges inỹ it by training a model onỹ it for all data prior to July 21, 2019. We then 230 used this model to predict the prevalence of non-influenza ILI (π it ) for dates after and including July 21, 2019. We calculated the ILI surge as the difference between the observed proportion of non-influenza iliỹ it /n it andπ it . More specifically, to account for variation in the number of reporting providers, we trained the 5 .....
Document: We identified ILI surges inỹ it by training a model onỹ it for all data prior to July 21, 2019. We then 230 used this model to predict the prevalence of non-influenza ILI (π it ) for dates after and including July 21, 2019. We calculated the ILI surge as the difference between the observed proportion of non-influenza iliỹ it /n it andπ it . More specifically, to account for variation in the number of reporting providers, we trained the 5 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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