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_37
Snippet: As COVID new case countsz it represent the number of confirmed cases in an entire state and ILINet data represents the number of cases seen by a select number of enrolled providers, we must estimate scaling factors w i to enable comparison of ILINet data to confirmed case counts at the who state level. Let π * it denote the probability that a patient with ILI in state i has COVID as estimated from ILINet data. Let p i denote the population of st.....
Document: As COVID new case countsz it represent the number of confirmed cases in an entire state and ILINet data represents the number of cases seen by a select number of enrolled providers, we must estimate scaling factors w i to enable comparison of ILINet data to confirmed case counts at the who state level. Let π * it denote the probability that a patient with ILI in state i has COVID as estimated from ILINet data. Let p i denote the population of state i and let b i denote the number of primary care providers per 100,000 people in state i. We simulated the number of COVID cases (excess ILI meeting criteria above) as
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