title: Estimating incidence curves of several infections using symptom surveillance data.
abstract: We introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate.
authors: Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America. egoldste@hsph.harvard.edu
authors: Edward
authors: E
authors: Goldstein
authors: Benjamin J
authors: BJ
authors: Cowling
authors: Allison E
authors: AE
authors: Aiello
authors: Saki
authors: S
authors: Takahashi
authors: Gary
authors: G
authors: King
authors: Ying
authors: Y
authors: Lu
authors: Marc
authors: M
authors: Lipsitch
mesh_terms: D003141:Communicable Diseases / Q000150:complications / Q000453:epidemiology*; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D005334:Fever / Q000150:complications / Q000453:epidemiology; D006306:Health Surveys; D006801:Humans; D015994:Incidence; D007251:Influenza, Human / Q000150:complications / Q000453:epidemiology*; D008824:Michigan / Q000453:epidemiology; D011159:Population Surveillance; D014495:Universities