A time series aggregation model for predicting the incidence of syphilis.
The incidence of infectious syphilis is much different from the number of reported cases in any given year; therefore, an estimate of the incidence is needed to formulate effective control programs for the future. A time series aggregation model that predicts the incidence of syphilis was developed. The model accounts for the number of treated but unreported cases, and it provides estimates of incidence for different age and racial groups. As an illustration the model was applied for estimation of the incidence of syphilis in the city of Chicago, Illinois. A sensitivity analysis revealed that a 10% variation in input parameters would cause an error of smaller than or equal to 4.1% in the estimates of incidence.
D000367:Age Factors; D002641:Chicago; D004812:Epidemiologic Methods; D005006:Ethnicity; D005544:Forecasting; D006801:Humans; D008962:Models, Theoretical; D011336:Probability; D013587:Syphilis / Q000453:epidemiology* / Q000503:physiopathology