title: Using an adjusted Serfling regression model to improve the early warning at the arrival of peak timing of influenza in Beijing.
abstract: Serfling-type periodic regression models have been widely used to identify and analyse epidemic of influenza. In these approaches, the baseline is traditionally determined using cleaned historical non-epidemic data. However, we found that the previous exclusion of epidemic seasons was empirical, since year-year variations in the seasonal pattern of activity had been ignored. Therefore, excluding fixed 'epidemic' months did not seem reasonable. We made some adjustments in the rule of epidemic-period removal to avoid potentially subjective definition of the start and end of epidemic periods. We fitted the baseline iteratively. Firstly, we established a Serfling regression model based on the actual observations without any removals. After that, instead of manually excluding a predefined 'epidemic' period (the traditional method), we excluded observations which exceeded a calculated boundary. We then established Serfling regression once more using the cleaned data and excluded observations which exceeded a calculated boundary. We repeated this process until the R2 value stopped to increase. In addition, the definitions of the onset of influenza epidemic were heterogeneous, which might make it impossible to accurately evaluate the performance of alternative approaches. We then used this modified model to detect the peak timing of influenza instead of the onset of epidemic and compared this model with traditional Serfling models using observed weekly case counts of influenza-like illness (ILIs), in terms of sensitivity, specificity and lead time. A better performance was observed. In summary, we provide an adjusted Serfling model which may have improved performance over traditional models in early warning at arrival of peak timing of influenza.
authors.affiliation: Beijing Center for Disease Prevention and Control, Beijing, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Wang
authors.affiliation: Beijing Center for Disease Prevention and Control, Beijing, China.
authors.forename: Shuangsheng
authors.initials: S
authors.lastname: Wu
authors.affiliation: School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, Australia.
authors.forename: C Raina
authors.initials: CR
authors.lastname: MacIntyre
authors.affiliation: School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Hongbin
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Beijing Center for Disease Prevention and Control, Beijing, China.
authors.forename: Weixian
authors.initials: W
authors.lastname: Shi
authors.affiliation: Beijing Center for Disease Prevention and Control, Beijing, China.
authors.forename: Xiaomin
authors.initials: X
authors.lastname: Peng
authors.affiliation: Beijing Center for Disease Prevention and Control, Beijing, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Duan
authors.affiliation: Beijing Center for Disease Prevention and Control, Beijing, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Yang
authors.affiliation: Beijing Center for Disease Prevention and Control, Beijing, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Beijing Center for Disease Prevention and Control, Beijing, China.
authors.forename: Quanyi
authors.initials: Q
authors.lastname: Wang
mesh_terms: D000068476:Beijing / Q000453:epidemiology; D003198:Computer Simulation; D058872:Epidemics; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D015233:Models, Statistical; D012044:Regression Analysis