Using an innovative method to develop the threshold of seasonal influenza epidemic in China.

Xunjie Cheng,Yuelong Shu,Guoqing Hu,Dayan Wang,Jing Yang,Yang Yang,Tao Chen,Eric Hy Lau

doi:10.1371/journal.pone.0202880

Abstract

BackgroundProper early warning thresholds for defining seasonal influenza epidemics are crucial for timely engagement of intervention strategies, but are currently not well established in China. We propose a novel moving logistic regression method (MLRM) to determine epidemic thresholds and validate them with the Chinese influenza surveillance data.MethodsFor each province, historical epidemic waves are formed as weekly percentages of laboratory-confirmed patients among all clinically diagnosed influenza cases. For each epidemic curve that is approximately symmetric, a series of logistic curves are fitted to increasing temporal range of the epidemic, and the threshold is determined based on the best-fitting logistic curve.ResultsUsing surveillance data of seasonal influenza collected during 2010–2014 in 30 provinces of China, we screened 153 epidemic waves and identified 100 as approximately symmetric; and 85 of the 100 waves were satisfactorily fitted. Compared to two published approaches, the MLRM identified lower thresholds of seasonal influenza epidemics, leading to about three weeks earlier detection of onset and about four weeks later detection of closure of the epidemics. The potential misclassification proportion of influenza epidemic waves was 6% for the MLRM, comparable to that for the two published approaches.ConclusionsThe MLRM offers an alternative to existing methods for defining early warning thresholds for the surveillance of seasonal influenza, and can be readily generalized to other countries and other infectious agents. The thresholds we identified can be used for early detection of future influenza epidemics in China.

Highlights

Seasonal influenza is a major contributor to global burden of diseases and injuries
We propose a novel moving logistic regression method (MLRM) to determine epidemic thresholds and validate them with the Chinese influenza surveillance data
The thresholds we identified can be used for early detection of future influenza epidemics in China

Summary

Introduction

Seasonal influenza is a major contributor to global burden of diseases and injuries. Each year, about 5%-10% of adults and 20%-30% of children suffer from seasonal influenza worldwide, leading to three to five million severe cases and 0.25 to 0.50 million deaths [1]. To effectively allocate resources necessary for controlling seasonal epidemics of influenza, public health authorities need to use convenient and scientifically sound approaches to properly define the onset and closure (ending) of influenza epidemic waves. “Epidemic threshold” here refers to the level of the epidemic indicating the onset and closure of an epidemic season, this term is commonly used to represent the basic or effective reproductive numbers. The time when the effective reproductive number exceeding 1 may not coincide with the time when the surveillance data reaches an alarming level (in other words the onset of an epidemic). Proper early warning thresholds for defining seasonal influenza epidemics are crucial for timely engagement of intervention strategies, but are currently not well established in China. We propose a novel moving logistic regression method (MLRM) to determine epidemic thresholds and validate them with the Chinese influenza surveillance data

Methods

Results

Conclusion