Abstract

Can early warning systems be developed to predict influenza epidemics? Using Australian influenza surveillance and local internet search query data, this study investigated whether seasonal influenza epidemics in China, the US and the UK can be predicted using empirical time series analysis. Weekly national number of respiratory cases positive for influenza virus infection that were reported to the FluNet surveillance system in Australia, China, the US and the UK were obtained from World Health Organization FluNet surveillance between week 1, 2010, and week 9, 2018. We collected combined search query data for the US and the UK from Google Trends, and for China from Baidu Index. A multivariate seasonal autoregressive integrated moving average model was developed to track influenza epidemics using Australian influenza and local search data. Parameter estimates for this model were generally consistent with the observed values. The inclusion of search metrics improved the performance of the model with high correlation coefficients (China = 0.96, the US = 0.97, the UK = 0.96, p < 0.01) and low Maximum Absolute Percent Error (MAPE) values (China = 16.76, the US = 96.97, the UK = 125.42). This study demonstrates the feasibility of combining (Australia) influenza and local search query data to predict influenza epidemics a different (northern hemisphere) scales.

Highlights

  • Determining the key drivers of the dynamics of seasonal and non-seasonal influenza outbreaks remains a major challenge[1]

  • This study aims to develop an empirical time series model using Australian influenza surveillance and local internet search query data to predict seasonal influenza epidemics in the northern hemisphere

  • The influenza epidemics were observed to peak between January and March in the US, the UK and China and between August and October in Australia

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Summary

Introduction

Determining the key drivers of the dynamics of seasonal and non-seasonal influenza outbreaks remains a major challenge[1]. Deaths reported in notified laboratory cases confirmed influenza was higher in 2017 (n = 745) than in recent years (5 year average = 176; Range: 28–745)[4] This strain (in conjunction with influenza B virus) led to the largest influenza epidemic outbreak in the last five years in the northern hemisphere during the 2017–2018 season, up to 15th March, 2018, a total of 128 influenza-associated pediatric deaths and 327 confirmed deaths had been reported in the US and the UK respectively[5,6]. This study aims to develop an empirical time series model using Australian influenza surveillance and local internet search query data to predict seasonal influenza epidemics in the northern hemisphere. Our aim is to improve upon predictive tools utilising only infection surveillance data through complementation with internet search data

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