Global influenza surveillance systems to detect the spread of influenza-negative influenza-like illness during the COVID-19 pandemic: Time series outlier analyses from 2015–2020

Abstract

Background Surveillance systems are important in detecting changes in disease patterns and can act as early warning systems for emerging disease outbreaks. We hypothesized that analysis of data from existing global influenza surveillance networks early in the COVID-19 pandemic could identify outliers in influenza-negative influenza-like illness (ILI). We used data-driven methods to detect outliers in ILI that preceded the first reported peaks of COVID-19. Methods and findings We used data from the World Health Organization’s Global Influenza Surveillance and Response System to evaluate time series outliers in influenza-negative ILI. Using automated autoregressive integrated moving average (ARIMA) time series outlier detection models and baseline influenza-negative ILI training data from 2015–2019, we analyzed 8,792 country-weeks across 28 countries to identify the first week in 2020 with a positive outlier in influenza-negative ILI. We present the difference in weeks between identified outliers and the first reported COVID-19 peaks in these 28 countries with high levels of data completeness for influenza surveillance data and the highest number of reported COVID-19 cases globally in 2020. To account for missing data, we also performed a sensitivity analysis using linear interpolation for missing observations of influenza-negative ILI. In 16 of the 28 countries (57%) included in this study, we identified positive outliers in cases of influenza-negative ILI that predated the first reported COVID-19 peak in each country; the average lag between the first positive ILI outlier and the reported COVID-19 peak was 13.3 weeks (standard deviation 6.8). In our primary analysis, the earliest outliers occurred during the week of January 13, 2020, in Peru, the Philippines, Poland, and Spain. Using linear interpolation for missing data, the earliest outliers were detected during the weeks beginning December 30, 2019, and January 20, 2020, in Poland and Peru, respectively. This contrasts with the reported COVID-19 peaks, which occurred on April 6 in Poland and June 1 in Peru. In many low- and middle-income countries in particular, the lag between detected outliers and COVID-19 peaks exceeded 12 weeks. These outliers may represent undetected spread of SARS-CoV-2, although a limitation of this study is that we could not evaluate SARS-CoV-2 positivity. Conclusions Using an automated system of influenza-negative ILI outlier monitoring may have informed countries of the spread of COVID-19 more than 13 weeks before the first reported COVID-19 peaks. This proof-of-concept paper suggests that a system of influenza-negative ILI outlier monitoring could have informed national and global responses to SARS-CoV-2 during the rapid spread of this novel pathogen in early 2020. Natalie L Cobb and colleagues use routine influenza surveillance data to detect outliers in influenza-like-illness during the COVID-19 pandemic. Author summary Why was this study done? Early detection of respiratory viral outbreaks, such as SARS-CoV-2, is key for public health response and mitigation measures. In this study, we used routine influenza surveillance data to detect outliers in influenza-like illness (ILI) during the COVID-19 pandemic that could suggest spread of SARS-CoV-2. We hypothesized that using data-driven methods would identify increased case counts of influenza-negative ILI prior to reported peaks of COVID-19. What did the researchers do and find? We used routine influenza surveillance data from the World Health Organization’s FluNet and applied automated outlier detection methods to identify outliers in influenza-negative ILI in 2020 across 28 countries. In 16 countries, we detected outliers that preceded the first reported COVID-19 peaks, with an average lag time of 13.3 weeks. In 7 countries, the week of the first outlier changed when accounting for missing data in the models. What do these findings mean? This study serves as a proof of concept and suggests a potential role for the use of automated data monitoring and outlier detection systems to identify outbreaks in respiratory viral illness. These findings also highlight the importance of strengthening routine disease surveillance networks to enhance our ability to identify novel diseases and inform public health responses on a global scale.