The WHO Global Influenza Surveillance and Response System had outliers to typical influenza-like illness (ILI) in 16 out of 28 countries an average of 13.3 weeks before the first reported COVID-19 peak, reports a study in PLOS Medicine.
The study, by Natalie Cobb and colleagues from the University of Washington, reports that surveillance systems are important in detecting changes in disease patterns and can act as early warning systems for emerging disease outbreaks.
The WHO Global Influenza Surveillance and Response System is a network of centres and laboratories across 123 WHO member states that collect respiratory specimens for influenza testing. Data from these labs are made available through FluNet, a web-based tool for monitoring influenza trends.
In this study, the researchers evaluated outliers in influenza-negative ILI in 2020 compared with trends over the previous five years among 28 countries with established ILI surveillance and a high incidence of COVID-19. They found that in 16 countries, outliers in this dataset preceded the first reported COVID-19 peaks with an average lag time of 13.3 weeks. The earliest outliers occurred during the week of 13 January 2020, in Peru, the Philippines, Poland and Spain.
In the United States and the United Kingdom, outliers in the dataset were detectable in the week of 9 March 2020, four to six weeks before the first week of the reported COVID-19 peak. Lag times of more than 20 weeks were seen in some countries.
The researchers said that these outliers might represent undetected spread of COVID-19 in early 2020, although a limitation is that it was not possible to evaluate SAR-CoV-2 positivity during this time.
The findings “highlight the importance of strengthening routine disease surveillance networks to enhance the ability to identify novel diseases and inform public health responses on a global scale”, they said.
Cobb added: “In the first year of the COVID-19 pandemic, we found increases in cases of non-influenza respiratory illness before the first reported major outbreaks of COVID-19, suggesting COVID-19 may have spread much faster than initially reported globally. We propose using automated tracking of respiratory illness in existing surveillance networks to identify new outbreaks in real time as a type of early warning system.”
Study details
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
Natalie Cobb, Sigrid Collierm Engi Attia, Orvalho Augusto, T. Eoin West, Bradley Wagenaar.
Published in PLOS Medicine on 19 July 2022
Abstract
Background
Surveillance systems are important in detecting changes in disease patterns and can act as early warning systems for emerging disease outbreaks. We hypothesised 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 analysed 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 influenzanegative 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.
See more from MedicalBrief archives:
Calls for aggressive China-style containment amid sobering WHO warning
World in lockdown as global infections exceed 200,000
Former UCT doctor warns SA: Be ready for an enigmatic disease and a flood of patients