Flu surveillance system may have shown early signs of COVID-19 pandemic


Outliers in the number of influenza-like illness (ILI) cases that tested negative for influenza were present in global influenza surveillance networks at the start of the COVID-19 pandemic, an average of 13.3 weeks before the first peaks of COVID-19 reported in 16 of the 28 countries included in a new study published on July 19 in OLP Medicine by Natalie Cobb of the University of Washington, USA, and colleagues.

Surveillance systems are important for detecting changes in disease patterns and can serve as early warning systems for outbreaks of emerging diseases. The WHO Global Influenza Surveillance and Response System (GISRS) is a network of centers and laboratories in 123 WHO member states that collect respiratory specimens for influenza testing. Data from these labs is made available through FluNet, a web-based influenza trend monitoring tool.

In the new study, Cobb and colleagues assessed outliers of influenza-negative ILI in 2020 compared to trends over the previous five years in 28 countries with established ILI surveillance and high incidence of COVID-19. The team found that in 16 countries, outliers in this dataset preceded the first reported COVID-19 peaks with an average lag of 13.3 weeks. The first outliers occurred during the week of January 13, 2020 in Peru, the Philippines, Poland and Spain. In the US and UK, outliers in the dataset were detectable the week of March 9, 2020, 4-6 weeks before the first week of the reported COVID-19 peak. Delays of more than 20 weeks have been observed in some countries. The researchers say these outliers may represent undetected spread of COVID-19 in early 2020, although one limitation is that it was not possible to assess SAR-CoV-2 positivity during that time. period.

The findings “underscore the importance of strengthening routine disease surveillance networks to improve the ability to identify new diseases and inform public health responses globally,” the researchers say.

Cobb adds: “During the first year of the COVID-19 pandemic, we saw an increase in cases of non-influenza respiratory illness before the first major reported outbreaks of COVID-19, suggesting that COVID-19 may have spread much faster than initially reported globally. We propose using automated respiratory disease tracking in existing surveillance networks to identify new outbreaks in real time as a type of early warning system.

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