Outliers in the number of influenza-like illness (ILI) cases who tested negative for influenza were present in the global influenza surveillance network at the start of the COVID-19 pandemic, an average of 13.3 from the first reported COVID-19 peaks. was the week before. A new study published on July 19 covered 28 countries PLOS Medicine by Natalie Cobb and colleagues from the University of Washington, USA.
Surveillance systems are important in detecting changes in disease patterns and can act as an early warning system for emerging disease outbreaks. The WHO Global Influenza Surveillance and Response System (GISRS) is a network of centers and laboratories in 123 WHO member states that collect respiratory samples for influenza testing. Data from these laboratories is made available through FluNet, a web-based tool for monitoring influenza trends.
In the new study, Koob and colleagues evaluated outliers in influenza-negative ILI in 2020, compared with trends for the past five years, with established ILI surveillance in 28 countries and a higher 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 interval of 13.3 weeks. The first outliers occurred in Peru, the Philippines, Poland and Spain during the week of January 13, 2020. In the United States and the United Kingdom, outliers in the dataset could be detected in the week of March 9, 2020, 4 to 6 weeks before the first week of the reported COVID-19 peak. A lag of more than 20 weeks was observed in some countries. Researchers say these outliers may represent undetected spread of COVID-19 in early 2020, although one limitation is that it was not possible to evaluate SAR-CoV-2 positivity during this time.
The researchers say the findings “highlight the importance of strengthening routine disease surveillance networks to increase their ability to identify new diseases and inform public health responses on a global scale.”
Cobb says, “In the first year of the COVID-19 pandemic, we saw an increase in cases of non-influenza respiratory illness prior to the first reported major outbreaks of COVID-19, suggesting that COVID- 19 may spread much faster than initially reported globally.. We are using respiratory disease monitoring networks in existing surveillance networks to identify new outbreaks in real time as a kind of early warning system. propose to use automatic tracking.”
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