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Updated: Jun 23, 2026
High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
Can Haematological Parameters Discriminate COVID-19 from Influenza?
Sahar Gnaba1, Dmitry Sukhachev2, Tiffany Pascreau1,3
1Biology Department, Foch Hospital, 92150 Suresnes, France.
Insights
Differentiating COVID-19 from influenza is crucial due to different treatments. Cellular population data from complete blood counts, particularly CD16pos monocyte and B-lymphocyte levels, effectively distinguish between these viral infections.
Area of Science:
- Hematology
- Infectious Diseases
- Immunology
Background:
- COVID-19 and influenza share similar symptoms, necessitating accurate differentiation for appropriate treatment and prognosis.
- Complete blood count (CBC) parameters are often comparable between these viral infections.
- Novel biomarkers are needed to reliably distinguish between COVID-19 and influenza.
Purpose of the Study:
- To evaluate the utility of complete blood count (CBC) cellular population data (CPD) and automated flow cytometry in differentiating COVID-19 from influenza.
- To identify specific CBC parameters and leukocyte subpopulations that can discriminate between these two diseases.
Main Methods:
- Analysis of CBC, including leukocyte cellular population data (CPD), and automated flow cytometry in 350 COVID-19 and 102 influenza patients.
- Comparison of various hematological parameters and cell counts between the two patient groups.
- Development of a logistic regression model incorporating 17 parameters, including CPD, to discriminate between COVID-19 and influenza.
Main Results:
- Platelet counts were lower in influenza patients compared to COVID-19 patients.
- CD16pos monocyte counts and the ratio of CD16pos monocytes to total monocytes were higher in COVID-19 patients.
- A logistic regression model achieved 96.2% sensitivity and 86.6% efficiency in discriminating COVID-19 from influenza, with an AUC of 0.862.
Conclusions:
- While classical CBC parameters are similar, specific cellular population data (CPD), CD16pos monocyte levels, and B-lymphocyte counts can effectively differentiate COVID-19 from influenza.
- Automated flow cytometry analysis of leukocyte subpopulations offers a valuable tool for distinguishing these viral respiratory infections.
- The developed logistic regression model demonstrates high accuracy in clinical settings for differentiating COVID-19 and influenza.
Abstract:
Symptoms of COVID-19 are similar to the influenza virus, but because treatments and prognoses are different, it is important to accurately and rapidly differentiate these diseases. The aim of this study was to evaluate whether the analysis of complete blood count (CBC), including cellular population (CPD) data of leukocytes and automated flow cytometry analysis, could discriminate these pathologies. In total, 350 patients with COVID-19 and 102 patients with influenza were included between September 2021 and April 2022 in the tertiary hospital of Suresnes (France). Platelets were lower in patients with influenza than in patients with COVID-19, whereas the CD16pos monocyte count and the ratio of the CD16pos monocytes/total monocyte count were higher. Significant differences were observed for 9/56 CPD of COVID-19 and flu patients. A logistic regression model with 17 parameters, including among them 11 CPD, the haemoglobin level, the haematocrit, the red cell distribution width, and B-lymphocyte and CD16pos monocyte levels, discriminates COVID-19 patients from flu patients. The sensitivity and efficiency of the model were 96.2 and 86.6%, respectively, with an area under the curve of 0.862. Classical parameters of CBC are very similar among the three infections, but CPD, CD16pos monocytes, and B-lymphocyte levels can discriminate patients with COVID-19.
