Coinfection With SARS-CoV-2 and Influenza A(H1N1) in a Patient Seen at an Influenza-like Illness Surveillance Site in Egypt: Case Report
Bibliographic record
Abstract
BACKGROUND: Sentinel surveillance of influenza-like illness (ILI) in Egypt started in 2000 at 8 sentinel sites geographically distributed all over the country. In response to the COVID-19 pandemic, SARS-CoV-2 was added to the panel of viral testing by polymerase chain reaction for the first 2 patients with ILI seen at one of the sentinel sites. We report the first SARS-CoV-2 and influenza A(H1N1) virus co-infection with mild symptoms detected through routine ILI surveillance in Egypt. OBJECTIVE: This report aims to describe how the case was identified and the demographic and clinical characteristics and outcomes of the patient. METHODS: The case was identified by Central Public Health Laboratory staff, who contacted the ILI sentinel surveillance officer at the Ministry of Health. The case patient was contacted through a telephone call. Detailed information about the patient's clinical picture, course of disease, and outcome was obtained. The contacts of the patient were investigated for acute respiratory symptoms, disease confirmation, and outcomes. RESULTS: Among 510 specimens collected from patients with ILI symptoms from October 2019 to August 2020, 61 (12.0%) were COVID-19-positive and 29 (5.7%) tested positive for influenza, including 15 (51.7%) A(H1N1), 11 (38.0%) A(H3N2), and 3 (10.3%) influenza B specimens. A 21-year-old woman was confirmed to have SARS-CoV-2 and influenza A(H1N1) virus coinfection. She had a high fever of 40.2 °C and mild respiratory symptoms that resolved within 2 days with symptomatic treatment. All five of her family contacts had mild respiratory symptoms 2-3 days after exposure to the confirmed case, and their symptoms resolved without treatment or investigation. CONCLUSIONS: This case highlights the possible occurrence of SARS-CoV-2/influenza A(H1N1) coinfection in younger and healthy people, who may resolve the infection rapidly. We emphasize the usefulness of the surveillance system for detection of viral causative agents of ILI and recommend broadening of the testing panel, especially if it can guide case management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".