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Rapid Molecular Detection and Differentiation of Influenza Viruses A and B
Published on: January 30, 2017
Differentiating coronavirus disease 2019 (COVID-19) from influenza and dengue
Tun-Linn Thein1, Li Wei Ang1, Barnaby Edward Young1,2,3
1National Centre for Infectious Diseases, 16 Jalan Tan Tock Seng, Singapore, 308442, Singapore.
Insights
Early predictors like shortness of breath and lymphocyte count can help distinguish COVID-19 from influenza and dengue, aiding timely diagnosis and reducing community transmission.
Area of Science:
- Infectious Diseases
- Epidemiology
- Clinical Diagnostics
Background:
- COVID-19 shares non-specific symptoms with influenza and dengue, complicating early diagnosis and potentially increasing transmission.
- Accurate differentiation is crucial for appropriate patient management and public health interventions.
Purpose of the Study:
- To identify early clinical and laboratory predictors for distinguishing COVID-19 from influenza and dengue.
- To develop predictive models for primary care physicians, especially in resource-limited settings.
Main Methods:
- Logistic regression models were employed to analyze data from 126 COVID-19, 171 influenza, and 180 dengue patients presenting within 5 days of symptom onset.
- All diagnoses were confirmed via reverse transcriptase polymerase chain reaction (RT-PCR).
- Model performance was assessed using receiver operating characteristic (ROC) curves.
Main Results:
- Shortness of breath and diarrhea were key predictors for COVID-19 versus influenza.
- Higher lymphocyte counts predicted COVID-19 in comparisons with both influenza and dengue.
- Cough and elevated platelet count indicated COVID-19, while headache, joint pain, rash, and nausea suggested dengue over COVID-19.
- All models demonstrated strong performance with cross-validated area under the ROC curve > 0.85.
Conclusions:
- Specific clinical features and laboratory markers can effectively differentiate COVID-19 from influenza and dengue.
- These findings support improved diagnostic decision-making in primary care, particularly in resource-limited environments.
- Early identification facilitates timely interventions and reduces disease spread.
Abstract:
The novel coronavirus disease 2019 (COVID-19) presents with non-specific clinical features. This may result in misdiagnosis or delayed diagnosis, and lead to further transmission in the community. We aimed to derive early predictors to differentiate COVID-19 from influenza and dengue. The study comprised 126 patients with COVID-19, 171 with influenza and 180 with dengue, who presented within 5 days of symptom onset. All cases were confirmed by reverse transcriptase polymerase chain reaction tests. We used logistic regression models to identify demographics, clinical characteristics and laboratory markers in classifying COVID-19 versus influenza, and COVID-19 versus dengue. The performance of each model was evaluated using receiver operating characteristic (ROC) curves. Shortness of breath was the strongest predictor in the models for differentiating between COVID-19 and influenza, followed by diarrhoea. Higher lymphocyte count was predictive of COVID-19 versus influenza and versus dengue. In the model for differentiating between COVID-19 and dengue, patients with cough and higher platelet count were at increased odds of COVID-19, while headache, joint pain, skin rash and vomiting/nausea were indicative of dengue. The cross-validated area under the ROC curve for all four models was above 0.85. Clinical features and simple laboratory markers for differentiating COVID-19 from influenza and dengue are identified in this study which can be used by primary care physicians in resource limited settings to determine if further investigations or referrals would be required.
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