ArticlePloS one2020
Clinical signs predictive of influenza virus infection in Cameroon.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- Pooled it
- Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review.BMJ open · 2023Pooled it
- Epidemiological trends of influenza A and B in one hospital in Chengdu and national surveillance data (2019-2024).Frontiers in cellular and infection microbiology · 2025Article
- The Detection of Influenza Virus Before and During the COVID-19 Pandemic in Cameroon.Influenza and other respiratory viruses · 2024Article
- The COVID-19 wave was already here: High seroprevalence of SARS-CoV-2 antibodies among staff and students in a Cameroon University.Journal of public health in Africa · 2023Article
- Discrimination and Characterization of the Volatile Organic Compounds inMolecules (Basel, Switzerland) · 2022Article
- Does knowing the influenza epidemic threshold has been reached influence the performance of influenza case definitions?PloS one · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Influenza virus accounts for majority of respiratory virus infections in Cameroon. According to the World Health Organization (WHO), influenza-like illnesses (ILI) are identified by a measured temperature of ≥38°C and cough, with onset within the past 10 days. Other symptoms could as well be observed however, none of these are specific to influenza alone. This study aimed to determine symptom based predictors of influenza virus infection in Cameroon. Individuals with ILI were recruited from 2009-2018 in sentinel sites of the influenza surveillance system in Cameroon according to the WHO case definition. Individual data collection forms accompanied each respiratory sample and contained clinical data. Samples were analyzed for influenza using the gold standard assay. Two statistical methods were compared to determine the most reliable clinical predictors of influenza virus activity in Cameroon: binomial logistic predictive model and random forest model. Analyses were performed in R version 3.5.2. A total of 11816 participants were recruited, of which, 24.0% were positive for influenza virus. Binomial logistic predictive model revealed that the presence of cough, rhinorrhoea, headache and myalgia are significant predictors of influenza positivity. The prediction model had a sensitivity of 75.6%, specificity of 46.6% and AUC of 66.7%. The random forest model categorized the reported symptoms according to their degree of importance in predicting influenza virus infection. Myalgia had a 2-fold higher value in predicting influenza virus infection compared to any other symptom followed by arthralgia, head ache, rhinorrhoea and sore throat. The model had a OOB error rate of 25.86%. Analysis showed that the random forest model had a better performance over the binomial regression model in predicting influenza infection. Rhinorrhoea, headache and myalgia were symptoms reported by both models as significant predictors of influenza infection in Cameroon. These symptoms could be used by clinicians in their decision to treat patients.
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