Genetic diversity of influenza A(H3N2) viruses in Northern Cameroon during the 2014-2016 influenza seasons.

In Cameroon, genome characterization of influenza virus has been performed only in the Southern regions meanwhile genetic diversity of this virus varies with respect to locality. The Northern region characterized by a Sudan tropical climate might have distinct genetic characterization. This study aimed to better understand the genetic diversity of influenza A(H3N2) viruses circulating in Northern Cameroon. Sequences of three gene segments (hemagglutinin (HA), neuraminidase (NA) and matrix (M) genes) were obtained from 16 A(H3N2) virus strains collected during the 2014 to 2016 influenza seasons in Garoua. The HA gene segments were analysed with respect to reference strains while the NA and M gene was analysed for reported genetic markers of resistance to antivirals. Analysis of the HA sequences revealed that majority of the virus strains grouped together with the 2016-2017 vaccine strain (3C.2a-A/Hong Kong/4801/2014) while 3/5 (60%) of the 2015 viral strains grouped together with the 2015-2016 vaccine strain 3C.3a-A/Switzerland/9715293/2013. Within clade 3C.2a, Northern Cameroon sequences mostly grouped in sub-clade A3 (10/16). Analysis of the coding regions of the NA and M genes showed that none had genetic markers of resistance to neuraminidase inhibitors but all strains possessed the S31N substitution of resistance to amantadine. Due to some discrepancies observed in this region with respect to the Southern regions of Cameroon, there is necessity of including all regions within a country in the sentinel surveillance of influenza. These data will enable to track changes in influenza viruses in Cameroon.

Genetic characterization of influenza B virus in Cameroon and high frequency of reassortant strains.

Influenza B is broadly divided into B/Victoria and B/Yamagata lineages based on its genetic and antigenic properties. We describe in this study the first report on genome characterization of type B influenza virus in the Cameroon National Influenza Center (NIC) between 2014 and 2017. Respiratory samples were collected as part of the influenza surveillance activity in the NIC. RNA products were tested for the presence of influenza using the CDC Influenza A/B typing panel. Thirty-five samples positive for influenza B were selected for sequencing three gene segments (HA, NA, and M) and phylogenetic trees were generated by MEGA version 6.0. Nucleotide phylogenetic analysis of the HA gene revealed the presence of three major clades among Cameroonian strains. All Victoria lineages grouped into B/Victoria clade 1A, while, Yamagata lineages grouped into Yamagata clade 2 (2014 strains) and Yamagata clade 3 (2015-2017). We observed a high frequency of reassortant viruses with Yamagata-like HA gene and Victoria-like NA gene (27.4%; 23/84). The results from this study confirm variations in the genome composition of type B influenza virus and emphasize on the relevance of molecular surveillance for spotting peculiar genetic variants of public health and clinical significance.

Clinical signs predictive of influenza virus infection in Cameroon.

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.

Influence of meteorological parameters in the seasonality of influenza viruses circulating in Northern Cameroon.

BACKGROUND: Several studies have demonstrated the role of meteorological parameters in the seasonality of influenza viruses in tropical and subtropical regions, most importantly temperature, humidity, and rainfall. OBJECTIVES: This study aimed to describe the influence of meteorological parameters in the seasonality of influenza viruses in Northern Cameroon, a region characterized by high temperatures. METHODS: This was a retrospective study performed in Garoua Cameroon from January 2014 to December 2016. Monthly proportions of confirmed influenza cases from six sentinel sites were considered as dependent variables, whereas monthly values of mean temperature, average relative humidity, and accumulated rainfall were considered as independent variables. A vector error correction model was used to determine the relationship between influenza activity and the meteorological variables. RESULTS AND CONCLUSION: Analysis showed that there was a statistically significant association between overall influenza activity and influenza A activity with respect to average relative humidity. A unit increase in humidity within a given month leads to more than 85% rise in the overall influenza and influenza A activity 2 months later. Meanwhile, none of the three meteorological variables could explain influenza B activity. This observation is essential in filling the gap of knowledge and could help in the prevention and control strategies to strengthen influenza surveillance program in Cameroon.

Molecular characterization of influenza A(H1N1)pdm09 in Cameroon during the 2014-2016 influenza seasons.

In 2009, Influenza A(H1N1)pdm09 caused the first influenza pandemic of the 21st century with high mortality rates of about 284 500 deaths. This virus, however, continues to circulate as a seasonal influenza virus and to cause illness and deaths worldwide. In this study, we describe the genetic diversity of A(H1N1)pdm09 viruses collected between 2014 and 2016 in Cameroon. Three gene segments (HA, NA and M) of Cameroon strains were studied. The phylogenetic tree of the coding nucleotide sequences was generated by MEGA version 6.0 using a Maximum Likelihood method. The NA and M protein coding sequences were analyzed for the reported genetic markers of resistance against neuraminidase inhibitors and adamantanes, while predicted vaccine efficacy was estimated using the Pepitope method. Overall 39 strains were obtained. Phylogenetic analysis of the HA gene of influenza A(H1N1)pdm09 showed that Cameroon strains belonged to two major clades. The 2014 Cameroon sequences belonged to clade 6C while all sequences collected between 2015 and 2016 belonged to clade 6B. Majority of the samples had some mutations in the NA gene notably: I117M, N248D, and N369K while the amantadine-resistant M mutant, S31N, was found to be absent only in the two sequences collected in 2014. Overall, A/California/07/2009 vaccine strain showed a predicted vaccine efficacy of 24.55% to 35.77% against Cameroon A(H1N1)pdm09 strains circulating between 2014 and 2016. Our findings confirms the fast evolution of A(H1N1)pdm09 since its first introduction and highlights on the importance of influenza vaccine in reducing the burden caused by influenza in the community.

Associations between meteorological parameters and influenza activity in a subtropical country: Case of five sentinel sites in Yaoundé-Cameroon.

Influenza is associated with highly contagious respiratory infections. Previous research has found that influenza transmission is often associated with climate variables especially in temperate regions. This study was performed in order to fill the gap of knowledge regarding the relationship between incidence of influenza and three meteorological parameters (temperature, rainfall and humidity) in a tropical setting. This was a retrospective study performed in Yaoundé-Cameroon from January 2009 to November 2015. Weekly proportions of confirmed influenza cases from five sentinel sites were considered as dependent variables, whereas weekly values of mean temperature, average relative humidity and accumulated rainfall were considered as independent variables. A univariate linear regression model was used in determining associations between influenza activity and weather covariates. A time-series method was used to predict on future values of influenza activity. The data was divided into 2 parts; the first 71 months were used to calibrate the model, and the last 12 months to test for prediction. Overall, there were 1173 confirmed infections with influenza virus. Linear regression analysis showed that there was no statistically significant association observed between influenza activity and weather variables. Very weak relationships (-0.1 < r < 0.1) were observed. Three prediction models were obtained for the different viral types (overall positive, Influenza A and Influenza B). Model 1 (overall influenza) and model 2 (influenza A) fitted well during the estimation period; however, they did not succeed to make good forecasts for predictions. Accumulated rainfall was the only external covariate that enabled good fit of both models. Based on the stationary R2, 29.5% and 41.1% of the variation in the series can be explained by model 1 and 2, respectively. This study laid more emphasis on the fact that influenza in Cameroon is characterized by year-round activity. The meteorological variables selected in this study did not enable good forecast of future influenza activity and certainly acted as proxies to other factors not considered, such as, UV radiation, absolute humidity, air quality and wind.