Estimation of the burden of flu-association influenza-like illness visits on total clinic visits through the sentinel influenza monitoring system in Senegal during the 2013–2015 influenza seasons

Abstract

Knowing the burden of influenza is helpful for policy decisions. Here we estimated the contribution of influenza-like illness (ILI) visits associated with laboratory-confirmed influenza among all clinic visits in a Senegal sentinel network. ILI data from ten sentinel sites were collected from January 2013 to December 2015. ILI was defined as an axillary measured fever of more than 37.5 °C with a cough or a sore throat. Collected nasopharyngeal swabs were tested for influenza viruses by rRT-PCR. Influenza-associated ILI was defined as ILI with laboratory-confirmed influenza. For the influenza disease burden estimation, we used all-case outpatient visits during the study period who sought care at selected sites. Of 4030 ILI outpatients tested, 1022 were influenza positive. The estimated proportional contribution of influenza-associated ILI was, per 100 outpatients, 1.2 (95% CI 1.1-1.3), 0.32 (95% CI 0.28-0.35), 1.11 (95% CI 1.05-1.16) during 2013, 2014, 2015, respectively. The age-specific outpatient visits proportions of influenza-associated ILI were higher among children under 5 years (0.68%, 95% CI: 0.62-0.70). The predominant virus during years 2013 and 2015 was influenza B while A/H3N2 subtype was predominant during 2014. Influenza viruses cause a substantial burden of outpatient visits particularly among children under 5 of age in Senegal and highlight the need of vaccination in risk groups.

Evaluation of a mobile health approach to improve the Early Warning System of influenza surveillance in Cameroon

“…The estimated incidence of influenza‐associated ILI outpatient visits in 2019 (0.37) was lower than that observed in Senegal within the cumulative period of 2013‐2015 (0.9/100 population), in the USA (8.7/1000 population) and in Thailand (14.2/1000 population) 4,12,13 . Our results might underestimate the burden of influenza‐associated ILI in Cameroon since the majority of patients with ILI do not refer to any health facility for treatment.…”

“…Reporting of surveillance data has mostly made use of paper‐based systems, mobile phone–based systems and Web‐based systems. Among these, mobile and Internet technologies have been successfully used for EWS in several countries and settings 2‐5 . In Cameroon, there has been progress in the collection tools for influenza epidemiological data from forms to SMS (short message service) to smartphone using the Internet in order to improve on the timeliness of data collected.…”

Effect of a Russian-backbone live-attenuated influenza vaccine with an updated pandemic H1N1 strain on shedding and immunogenicity among children in The Gambia: an open-label, observational, phase 4 study

“…We were also unable to confirm viral shedding with an LAIV-specific RT-PCR in all participants because of lower sensitivity compared with the haemagglutinin-specific RT-PCR, which is important to fully exclude interference from wild-type circulating strains. However, by doing the study outside of the peak influenza season, 7 undertaking clinical review at enrolment, and doing baseline RT-PCR screening for influenza virus, it is unlikely that our results were affected by wild-type influenza infections. Our shedding data at day 2 are also similar to those reported from Senegal using Nasovac-S (Cal09 19%, H3N2 48%, and influenza B 66%).…”

“…When LAIV was updated in 2017-18, haemagglutinin and neuraminidase from pH1N1 Cal09 were replaced with those from NY15, whereas identical H3N2 ( The study was done outside the peak influenza transmission season (June to October) based on sur veillance data from Senegal and unpublished data from studies in The Gambia. 7 Children received one dose of intranasal trivalent Russian-backbone LAIV (Nasovac-S; northern hemisphere formulation) in either 2017 (the Cal09 strain from 2016-17) or 2018 (the NY15 strain from 2017-18 formulation). Children received the vaccine formulation that corresponded with their year of enrolment.…”

Estimation of Seasonal Influenza Attack Rates and Antibody Dynamics in Children Using Cross-Sectional Serological Data

“…a) the number of individuals in each cohort with their respective birth and sampling month. Grey filled circles indicate the frequency of A/H3N2 infections that were observed in Senegal from 2013 to 2015 (obtained from[12]). b) the estimated attack rate for the Gambia cohorts with 95% quantile interval, c) the estimated number of infections per age in months, d) the estimated age at first infection in months.…”