A prospective, multi-site, cohort study to estimate incidence of infection and disease due to Lassa fever virus in West African countries (the Enable Lassa research programme)-Study protocol.

BACKGROUND: Lassa fever (LF), a haemorrhagic illness caused by the Lassa fever virus (LASV), is endemic in West Africa and causes 5000 fatalities every year. The true prevalence and incidence rates of LF are unknown as infections are often asymptomatic, clinical presentations are varied, and surveillance systems are not robust. The aim of the Enable Lassa research programme is to estimate the incidences of LASV infection and LF disease in five West African countries. The core protocol described here harmonises key study components, such as eligibility criteria, case definitions, outcome measures, and laboratory tests, which will maximise the comparability of data for between-country analyses. METHOD: We are conducting a prospective cohort study in Benin, Guinea, Liberia, Nigeria (three sites), and Sierra Leone from 2020 to 2023, with 24 months of follow-up. Each site will assess the incidence of LASV infection, LF disease, or both. When both incidences are assessed the LASV cohort (nmin = 1000 per site) will be drawn from the LF cohort (nmin = 5000 per site). During recruitment participants will complete questionnaires on household composition, socioeconomic status, demographic characteristics, and LF history, and blood samples will be collected to determine IgG LASV serostatus. LF disease cohort participants will be contacted biweekly to identify acute febrile cases, from whom blood samples will be drawn to test for active LASV infection using RT-PCR. Symptom and treatment data will be abstracted from medical records of LF cases. LF survivors will be followed up after four months to assess sequelae, specifically sensorineural hearing loss. LASV infection cohort participants will be asked for a blood sample every six months to assess LASV serostatus (IgG and IgM). DISCUSSION: Data on LASV infection and LF disease incidence in West Africa from this research programme will determine the feasibility of future Phase IIb or III clinical trials for LF vaccine candidates.

Serologic response to SARS-CoV-2 in an African population.

Official case counts suggest Africa has not seen the expected burden of COVID-19 as predicted by international health agencies, and the proportion of asymptomatic patients, disease severity, and mortality burden differ significantly in Africa from what has been observed elsewhere. Testing for SARS-CoV-2 was extremely limited early in the pandemic and likely led to under-reporting of cases leaving important gaps in our understanding of transmission and disease characteristics in the African context. SARS-CoV-2 antibody prevalence and serologic response data could help quantify the burden of COVID-19 disease in Africa to address this knowledge gap and guide future outbreak response, adapted to the local context. However, such data are widely lacking in Africa. We conducted a cross-sectional seroprevalence survey among 1,192 individuals seeking COVID-19 screening and testing in central Cameroon using the Innovita antibody-based rapid diagnostic. Overall immunoglobulin prevalence was 32%, IgM prevalence was 20%, and IgG prevalence was 24%. IgM positivity gradually increased, peaking around symptom day 20. IgG positivity was similar, gradually increasing over the first 10 days of symptoms, then increasing rapidly to 30 days and beyond. These findings highlight the importance of diagnostic testing and asymptomatic SARS-CoV-2 transmission in Cameroon, which likely resulted in artificially low case counts. Rapid antibody tests are a useful diagnostic modality for seroprevalence surveys and infection diagnosis starting 5-7 days after symptom onset. These results represent the first step towards better understanding the SARS-CoV-2 immunological response in African populations.

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.