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.

Although acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied and compared a range of robust statistical and machine learning models including random forest (RF) regression, support vector machines (SVM) regression, multivariable linear regression and ARIMA models to forecast 2012 to 2018 trends of reported ILI cases in Cameroon, using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The R2 and RMSE (Root Mean Squared Error) were statistically similar across most of the methods, however, RF and SVM had the highest average R2 (0.78 and 0.88, respectively) for predicting ILI per 100,000 persons at the country level. This study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and the usefulness of search data for monitoring ILI in sub-Saharan African countries.

Purpose:In 2012, Cameroon experienced a large measles outbreak of over 14,000 cases. To determine the spatio-temporal dynamics of measles transmission in Cameroon, we analysed weekly case data collected by the Ministry of Health. Methods:We compared several multivariate time-series models of population movement to characterize the spatial spread of measles in Cameroon. Using the best model, we evaluated the contribution of population mobility to disease transmission at increasing geographic resolutions: region, department, and health district.Results: Our spatio-temporal analysis showed that the Power Law model, which accounts for long-distance population movement, best represents the spatial spread of measles in Cameroon. Population movement between health districts within departments contributed to 7.6% (range: 0.4%−13.4%) of cases at the district level, while movement between departments within regions contributed to 16.0% (range: 1.3%−23.2%) of cases. Long-distance movement between regions

From May 2016 to March 2017, 22 poultry outbreaks of avian influenza A(H5N1) were reported in Cameroon, mainly in poultry farms and live bird markets. No human cases were reported. In this study, we sought to describe the 2016 A(H5N1) outbreak strain and to investigate the risk of infection in exposed individuals. We find that highly pathogenic influenza subtype A(H5N1), clade 2.3.2.1c from Cameroon is closely related phylogenetically and antigenically to strains isolated in central and western Africa at the time. No molecular markers of increased human transmissibility were noted; however, seroconversion was detected in two poultry workers (1.5% of total screened). Therefore, the continued outbreaks of avian influenza in poultry and the risk of zoonotic human infection highlight the crucial need for continued and vigilant influenza surveillance and research in Africa, especially in areas of high poultry trade, such as Cameroon.

Although acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied statistical and machine learning models to forecast 2012 to 2018 trends in ILI cases reported by the Cameroon Ministry of Health (MOH), using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The variance explained by the models based on Google search data were 87.7%, 79.1% and 52.0% for the whole country, the Littoral and Centre regions respectively. Our study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and demonstrates the potential usefulness of search data for monitoring ILI in sub-Saharan African countries.