Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling.

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

Forecasting influenza-like illness trends in Cameroon using Google Search Data.

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.

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.

Spatio-temporal dynamics of measles outbreaks in Cameroon.

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 analyzed 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, whereas movement between departments within regions contributed to 16.0% (range: 1.3%-23.2%) of cases. Long-distance movement between regions contributed to 16.7% (range: 0.1%-59.0%) of cases at the region level, 20.1% (range: 7.1%-30.0%) at the department level, and 29.7% (range: 15.3%-47.6%) at the health district level. CONCLUSIONS: Population long-distance mobility is an important driver of measles dynamics in Cameroon. These findings demonstrate the need to improve our understanding of the roles of population mobility and local heterogeneity of vaccination coverage in the spread and control of measles in Cameroon.

Evidence of exposure and human seroconversion during an outbreak of avian influenza A(H5N1) among poultry in Cameroon.

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.