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Background: Co-infection with malaria and intestinal parasites is common in children in Africa and may affect their immune response to a malaria parasite infection. Prior studies suggest that co-infections may lead to increased susceptibility to malaria infection and disease severity; however, other studies have shown the reverse. Knowledge on how co-morbidities specifically affect the immune response to malaria antigens is limited. Therefore, this study sought to determine the prevalence of co-infection of malaria and intestinal parasites and its association with antibody levels to malaria merozoite antigens., Methods: A cross sectional study was carried out in two villages with high transmission of malaria in Cameroon (Ngali II and Mfou) where mass drug administration (MDA) had been administered at ~6-month intervals (generally with albendazole or mebendazole). Children aged 1-15 years were enrolled after obtaining parental consent. A malaria rapid diagnostic test was used on site. Four (4) ml of peripheral blood was collected from each participant to determine Plasmodium falciparum infections by microscopy, haemoglobin levels and serology. Fresh stool samples were collected and examined by wet mount, Kato-Katz method and modified Ritchie concentration techniques. A Multiplex Analyte Platform assay was used to measure antibody levels., Results: A total of 320 children were enrolled. The prevalence of malaria by blood smear was 76.3% (244/320) and prevalence of malaria and intestinal parasites was 16.9% (54/320). Malaria prevalence was highest in young children; whereas, intestinal parasites (IP+) were not present until after 3 years of age. All children positive for malaria had antibodies to MSP142, MSP2, MSP3 and EBA175. No difference in antibody levels in children with malaria-co infections compared to malaria alone were found, except for antibody levels to EBA-175 were higher in children co-infected with intestinal protozoa (p = 0.018), especially those with Entamoeba histolytica infections (p = 0.0026)., Conclusion: Antibody levels to EBA175 were significantly higher in children co-infected with malaria and E. histolytica compared to children infected with malaria alone. It is important to further investigate why and how the presence of these protozoans might modulate the immune response to malaria antigens., Competing Interests: The authors have declared that no competing interests exist.

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., Competing Interests: The authors have declared that no competing interests exist.

Background: Modelling is widely used to inform decisions about management of malaria and acute febrile illnesses. Most models depend on estimates of the probability that untreated patients with malaria or bacterial illnesses will progress to severe disease or death. However, data on these key parameters are lacking and assumptions are frequently made based on expert opinion. Widely diverse opinions can lead to conflicting outcomes in models they inform., Methods and Findings: A Delphi survey was conducted with malaria experts aiming to reach consensus on key parameters for public health and economic models, relating to the outcome of untreated febrile illnesses. Survey questions were stratified by malaria transmission intensity, patient age, and HIV prevalence. The impact of the variability in opinion on decision models is illustrated with a model previously used to assess the cost-effectiveness of malaria rapid diagnostic tests. Some consensus was reached around the probability that patients from higher transmission settings with untreated malaria would progress to severe disease (median 3%, inter-quartile range (IQR) 1-5%), and the probability that a non-malaria illness required antibiotics in areas of low HIV prevalence (median 20%). Children living in low transmission areas were considered to be at higher risk of progressing to severe malaria (median 30%, IQR 10-58%) than those from higher transmission areas (median 13%, IQR 7-30%). Estimates of the probability of dying from severe malaria were high in all settings (medians 60-73%). However, opinions varied widely for most parameters, and did not converge on resurveying., Conclusions: This study highlights the uncertainty around potential consequences of untreated malaria and bacterial illnesses. The lack of consensus on most parameters, the wide range of estimates, and the impact of variability in estimates on model outputs, demonstrate the importance of sensitivity analysis for decision models employing expert opinion. Results of such models should be interpreted cautiously. The diversity of expert opinion should be recognised when policy options are debated.