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The accurate diagnosis of nonmalarial febrile illnesses remains a large challenge in many malaria-endemic countries (1). The etiologic agents in this context are often not identified because of nonspecific clinical [...]

Objective: To examine the association between animal contact (primarily dogs and cats) and non-malarial fever, as well as with secondary symptoms of headache, nausea, vomiting, and cough, in 687 children in Greater Accra Region, Ghana. Design: Cross-sectional study of acute febrile illness among children aged 1-15 years old between October 2016 and August 2017. Setting: Ledzokuku-Krowor Municipal Assembly (LEKMA) Hospital, Teshie, Greater Accra Region. Participants: The study included children with acute fever, defined as a measured temperature of greater than 37.5°C, occurring less than seven days before the hospital visit, and afebrile children as controls. Main outcome measures: Measured fever, self-reported fever, and secondary symptoms, each adjusting for patient household characteristics. Results: Animal contact was neither associated with measured fever (OR = 1.04, 95% CI 0.73-1.49) nor with selfreported fever (OR = 0.97, 95% CI 0.68-1.39). Animal contact was associated with headache (OR = 3.26, 95% CI 2.23-4.77, P < .01) and nausea (OR = 3.05, 95% CI 1.99-4.68, P < .01), but not with vomiting or cough. Additional models that used alternate inclusion criteria to define non-malarial fever yielded similar results. Several bacterial zoonoses that could plausibly have been transmitted by dogs and cats were diagnosed in the study population. Conclusion: These findings suggest the need for future studies to evaluate animal contact as a risk factor for bacterial zoonoses that may serve as an etiological driver of acute febrile illness. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is one of the fastest evolving pandemics in recent history. As such, the SARS-CoV-2 viral evolution needs to be continuously tracked. This study sequenced 1123 SARS-CoV-2 genomes from patient isolates (121 from arriving travellers and 1002 from communities) to track the molecular evolution and spatio-temporal dynamics of the SARS-CoV-2 variants in Ghana. The data show that initial local transmission was dominated by B.1.1 lineage, but the second wave was overwhelmingly driven by the Alpha variant. Subsequently, an unheralded variant under monitoring, B.1.1.318, dominated transmission from April to June 2021 before being displaced by Delta variants, which were introduced into community transmission in May 2021. Mutational analysis indicated that variants that took hold in Ghana harboured transmission enhancing and immune escape spike substitutions. The observed rapid viral evolution demonstrates the potential for emergence of novel variants with greater mutational fitness as observed in other parts of the world. In this genomic epidemiology study from Ghana, the authors sequence ~1,000 SARS-CoV-2 whole genomes from March 2020 to September 2021. They describe changes in the predominant circulating lineages over time and infer how variants of concern were likely introduced into the country. [ABSTRACT FROM AUTHOR]

Background: Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters.Methods: We obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers.Results: The multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location.Conclusion: The study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings. [ABSTRACT FROM AUTHOR]

We report new molecular evidence of locally acquired dengue virus infections in Ghana. We detected dengue viral RNA among children with suspected malaria by using a multipathogen real-time PCR. Subsequent sequence analysis revealed a close relationship with dengue virus serotype 2, which was implicated in a 2016 outbreak in Burkina Faso. [ABSTRACT FROM AUTHOR]