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Buruli ulcer (BU) is a disabling and stigmatising neglected tropical disease (NTD). Its distribution and burden are unknown because of underdiagnosis and underreporting. It is caused by Mycobacterium ulcerans, an environmental pathogen whose environmental niche and transmission routes are not fully understood. The main control strategy is active surveillance to promote early treatment and thus limit morbidity, but these activities are mostly restricted to well-known endemic areas. A better understanding of environmental suitability for the bacterium and disease could inform targeted surveillance, and advance understanding of the ecology and burden of BU. We used previously compiled point-level datasets of BU and M. ulcerans occurrence, evidence for BU occurrence within national and sub-national areas, and a suite of relevant environmental covariates in a distribution modelling framework. We fitted relationships between BU and M. ulcerans occurrence and environmental predictors by applying regression and machine learning based algorithms, combined in an ensemble model to characterise the optimal ecological niche for the disease and bacterium across Africa at a resolution of 5km x 5km. Proximity to waterbodies was the strongest predictor of suitability for BU, followed potential evapotranspiration. The strongest predictors of suitability for M. ulcerans were deforestation and potential evapotranspiration. We identified patchy foci of suitability throughout West and Central Africa, including areas with no previous evidence of the disease. Predicted suitability for M. ulcerans was wider but overlapping with that of BU. The estimated population living in areas predicted suitable for the bacterium and disease was 46.1 million. These maps could be used to inform burden estimations and case searches which would generate a more complete understanding of the spatial distribution of BU in Africa, and may guide control programmes to identify cases beyond the well-known endemic areas. Author summary: Like many neglected tropical diseases primarily affecting the rural poor, Buruli ulcer (BU) is under-detected and under-reported within routine health information systems. As such, the burden and distribution are not fully known, impeding appropriate targeting of health resources, control, and care for people affected. Having previously evaluated and mapped the existing evidence for BU and its causative agent M. ulcerans, we concluded that the disease was likely to occur beyond the range of known endemic areas. However, we were left with the question of where exactly these undetected cases might be occurring. Answering this question required a more fine-scale approach: BU is highly focal, presumably due to local variation in the environmental factors which determine suitability for M. ulcerans survival and transmission to humans. We used the compiled evidence and geographical datasets to build statistical models representing the relationship between environmental factors and previously reported cases. This allowed us to define the ecological niche of BU, and subsequently to identify areas across Africa where this niche was met, providing suitable conditions for the disease. We constructed separate models of suitability for M. ulcerans, using locations where its DNA had been detected in environmental sources. Unsurprisingly, suitability for M. ulcerans was predicted to be wider than, but geographically overlapping with that for BU. This implies that beyond the conditions necessary for survival of the bacterium, additional factors are required for transmission to humans. The high-resolution suitability maps we present are intended to guide case search activities which may identify endemic areas beyond the known endemic range. Data on the true prevalence of BU from targeted case searches within predicted-suitable areas will also allow us to validate and refine the models, and potentially to predict the probability of cases occurring within predicted suitable areas. [ABSTRACT FROM AUTHOR]

Immune-based diagnosis of Buruli ulcer disease (BUD) in children is difficult due to cross-reactivity between mycobacteria. We found that T-cell IFNγ/TNFα responses against Mycobacterium (M.) ulcerans and M. tuberculosis (PPDMulc, PPDMtub) were different between children with BUD (n  = 27) and TB (n  = 20) but only ratios (PPDMtub/PPDMulc) discriminated the study groups efficiently. [ABSTRACT FROM AUTHOR]

Introduction: Coronavirus disease-19 (COVID-19), which started in late December, 2019, has spread to affect 216 countries and territories around the world. Globally, the number of cases of SARS-CoV-2 infection has been growing exponentially. There is pressure on countries to flatten the curves and break transmission. Most countries are practicing partial or total lockdown, vaccination, massive education on hygiene, social distancing, isolation of cases, quarantine of exposed and various screening approaches such as temperature and symptom-based screening to break the transmission. Some studies outside Africa have found the screening for fever using non-contact thermometers to lack good sensitivity for detecting SARS-CoV-2 infection. The aim of this study was to determine the usefulness of clinical symptoms in accurately predicting a final diagnosis of COVID-19 disease in the Ghanaian setting. Method: The study analysed screening and test data of COVID-19 suspected, probable and contacts for the months of March to August 2020. A total of 1,986 participants presenting to Tamale Teaching hospital were included in the study. Logistic regression and receiver operator characteristics (ROC) analysis were carried out. Results: Overall SARS-CoV-2 positivity rate was 16.8%. Those with symptoms had significantly higher positivity rate (21.6%) compared with asymptomatic (17.0%) [chi-squared 15.5, p-value, <0.001]. Patients that were positive for SARS-CoV-2 were 5.9 [3.9–8.8] times more likely to have loss of sense of smell and 5.9 [3.8–9.3] times more likely to having loss of sense of taste. Using history of fever as a screening tool correctly picked up only 14.8% of all true positives of SARS-CoV-2 infection and failed to pick up 86.2% of positive cases. Using cough alone would detect 22.4% and miss 87.6%. Non-contact thermometer used alone, as a screening tool for COVID-19 at a cut-off of 37.8 would only pick 4.8% of positive SARS-CoV-2 infected patients. Conclusion: The use of fever alone or other symptoms individually [or in combination] as a screening tool for SARS-CoV-2 infection is not worthwhile based on ROC analysis. Use of temperature check as a COVID-19 screening tool to allow people into public space irrespective of the temperature cut-off is of little benefit in diagnosing infected persons. We recommend the use of facemask, hand hygiene, social distancing as effective means of preventing infection. [ABSTRACT FROM AUTHOR]