BackgroundMonkeypox (MPX) is endemic in Nigeria, but it was first reported in Adamawa state, North-Eastern Nigeria, in January 2022. There are currently 172 cases of MPX in Nigeria, with four reported deaths, and Adamawa has the second-highest case count. Therefore, this study was undertaken to evaluate the epidemiological profile of this viral disease.MethodsThis is a cross-sectional study. The skin and blood samples were screened for the presence for Monkeypox virus (MPXV) and Varicella Zoster virus (VZV) DNA by real-time PCR; the clinical diagnosis was based on symptoms of visual signs of skin lesions and other clinical symptoms from January to July 2022.ResultsA total of 33 suspected cases aged 1–57 years [26 (79%) males vs. 7 (21%) females] were screened for MPX and VZV. Twenty-four (72.7%) were positive (6.1% were MPX only, 39% were VZV only, and 27% were both MPX and VZV). Most cases of MPX (82%), VZV (69%) and MPX-VZV co-infection (78%) occurred in males. More than half (54%) of those infected were children and adolescents between 0 and 19 years. All patients experienced body rashes and itching, and other clinical symptoms included fever, headache, mouth sores, muscle aches and lymphadenopathy. Over 64 and 86% of patients had contact with livestock and rodents, respectively.ConclusionMPXV, VZV and MPXV-VZV co-infections occurred predominantly among males and children in Adamawa state, Nigeria. Given the patient contact with rodents and livestock, further research on the animal reservoir is needed to highlight the transmission of MPXV in Adamawa.

No abstract

Healthcare workers (HCWs) face an unprecedented higher risk of COVID-19 infection due to their work and exposure. In this study, we aim to examine the associated risk factors for COVID-19 infection among HCWs in North-East Nigeria. We used data collected retrospectively among a cohort of clinical and non-clinical HCWs in six healthcare facilities in Adamawa State, Nigeria. We estimated the marginal probability of COVID-19 infection among HWCs using alternating logistic regression via the generalized estimating equations (GEE) approach. Among the 318 HCWs, 178 (55.97%) were males, mean (±SD) age was 36.81 (±8.98), 237 (74.76%) were clinical, and 80 (25.24) were non-clinical staff. The overall prevalence of COVID-19 was 16.67% among HCWs. After adjusting for other variables in the model, our results showed that clinical staff had a 5-fold higher risk of COVID-19 infection than non-clinical staff (aOR = 5.07, 95% CI: 1.32–19.52). Moreover, significant exposure risk factors for COVID-19 infection for HCWs increase with age, time spent attending to patients, caring for COVID-19 patients, and having worked with COVID-19 samples, while the risk decreases with the use of an N95 mask. Our findings suggested that the burden of COVID-19 infection is higher for clinical staff than non-clinical staff, and increasing age contributed to the increased risk.

Bovine respiratory disease (BRD) is a major cause of illness and death in cattle; however, its global extent and distribution remain unclear. As climate change continues to impact the environment, it is important to understand the environmental factors contributing to BRD’s emergence and re-emergence. In this study, we used machine-learning models and remotely sensed climate data at 2.5 min (21 km2) resolution environmental layers to estimate the risk of BRD and predict its potential future distribution. We analysed 13,431 BRD cases from 1727 cities worldwide between 2005 and 2021 using two machine-learning models, maximum entropy (MaxEnt) and Boosted Regression Trees (BRT), to predict the risk and geographical distribution of the risk of BRD globally with varying model parameters. Different re-sampling regimes were used to visualise and measure various sources of uncertainty and prediction performance. The best-fitting model was assessed based on the area under the receiver operator curve (AUC-ROC), positive predictive power and Cohen’s Kappa. We found that BRT had better predictive power compared with MaxEnt. Our findings showed that favourable habitats for BRD occurrence were associated with the mean annual temperature, precipitation of the coldest quarter, mean diurnal range and minimum temperature of the coldest month. Similarly, we showed that the risk of BRD is not limited to the currently known suitable regions of Europe and west and central Africa but extends to other areas, such as Russia, China and Australia. This study highlights the need for global surveillance and early detection systems to prevent the spread of disease across borders. The findings also underscore the importance of bio-security surveillance and livestock sector interventions, such as policy-making and farmer education, to address the impact of climate change on animal diseases and prevent emergencies and the spread of BRD to new areas.

No abstract