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Background: Monkey pox (Mpox) is a zoonotic viral illness that is endemic to Africa. In contrast to earlier years, the current Mpox outbreak has had an unusually rapid worldwide spread in terms of the number of cases, and most of these cases were among Men who have Sex with Men (MSM). The study aims to assess the knowledge, attitude, practice of infection prevention, and stigma of Mpox disease among members of the MSM community. Methods: The study was a cross-sectional design. A self-administered questionnaire was given to 498 respondents. Results: The mean age of the respondents was 30 years; twenty-three (4.6%) of the total respondents identified themselves as female gender. Urban dwellers were 344 (69.1%). Respondents had a good knowledge of both Mpox disease and transmission; an average knowledge of Mpox symptoms; and an average attitude towards prevention and the practice of prevention of Mpox. Age was found to be significantly associated with knowledge of transmission (p-value). Mpox infection was evaluated as a low-risk event by most responses, but the existence of both community stigma and self-stigma if infected were rated as high. Conclusion: The knowledge of Mpox disease and its transmission was good, while the attitude and practice of infection prevention was average. However, the existence of community and self-stigmatization was high; which perhaps may result in the possibility of an iceberg phenomenon of Mpox disease in the MSM community. Community sensitization, advocacy, and key population-friendly services are recommended for the MSM community to improve their general access to healthcare and reduce stigmatisation.

ObjectiveTo determine the illness severity and mortality among COVID-19-infected healthcare workers (HCWs).DesignA retrospective cohort study using population-level data. Secondary analysis was conducted on collated data from the Public Health Emergency Operations Centre (PHEOC) at the State Ministry of Health, Rivers State, Nigeria. Data were gathered from the COVID-19 patient database of the PHEOC on demographics, place of work, illness severity and outcome.ParticipantsThe cohort included all documented HCWs with confirmed COVID-19 infection (diagnosed by PCR).Primary and secondary outcome measuresIllness severity defined as ‘hospitalisation required’ and treatment outcome labelled as ‘alive’ or ‘dead’ were the outcomes of interest.ResultsThe mean age was 43 years and 50.5% of the cohort were female. Of the 301 HCWs infected, 187 patients were symptomatic with 32 requiring hospitalisation. Seven infected HCWs died of their COVID-19 infection, resulting in a case fatality ratio (CFR) of 2.3%. Population proportions for age groups, case presentation and mortality, would be significantly greater than those seen in the study population. Health professionals made up 79.7% (240) of the study cohort, with 68.8% (165) of them working at the teaching hospitals; the association between HCWs and health facilities they worked in, was significant. Symptomatic cases were more inclined to progress to severe illness (χ(1)2=15.219,α=<0.0001;adjusted OR (aOR) 10.658, 95% CI 2.494 to 45.552); patients also had greater odds of dying from COVID-19 (χ(5)2=13.7,α=0.003;aOR 1.079, 95% CI 1.02 to 1.141) per year increase in age adjusted for sex, case class and illness severity.ConclusionsFrontl-ine HCWs are at an increased risk of exposure to COVID-19 infections. In Nigeria, there is a higher risk of experiencing severe illness if symptomatic while infected with COVID-19. Preventive strategies, proper education and awareness must be put in place to protect HCWs.ObjectiveTo determine the illness severity and mortality among COVID-19-infected HCWs.

Background: The proper management of healthcare data is fundamental to the health system processes; artificial intelligence has proven its value in these processes. Artificial intelligence can simplify the management of information, improve data security, and automate data flow. It is also useful in the analysis and interpretation of big data. Hence, it has the possibility of screening and diagnosing diseases, categorizing disease severity, detecting therapeutic agents, and forecasting outbreak spots.Methods: A data analysis and validation engine was developed to perform data quality control checks, classify addresses, and generate epidemiology numbers using the index and parse command on the command-line interface of DAVE.Results: DAVE correctly formatted data and created a local copy of the datastore and the index. It also returned previous EPID numbers to each entry and assigned a new EPID number to missed entries. DAVE imported the entries into the data template of the existing data management tool and generated a sample manifest that is then sent to the Laboratory. The data flow from the point of collection to storage and reporting was assessed as 100% accurate without errors and in real-time; there was also the ability to roll back if any error occurred.Conclusions: DAVE is a semi-autonomous system that operates with minimal human intervention; it is automatically faster as it leverages computing power to parse, store, and retrieve data while practically eliminating the need for manual data quality assessment. The DAVE functionality can be extended to incorporate additional features like forecasting outbreaks of emerging/re-emerging diseases, categorizing the severity of diseases and analysis of data in our setting.