Performance of heat transfer mechanism in nucleate pool boiling -a relative approach of contribution to various heat transfer components

Nucleate pool steaming is an effective mode of transfer of heat that helps to reduce the use of fossil fuels and thus reduce pollution. Transfer of heat in nucleate pool steaming is examined to occur through the combination of natural convection, enhanced latent heat and convection transport. At the intermediate heat flux range, all three components play a principal character. In the elevated flux of heat area as the heat flux increases the enhanced convection contribution decreases while latent heat transport contribution has been found to increase considerably. In this study, we attempt to develop a heat transfer relationship for the coefficient of transfer of heat and suggested based on the relative benefactions of three components to the boiling flux of heat. The current work stressed the absolute motion of warmth guaranteeing from the summation of each of the three segments of regular convection, upgraded convection and idle warmth transport for water and methanol with round mathematical formed. The hypotheses of air pocket development in nucleate pool bubbling hypothesize for a significant bit of warmth move to the air pocket happens by conduction through a fluid microlayer framed on the warmed surface has been thought of. The maximum deviation of error between the present analytical and experimental one for water, methanol, ethanol and benzene is 6.89%, 5.24%, 5.64% and 6.21% respectively. The highest divergence in the middle of the forecasted data and different (analytical and investigational) outcomes is found to be ±2.54. Results from many other investigators have also been compared for the better visualization of heat transfer correlations to the present one. The highest divergence in the middle of the forecasted data and investigational one for Nusselt number is observed to be ±3.27 along with the present analytical ones.https://http//www.elsevier.com/locate/csiteam2022Mechanical and Aeronautical Engineerin

Distinct monkeypox virus lineages co-circulating in humans before 2022

The 2022 global mpox outbreak raises questions about how this zoonotic disease established effective human-to-human transmission and its potential for further adaptation. The 2022 outbreak virus is related to an ongoing outbreak in Nigeria originally reported in 2017, but the evolutionary path linking the two remains unclear due to a lack of genomic data between 2018, when virus exportations from Nigeria were first recorded, and 2022, when the global mpox outbreak began. Here, 18 viral genomes obtained from patients across southern Nigeria in 2019-2020 reveal multiple lineages of monkeypox virus (MPXV) co-circulated in humans for several years before 2022, with progressive accumulation of mutations consistent with APOBEC3 activity over time. We identify Nigerian A.2 lineage isolates, confirming the lineage that has been multiply exported to North America independently of the 2022 outbreak originated in Nigeria, and that it has persisted by human-to-human transmission in Nigeria for more than 2 years before its latest exportation. Finally, we identify a lineage-defining APOBEC3-style mutation in all A.2 isolates that disrupts gene A46R, encoding a viral innate immune modulator. Collectively, our data demonstrate MPXV capacity for sustained diversification within humans, including mutations that may be consistent with established mechanisms of poxvirus adaptation.</p

COVID-19 mortality rate and its associated factors during the first and second waves in Nigeria

COVID-19 mortality rate has not been formally assessed in Nigeria. Thus, we aimed to address this gap and identify associated mortality risk factors during the first and second waves in Nigeria. This was a retrospective analysis of national surveillance data from all 37 States in Nigeria between February 27, 2020, and April 3, 2021. The outcome variable was mortality amongst persons who tested positive for SARS-CoV-2 by Reverse-Transcriptase Polymerase Chain Reaction. Incidence rates of COVID-19 mortality was calculated by dividing the number of deaths by total person-time (in days) contributed by the entire study population and presented per 100,000 person-days with 95% Confidence Intervals (95% CI). Adjusted negative binomial regression was used to identify factors associated with COVID-19 mortality. Findings are presented as adjusted Incidence Rate Ratios (aIRR) with 95% CI. The first wave included 65,790 COVID-19 patients, of whom 994 (1∙51%) died; the second wave included 91,089 patients, of whom 513 (0∙56%) died. The incidence rate of COVID-19 mortality was higher in the first wave [54∙25 (95% CI: 50∙98–57∙73)] than in the second wave [19∙19 (17∙60–20∙93)]. Factors independently associated with increased risk of COVID-19 mortality in both waves were: age ≥45 years, male gender [first wave aIRR 1∙65 (1∙35–2∙02) and second wave 1∙52 (1∙11–2∙06)], being symptomatic [aIRR 3∙17 (2∙59–3∙89) and 3∙04 (2∙20–4∙21)], and being hospitalised [aIRR 4∙19 (3∙26–5∙39) and 7∙84 (4∙90–12∙54)]. Relative to South-West, residency in the South-South and North-West was associated with an increased risk of COVID-19 mortality in both waves. In conclusion, the rate of COVID-19 mortality in Nigeria was higher in the first wave than in the second wave, suggesting an improvement in public health response and clinical care in the second wave. However, this needs to be interpreted with caution given the inherent limitations of the country’s surveillance system during the study

The One Health approach to incident management of the 2019 Lassa fever outbreak response in Nigeria.

Globally, effective emergency response to disease outbreaks is usually affected by weak coordination. However, coordination using an incident management system (IMS) in line with a One Health approach involving human, environment, and animal health with collaborations between government and non-governmental agencies result in improved response outcome for zoonotic diseases such as Lassa fever (LF). We provide an overview of the 2019 LF outbreak response in Nigeria using the IMS and One Health approach. The response was coordinated via ten Emergency Operation Centre (EOC) response pillars. Cardinal response activities included activation of EOC, development of an incident action plan, deployment of One Health rapid response teams to support affected states, mid-outbreak review and after-action review meetings. Between 1st January and 29th December 2019, of the 5057 people tested for LF, 833 were confirmed positive from 23 States, across 86 Local Government Areas. Of the 833 confirmed cases, 650 (78%) were from hotspot States of Edo (36%), Ondo (26%) and Ebonyi (16%). Those in the age-group 21-40 years (47%) were mostly affected, with a male to female ratio of 1:1. Twenty healthcare workers were affected. Two LF naïve states Kebbi and Zamfara, reported confirmed cases for the first time during this period. The outbreak peaked earlier in the year compared to previous years, and the emergency phase of the outbreak was declared over by epidemiological week 17 based on low national threshold composite indicators over a period of six consecutive weeks. Multisectoral and multidisciplinary strategic One Health EOC coordination at all levels facilitated the swift containment of Nigeria's large LF outbreak in 2019. It is therefore imperative to embrace One Health approach embedded within the EOC to holistically address the increasing LF incidence in Nigeria

Exportation of Monkeypox Virus From the African Continent.

BACKGROUND: The largest West African monkeypox outbreak began September 2017, in Nigeria. Four individuals traveling from Nigeria to the United Kingdom (n = 2), Israel (n = 1), and Singapore (n = 1) became the first human monkeypox cases exported from Africa, and a related nosocomial transmission event in the United Kingdom became the first confirmed human-to-human monkeypox transmission event outside of Africa. METHODS: Epidemiological and molecular data for exported and Nigerian cases were analyzed jointly to better understand the exportations in the temporal and geographic context of the outbreak. RESULTS: Isolates from all travelers and a Bayelsa case shared a most recent common ancestor and traveled to Bayelsa, Delta, or Rivers states. Genetic variation for this cluster was lower than would be expected from a random sampling of genomes from this outbreak, but data did not support direct links between travelers. CONCLUSIONS: Monophyly of exportation cases and the Bayelsa sample, along with the intermediate levels of genetic variation, suggest a small pool of related isolates is the likely source for the exported infections. This may be the result of the level of genetic variation present in monkeypox isolates circulating within the contiguous region of Bayelsa, Delta, and Rivers states, or another more restricted, yet unidentified source pool

Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019 : A systematic analysis for the Global Burden of Disease Study 2019

Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC

Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Trends in Malaria Cases and Deaths: Assessing National Prevention and Control Progress in Burundi

Background:&nbsp;Malaria is associated with high morbidity and mortality especially in World’s tropical regions. In 2016, an estimated 216 million and 445,000 cases of malaria and deaths associated with malaria respectively were reported globally. Malaria is the first leading cause of outpatient visits, hospitalization and death in Burundi. We therefore examined the trend in malaria cases and deaths in Burundi.Methods:&nbsp;We extracted data from Burundi National Health Information System (BNHIS) and assessed trends in malaria cases and deaths from January 2015 to December 2017. A suspected case of malaria was defined as any person treated by anti-malarial drugs without testing while a confirmed case as any person with a positive microscopy or rapid diagnostic test for malaria parasite. We described malaria cases and deaths, and calculated malaria case incidence rate.Results:&nbsp;A total of22,225,699 malaria cases with 8,660 deaths (CFR 0.04%) was documented during the study period. Out of 22,225,699 cases, 45,291 cases (0.2%) were suspected malaria cases. The observed peak season of malaria infection in any of the studied year was in the raining season (March-June). All provinces of the country were affected. Kirundo and Cankuzo provinces the incidence of malaria cases increased from 10.1 cases per 1,000 persons in 2015 to 13.2 cases per 1,000 persons in 2017. The case fatality rate decreased from 0.06% in 2015 to 0.01% in 2017.Conclusions:&nbsp;An increasing trend in malaria prevalence was observed in Burundi but Kirundo and Cankuzo provinces were the most affected. However, the case fatality decreased within the studied period. Malaria intervention should be intensified/scaled up in the raining season and the most affected provinces

Reemergence of Human Monkeypox in Nigeria, 2017

In Nigeria, before 2017 the most recent case of human monkeypox had been reported in 1978. By mid-November 2017, a large outbreak caused by the West African clade resulted in 146 suspected cases and 42 laboratory-confirmed cases from 14 states. Although the source is unknown, multiple sources are suspected