Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Skilled delivery inequality in Ethiopia: To what extent are the poorest and uneducated mothers benefiting?

BACKGROUND: The fifth Millennium Development Goal (MDG) targeted at improving maternal health. In this regard, Ethiopia has shown substantial progresses in the past two decades. Nonetheless, these impressive gains are unevenly distributed among Ethiopian women with different socio-economic characteristics. This study aimed at investigating levels and trends of skilled delivery service, and wealth and education related inequalities from 2000 to 16. METHODS: Longitudinal data analysis was conducted on Ethiopian Demographic and Health Survey (EDHS) data of 2000, 2005, 2011 and 2016. The outcome variable was skilled delivery, while data on economic status and education level were used as dimensions of inequality. Rate Ratio (RR) and Rate Difference (RD) inequality measures were applied. STATA for windows version 10.1 statistical software was utilized for data analysis and presentation. The strength of association of inequality dimensions with the outcome variable was assessed using a 95% confidence interval. RESULTS: From total deliveries, 5.62%, 6.3%, 10.8% and 28% of them were attended by skilled birth attendant in 2000, 2005, 2011 and 2016 respectively. In the most recent survey (EDHS 2016), proportion of births attended by skilled birth attendance among women who completed secondary and above education was about 5.42 [95% CI (4.53, 6.09)] times more when compared to women with no formal education. Proportion of births attended by skilled birth attendance among women in the richest quintile was about 5.11 [95% CI (3.98, 6.12)] times higher than that of women in the poorest quintile. Moreover, gap of inequality on receiving skilled delivery service has increased substantially from 24.2 (2000) to 53.8 (2016) percentage points between women in the richest and poorest quintiles; and from 44.9 (2000) to 76.0 (2016) percentage points between women who completed secondary and above education and women with no formal education. CONCLUSIONS: Skilled birth attendance remained low and virtually unchanged during the period 2000–2011, but increased substantially in 2016. Gap on wealth and education related inequalities increased linearly during 2000–16. Most pronounced inequalities were observed in women’s level of education revealing women with no formal education were the most underserved subgroups. Encouraging women in education and economic development programs should be strengthened as part of the effort to attain Universal Health Coverage (UHC) of Sustainable Development Goals (SDGs) in Ethiopia

Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms

BACKGROUND: Skilled assistance during childbirth is essential to reduce maternal deaths. However, in Ethiopia, which is among the six countries contributing to more than half of the global maternal deaths, the coverage of births attended by skilled health personnel remains very low. The aim of this study was to identify determinants and develop a predictive model for skilled delivery service use in Ethiopia by applying logistic regression and machine-learning techniques. METHODS: Data from the 2016 Ethiopian Demographic and Health Survey (EDHS) was used for this study. Statistical Package for Social Sciences (SPSS) and Waikato Environment for Knowledge Analysis (WEKA) tools were used for logistic regression and model building respectively. Classification algorithms namely J48, Naïve Bayes, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were used for model development. The validation of the predictive models was assessed using accuracy, sensitivity, specificity, and area under Receiver Operating Characteristics (ROC) curve. RESULTS: Only 27.7% women received skilled delivery assistance in Ethiopia. First antenatal care (ANC) [AOR = 1.83, 95% CI (1.24–2.69)], birth order [AOR = 0.22, 95% CI (0.11–0.46)], television ownership [AOR = 6.83, 95% CI (2.52–18.52)], contraceptive use [AOR = 1.92, 95% CI (1.26–2.97)], cost needed for healthcare [AOR = 2.17, 95% CI (1.47–3.21)], age at first birth [AOR = 1.96, 95% CI (1.31–2.94)], and age at first sex [AOR = 2.72, 95% CI (1.55–4.76)] were determinants for utilizing skilled delivery services during the childbirth. Predictive models were developed and the J48 model had superior predictive accuracy (98%), sensitivity (96%), specificity (99%) and, the area under ROC (98%). CONCLUSIONS: First ANC and contraceptive uses were among the determinants of utilization of skilled delivery services. A predictive model was developed to forecast the likelihood of a pregnant woman seeking skilled delivery assistance; therefore, the predictive model can help to decide targeted interventions for a pregnant woman to ensure skilled assistance at childbirth. The model developed through the J48 algorithm has better predictive accuracy. Web-based application can be build based on results of this study

Evaluating the skills of the CMIP5 global climate models using multicriteria decision-making analysis in Ethiopia

This study evaluates the skills of 30 CMIP5 GCMs and the Multimodel Ensemble (MME) in reproducing the characteristics of observed precipitation (Pr), minimum (Tmin), and maximum (Tmax) temperature over the Middle Awash sub-basin (MASB) in Ethiopia. The MME of the climate variables was generated using the simple arithmetic mean method. The entire analysis was performed on the raw historical GCM simulations (before bias correction) and observed data for the periods 1981–2005 based on monthly and annual time series data over the annual and seasonal temporal resolutions. This study considered two approaches. The first one was an evaluation of GCMs employing five statistical performance metrics (SPMs), i.e., mean, CV, PBIAS, RSR, and r. The second approach involves the application of multicriteria decision-making (MCDM) analysis, adopting three SPMs (PBIAS, RSR, and r). The relative weights of the three metrics were determined by the entropy method. Besides, the weighted average and compromise programming techniques were employed to rank and select the best-performing GCMs. The findings from the first approach using five SPMs demonstrate that, for a given variable of interest, a GCM that performs well for one SPM may fail to produce the same for another SPM on the same temporal scale. Likewise, for the same SPM at different resolutions, a GCM may perform well for a one-time scale but poorly for another. These suggested that the results of GCM skills relied mainly on the SPM, time scale, and data formats chosen for analysis. Hence, it is critical to comprehensively evaluate the skill of GCMs using multiple performance metrics over a range of spatial and temporal settings and data formats. In addition, results of the MCDM analysis proved that the ensemble of GCMs, which provide adequate performance in simulating the salient features of Pr, Tmin, and Tmax concomitantly across the MASB, encompass CMCC-CMS, BCC-CSM1.1(m), CMCC-CM, BNU-ESM, CanESM2, and MPI-ESM-MR. However, it was observed that different GCMs performed much differently in characterizing various variables over a range of temporal scales and data formats. The MME also proved its superior potential in duplicating the climate of the study area over several individual GCMs. The overall findings attested that instead of aggregating the ranks from the three variables into one, it is recommended to treat each variable independently while developing a subset of best-performing GCMs for ensembling since each GCM responds differently to each variable under a set of conditions. Finally, the approaches and findings from this study will be valuable input for subsequent climate and hydrologic studies in the study area and beyond

Manuscript submission invitations from "predatory journals": What should authors do?

Press freedom and worldwide internet access have opened ample opportunity for a staggering number of poor open access journals and junk publishers to emerge. Dubious publishers are abusing and camouflaging the golden open access model. In 2012, Jeffery Beall shed light on the predatory journals (as he preferred to call them) and the threat to open access scientific publication. Publishing in predatory journals is continuing to be a major threat for the development of science in developing countries. The authors of this article proposed solutions and outline a fresh perspective to help authors avoid publishing in predatory journals

The role of the Stop Transmission of Polio (STOP) program in developing countries : the experience of Kenya

BACKGROUND: In 1988, the 41st World Health Assembly (WHA) marked the launch of the Global Polio Eradication Initiative (GPEI) for the eradication of polio. A key component of the GPEI has been the development and deployment of a skilled workforce to implement eradication activities. In 1989, the Stop Transmission of Polio (STOP) was initiated to address skilled human resource gaps and strengthen poliovirus surveillance. This paper describes the role of the STOP 52 team in technical capacity building and health system strengthening in the implementation of polio eradication strategies in Kenya following the outbreak of Circulating Vaccine-derived Poliovirus type 2 (cVDPV2). METHODS: Overview of the STOP program, deployment, and the modality of support are described. Descriptive analysis was conducted using data collected by the STOP 52 team during integrated supportive supervisory visits conducted from July 2018 to September 2019. Analyses were carried out using Epi-Info statistical software (Version 7.0) and maps were developed using Quantum Geographic Information System (Q-GIS) (version 3.12.0). RESULTS: The STOP 52 team supportively supervised 870 health facilities on Expanded Program on Immunization (EPI), and Acute Flaccid Paralysis (AFP) and other Vaccine-Preventable Diseases (VPDs) surveillance in 16 (34.1%) of the 47 counties during the study period. AFP surveillance was conducted in all health facilities supervised leading to the detection and investigation of 11 unreported AFP cases. The STOP 52 team, as part of the outbreak response, provided technical support to five successive rounds of polio Supplementary Immunization Activities (SIAs) conducted during the study period. Moreover, in addressing programmatic data needs, the STOP 52 Data Manager played a valuable role in enhancing the quality and use of data for evidence-based planning and decision-making. The STOP 52 team contributed to the development of operational plans, guidelines and training manuals, and participated in the delivery of various Training of Trainers (TOT) and On-the-Job Training (OJT) on EPI, AFP and other VPDs surveillance including data management. CONCLUSION: The STOP 52 team has contributed to polio eradication efforts in Kenya by enhancing AFP and other VPDs surveillance, supporting polio SIAs, strengthening EPI, use of quality EPI, AFP and other VPDs data, and capacity building of Frontline Health Workers (FLWs). The use of Open Data Kit (ODK) technology during supportive supervision, and AFP and other VPDs surveillance was found to be advantageous. A national STOP program should be modeled to produce a homegrown workforce to ensure the availability of more sustainable technical support for polio eradication efforts in Kenya and possibly other polio-affected countries

Epidemiology of measles cases, vaccine effectiveness, and performance towards measles elimination in The Gambia

Introduction: In 2011, member states of the World Health Organization (WHO) Africa Regional Office (AFRO) resolved to eliminate Measles by 2020. Our study aims to assess The Gambia’s progress towards the set AFRO measles elimination target and highlight surveillance and immunisation gaps to better inform future measles prevention strategies. Material and methods: A retrospective review of measles surveillance data for the period 2011–2019, was extracted from The Gambia case-based measles surveillance database. WHO—UNICEF national coverage estimates were used for estimating national level MCV coverage. Measles post campaign coverage survey coverage estimates were used to estimate national measles campaign coverage. Results: One hundred and twenty-five of the 863 reported suspected cases were laboratory confirmed as measles cases. More than half (53.6%) of the confirmed cases have unknown vaccination status, 24% of cases were vaccinated, 52.8% of cases occurred among males, and 72.8% cases were among urban residents. The incidence of measles cases per million population was lowest (0) in 2011–2012 and highest in 2015 and 2016 (31 and 23 respectively). The indicator for surveillance sensitivity was met in all years except in 2016 and 2019. Children aged 5–9 years (Incidence Rate Ratio—IRR = 0.6) and residents of Central River region (IRR = 0.21) had lower measles risk whilst unvaccinated (Adjusted IRR = 5.95) and those with unknown vaccination status (IRR 2.21) had higher measles risk. Vaccine effectiveness was 89.5%. Conclusion: The Gambia’s quest to attain measles elimination status by 2020 has registered significant success but it is unlikely that all target indicators will be met. Vaccination has been very effective in preventing cases. There is variation in measles risk by health region, and it will be important to take it into account when designing prevention and control strategies. The quality of case investigations should be improved to enhance the quality of surveillance for decision making

An epidemiological analysis of Acute Flaccid Paralysis (AFP) surveillance in Kenya, 2016 to 2018

Background: The poliovirus has been targeted for eradication since 1988. Kenya reported its last case of indigenous Wild Poliovirus (WPV) in 1984 but suffered from an outbreak of circulating Vaccine-derived Poliovirus type 2 (cVDPV2) in 2018. We aimed to describe Kenya's polio surveillance performance 2016-2018 using WHO recommended polio surveillance standards. Methods: Retrospective secondary data analysis was conducted using Kenyan AFP surveillance case-based database from 2016 to 2018. Analyses were carried out using Epi-Info statistical software (version 7) and mapping was done using Quantum Geographic Information System (GIS) (version 3.4.1). Results: Kenya reported 1706 cases of AFP from 2016 to 2018. None of the cases were confirmed as poliomyelitis. However, 23 (1.35%) were classified as polio compatible. Children under 5 years accounted for 1085 (63.6%) cases, 937 (55.0%) cases were boys, and 1503 (88.1%) cases had received three or more doses of Oral Polio Vaccine (OPV). AFP detection rate substantially increased over the years; however, the prolonged health workers strike in 2017 negatively affected key surveillance activities. The mean Non-Polio (NP-AFP) rate during the study period was 2.87/ 100,000 children under 15 years, and two adequate specimens were collected for 1512 (88.6%) AFP cases. Cumulatively, 31 (66.0%) counties surpassed target for both WHO recommended AFP quality indicators. Conclusions: The performance of Kenya's AFP surveillance system surpassed the minimum WHO recommended targets for both non-polio AFP rate and stool adequacy during the period studied. In order to strengthen the country's polio free status, health worker's awareness on AFP surveillance and active case search should be strengthened in least performing counties to improve case detection. Similar analyses should be done at the sub-county level to uncover underperformance that might have been hidden by county level analysis