Search

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone's electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 virus pandemic has caused a significant number of deaths worldwide. If the prevalence of the infection continues to grow, this could impact life expectancy. This paper provides first estimates of the potential direct impact of the COVID-19 pandemic on period life expectancy. Methods: From the estimates of bias-adjusted age-specific infection fatality rates in Hubei (China) and a range of six prevalence rate assumptions ranging from 1% to 70%, we built a discrete-time microsimulation model that simulates the number of people infected by COVID-19, the number dying from it, and the number of deaths from all causes week by week for a period of one year. We applied our simulation to four broad regions: North America and Europe; Latin America and the Caribbean; Southeastern Asia; and sub-Saharan African. For each region, 100,000 individuals per each 5-year age group are simulated. Results: At a 10% COVID-19 prevalence rate, the loss in life expectancy at birth is likely above 1 year in North America and Europe and in Latin America and the Caribbean. In Southeastern Asia and sub-Saharan Africa, one year lost in life expectancy corresponds to an infection prevalence of about 15% and 25%, respectively. Given the uncertainty in fatality rates, with a 50% prevalence of COVID-19 infections under 95% prediction intervals, life expectancy would drop by 3 to 9 years in North America and Europe, by 3 to 8 years in Latin America and the Caribbean, by 2 to 7 years in Southeastern Asia, and by 1 to 4 years in sub-Saharan Africa. In all prevalence scenarios, as long as the COVID-19 infection prevalence rate remains below 1 or 2%, COVID-19 would not affect life expectancy in a substantial manner. Interpretation: In regions with relatively high life expectancy, if the infection prevalence threshold exceeds 1 or 2%, the COVID-19 pandemic will break the secular trend of increasing life expectancy, resulting in a decline in period life expectancy. With life expectancy being a key indicator of human development, mortality increase, especially among the vulnerable subgroups of populations, would set a country back on its path of human development. [ABSTRACT FROM AUTHOR]

Objective: To characterize the impact of widespread inventory management policies on stock-outs of essential drugs in Zambia’s health clinics and develop related recommendations. Methods: Daily clinic storeroom stock levels of artemether-lumefantrine (AL) products in 2009–2010 were captured in 145 facilities through photography and manual transcription of paper forms, then used to determine historical stock-out levels and estimate demand patterns. Delivery lead-times and estimates of monthly facility accessibility were obtained through worker surveys. A simulation model was constructed and validated for predictive accuracy against historical stock-outs, then used to evaluate various changes potentially affecting product availability. Findings: While almost no stock-outs of AL products were observed during Q4 2009 consistent with primary analysis, up to 30% of surveyed facilities stocked out of some AL product during Q1 2010 despite ample inventory being simultaneously available at the national warehouse. Simulation experiments closely reproduced these results and linked them to the use of average past monthly issues and failure to capture lead-time variability in current inventory control policies. Several inventory policy enhancements currently recommended by USAID | DELIVER were found to have limited impact on product availability. Conclusions: Inventory control policies widely recommended and used for distributing medicines in sub-Saharan Africa directly account for a substantial fraction of stock-outs observed in common situations involving demand seasonality and facility access interruptions. Developing central capabilities in peripheral demand forecasting and inventory control is critical. More rigorous independent peer-reviewed research on pharmaceutical supply chain management in low-income countries is needed. [ABSTRACT FROM AUTHOR]

This study investigates meteorological drought in sub‐Saharan Africa within the context of elephant conservation. Prolonged drought significantly impacts elephants, leading to increased mortality rates and heightened human–elephant conflicts. We assess both the anticipated 21st century changes in impact‐relevant meteorological drought metrics and the efficacy of existing forecasting systems in predicting such droughts on seasonal time scales. The climate change element of our study uses the 6th Coupled Model Intercomparison Project (CMIP6) ensemble to evaluate projected change in 3‐month Standardized Precipitation Index (SPI3). We then carry out a quantitative assessment of seasonal forecast skill, utilizing 110 years of precipitation hindcasts generated by the European Centre for Medium Range Forecasting (ECMWF) system. Our findings indicate that persistent drought is projected to become more frequent over the 21st century in southern Africa, where the majority of elephants reside. Analysis of seasonal hindcasts indicates that, while the forecasts have greater skill than climatology, they remain highly uncertain. Previous work suggests that it may be possible to reduce this uncertainty by contextualizing forecasts within specific climate regimes. However, even with improved forecast skill, effective action hinges on the alignment of forecasts with the practical needs of conservation practitioners. Over the next decades, a co‐production approach will be critical for leveraging seasonal forecasts for climate change adaptation within the conservation sector. [ABSTRACT FROM AUTHOR]

Soil moisture is a critical environmental variable that determines primary productivity and contributes to climatic processes. It is, therefore, important to forecast soil moisture to inform expectations of derivative outputs reliably. While forecasting soil moisture continues to advance, there is a need to extend it to different geoclimatic regions, including in sub-Saharan Africa, where livelihoods predominantly rely on subsistence agriculture. We used remotely sensed soil moisture data produced by the European Space Agency – Climate Change Initiative (ESA CCI). The data, which covered the period 1978 to 2019, were used to forecast monthly soil moisture in different agroecological zones and land cover types. The Seasonal Random Walk, Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting models were trained on 70% of the data (November 1978 – August 2007) and subsequently applied to a test dataset (September 2007 – December 2019). All models showed solid prediction accuracies for all agroecological zones (unbiased root mean square error, ubRMSE ≤ 0.05 m3 m−3) and land cover types (ubRMSE ≤ 0.04 m3 m−3). This was corroborated by similarities in season-adjusted anomalies between observed and forecasted soil moisture for nearly all agroecological zones and land cover types, with a correlation coefficient of r > 0.5 for most locations). The broad-scale interpretation of soil moisture forecasting can inform moisture availability and variability by regions; however, more research is encouraged to improve forecasting at spatially and temporally detailed levels to assist small-scale farming practices in the continent. [ABSTRACT FROM AUTHOR]

Understanding the detailed timing of crop phenology and their variability enhances grain yield and quality by providing precise scheduling of irrigation, fertilization, and crop protection mechanisms. Advances in information and communication technology (ICT) provide a unique opportunity to develop agriculture-related tools that enhance wall-to-wall upscaling of data outputs from point-location data to wide-area spatial scales. Because of the heterogeneity of the worldwide agro-ecological zones where crops are cultivated, it is unproductive to perform plant phenology research without providing means to upscale results to landscape-level while safeguarding field-scale relevance. This paper presents an advanced, reproducible, and open-source software for plant phenology prediction and mapping (PPMaP) that inputs data obtained from multi-location field experiments to derive models for any crop variety. This information can then be applied consecutively at a localized grid within a spatial framework to produce plant phenology predictions at the landscape level. This software runs on the 'Windows' platform and supports the development of process-oriented and temperature-driven plant phenology models by intuitively and interactively leading the user through a step-by-step progression to the production of spatial maps for any region of interest in sub-Saharan Africa. Maize (Zea mays L.) was used to demonstrate the robustness, versatility, and high computing efficiency of the resulting modeling outputs of the PPMaP. The framework was implemented in R, providing a flexible and easy-to-use GUI interface. Since this allows for appropriate scaling to the larger spatial domain, the software can effectively be used to determine the spatially explicit length of growing period (LGP) of any variety. [ABSTRACT FROM AUTHOR]

Warfarin remains the most widely prescribed oral anticoagulant in sub‐Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21 machine‐learning techniques in predicting stable warfarin dose in sub‐Saharan Black‐African patients and (b) externally validated a previously developed Warfarin Anticoagulation in Patients in Sub‐Saharan Africa (War‐PATH) clinical dose–initiation algorithm. The development cohort included 364 patients recruited from eight outpatient clinics and hospital departments in Uganda and South Africa (June 2018–July 2019). Validation was conducted using an external validation cohort (270 patients recruited from August 2019 to March 2020 in 12 outpatient clinics and hospital departments). Based on the mean absolute error (MAE; mean of absolute differences between the actual and predicted doses), random forest regression (12.07 mg/week; 95% confidence interval [CI], 10.39–13.76) was the best performing machine‐learning technique in the external validation cohort, whereas the worst performing technique was model trees (17.59 mg/week; 95% CI, 15.75–19.43). By comparison, the simple, commonly used regression technique (ordinary least squares) performed similarly to more complex supervised machine‐learning techniques and achieved an MAE of 13.01 mg/week (95% CI, 11.45–14.58). In summary, we have demonstrated that simpler regression techniques perform similarly to more complex supervised machine‐learning techniques. We have also externally validated our previously developed clinical dose–initiation algorithm, which is being prospectively tested for clinical utility. [ABSTRACT FROM AUTHOR]

Although the emergence of gender segregation in early childhood is a well‐established pattern in formal settings (i.e., group childcare, preschool) from research predominantly in North America, little is known about the gender segregation among young children in Sub‐Saharan Africa, especially in the contexts of ethnically diverse informal urban settlements. Using naturalistic observations of 62 focal‐children (2 to 4 year olds) from 4 ethnic groups (Kamba, Kikuyu, Luo, and Maasai) in one informal urban settlement in Kenya, we examined the gender segregation in social interactions and proximity to other children in their daily lives. Focal‐children's ethnicity, age, and gender were considered as potential predictors of gender segregation (i.e., tendency to spend time in close proximity or social interaction with children of their same gender). Ethnicity was the best predictor of gender segregation, as Maasai children exhibited more evidence of gender segregation than children of other ethnicities. Age and gender did not predict the patterns of gender segregation in this sample. Findings are discussed with respect to cultural roles related to gender, ethnically diverse urban communities, social development in early childhood, and prevalent assumptions about the universality of gender segregation in early childhood. [ABSTRACT FROM AUTHOR]

Aim: HIV prevention measures in sub-Saharan Africa are still short of attaining the UNAIDS 90-90-90 fast track targets set in 2014. Identifying predictors for HIV status may facilitate targeted screening interventions that improve health care. We aimed at identifying HIV predictors as well as predicting persons at high risk of the infection.Method: We applied machine learning approaches for building models using population-based HIV Impact Assessment (PHIA) data for 41,939 male and 45,105 female respondents with 30 and 40 variables respectively from four countries in sub-Saharan countries. We trained and validated the algorithms on 80% of the data and tested on the remaining 20% where we rotated around the left-out country. An algorithm with the best mean f1 score was retained and trained on the most predictive variables. We used the model to identify people living with HIV and individuals with a higher likelihood of contracting the disease.Results: Application of XGBoost algorithm appeared to significantly improve identification of HIV positivity over the other five algorithms by f1 scoring mean of 90% and 92% for males and females respectively. Amongst the eight most predictor features in both sexes were: age, relationship with family head, the highest level of education, highest grade at that school level, work for payment, avoiding pregnancy, age at the first experience of sex, and wealth quintile. Model performance using these variables increased significantly compared to having all the variables included. We identified five males and 19 females individuals that would require testing to find one HIV positive individual. We also predicted that 4·14% of males and 10.81% of females are at high risk of infection.Conclusion: Our findings provide a potential use of the XGBoost algorithm with socio-behavioural-driven data at substantially identifying HIV predictors and predicting individuals at high risk of infection for targeted screening. [ABSTRACT FROM AUTHOR]

Background: A woman's ability to achieve her preferred family size is critical in addressing issues of high fertility in sub-Saharan Africa. The socio-cultural context in sub-Saharan Africa presents some difficulty for the attainment of preferred fertility for many women. Few studies in sub-Saharan Africa have examined the extent to which women are unable to achieve their preferred family sizes. This study, therefore, examines the factors that are associated with the non-attainment of women's preferred fertility by the end of their reproductive years. Data and methods: The study analyzed pooled cross-sectional data with a sub-sample of 1,888 currently married women aged 45–49 years from five rounds of the Ghana Demographic and Health Survey, 1993 to 2014. Test of associations and multinomial logistic regression analysis were used to examine the predictors of underachieved and overachieved fertility relative to achieved fertility. Results: The results indicate that 44 per cent of the women recorded overachieved fertility while about 36 per cent underachieved their fertility. Partner wants more, experiencing child loss and married more than once were significantly associated with overachieved fertility. Nonetheless, increased years of a woman's education and delaying her at first birth were negatively associated with overachieved fertility. On the other hand, underachieved fertility was significantly associated with having a partner with fewer fertility preference, being of the Islamic faith and ever use of modern contraception. Conclusion: Partner's fertility preference, child loss experience, marrying more than once and ever use of modern contraception were important predictors of a woman's inability to achieve her fertility preference. Policies to regulate men's fertility behaviour, delaying age at first birth, use of modern contraception, encouraging longer years of education, and reducing infant and child mortality are important strategies to achieve fertility preference in Ghana. [ABSTRACT FROM AUTHOR]

Old, climatically buffered, infertile landscapes (OCBILs) have been hypothesized to harbour an elevated number of persistent plant lineages and are predicted to occur across different parts of the globe, interspersed with other types of landscapes. We tested whether the mean age of a plant community is associated with occurrence on OCBILs, as predicted by climatic stability and poor soil environments. Using digitized occurrence data for seed plants occurring in Australia (7033 species), sub-Saharan Africa (3990 species) and South America (44 482 species), regions that comprise commonly investigated OCBILs (Southwestern Australian Floristic Region, Greater Cape Floristic Region and campos rupestres), and phylogenies pruned to match the species occurrences, we tested for associations between environmental data (current climate, soil composition, elevation and climatic stability) and two novel metrics developed here that capture the age of a community (mean tip length and mean node height). Our results indicate that plant community ages are influenced by a combination of multiple environmental predictors that vary globally; we did not find statistically strong associations between the environments of OCBIL areas and community age, in contrast to the prediction for these landscapes. The Cape Floristic Region was the only OCBIL that showed a significant, although not strong, overlap with old communities. [ABSTRACT FROM AUTHOR]

In Delta state, Nigeria, where food production is rain-fed, climatic risks have compromised rural livelihoods. Information about future weather conditions can help farmers adapt more efficiently. Data obtained using group discussions and semi-structured interviews revealed that farmers in Delta state rely on indigenous knowledge systems, despite occasional inconsistencies. Most farmers' trust in seasonal climate forecasts has dwindled because they suffered losses when previously relying on the seasonal forecasts in a planting season. With climatic risks expected to increase by 2030, there is a need to rebuild farmers' trust in seasonal climate forecasts, using a proposed multiple evidence-based approach. [ABSTRACT FROM AUTHOR]

Alcohol use among HIV-positive individuals in sub-Saharan Africa directly impacts adherence to antiretroviral therapy and HIV outcomes. Few studies have examined approaches to reduce alcohol use among HIV-affected couples, despite evidence that alcohol use is a couple-level concern. We conducted a qualitative study with 23 alcohol-using couples to identify multilevel barriers and facilitators of alcohol use, and potential intervention options with couples. Data were analyzed at individual and dyadic levels using framework analysis. All couples were married and had at least one partner on ART. Men were the primary alcohol drinkers with few women reporting alcohol use. Most women tried to persuade their partners to reduce their alcohol intake and when unsuccessful, enlisted help from relatives and HIV care providers. Effective couple negotiation around men's alcohol use was constrained by negative peer influence and men's desire for friendship to cope with life stressors. Women were primarily concerned about the expense of alcohol and described how alcohol prevented the family from meeting basic needs and investing in the future. Alcohol use was described as a major barrier to ART adherence, but was also viewed as the cause of couple and family violence, extramarital partnerships, food insecurity, and poverty. We conclude that multilevel interventions based on couples' needs and preferences are urgently needed. Couple-based intervention approaches could include provider-led alcohol counseling with couples, alcohol reduction support groups for couples, couples' counseling to bolster couple communication and problem-solving around alcohol, and economic-strengthening interventions for couples. [ABSTRACT FROM AUTHOR]

Background: The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces.Methods: We undertook a systematic review (PROSPERO International Prospective Register of Systematic Reviews; ID = CRD42019116834) to identify empirical data on clinical malaria in Africa since 2000, where reports covered at least 5 continuous years. The trends in empirical data were then compared with the trends of time-space matched clinical malaria incidence from MAP using the Spearman rank correlation. The correlations (rho) between changes in empirically observed and modelled estimates of clinical malaria were displayed by forest plots and examined by meta-regression.Results: Sixty-seven articles met our inclusion criteria representing 124 sites from 24 African countries. The single most important factor explaining the correlation between empirical observations and modelled predictions was the slope of empirically observed data over time (rho = - 0.989; 95% CI - 0.998, - 0.939; p < 0.001), i.e. steeper declines were associated with a stronger correlation between empirical observations and modelled predictions. Factors such as quality of study, reported measure of malaria and endemicity were only slightly predictive of such correlations.Conclusions: In many locations, both local surveillance data and modelled estimates showed declines in malaria burden and hence similar trends. However, there was a weak association between individual surveillance datasets and the modelled predictions where stalling in progress or resurgence of malaria burden was empirically observed. Surveillance data were patchy, indicating a need for improved surveillance to strengthen both empiric reporting and modelled predictions. [ABSTRACT FROM AUTHOR]

Background: Postpartum hemorrhage (PPH) is the leading cause of maternal deaths worldwide. This study sought to quantify the potential health impact (morbidity and mortality reductions) that a low-cost uterine balloon tamponade (UBT) could have on women suffering from uncontrolled PPH due to uterine atony in sub-Saharan Africa.Methods: The Maternal and Neonatal Directed Assessment of Technology (MANDATE) model was used to estimate maternal deaths, surgeries averted, and cases of severe anemia prevented through UBT use among women with PPH who receive a uterotonic drug but fail this therapy in a health facility. Estimates were generated for the year 2018. The main outcome measures were lives saved, surgeries averted, and severe anemia prevented.Results: The base case model estimated that widespread use of a low-cost UBT in clinics and hospitals could save 6547 lives (an 11% reduction in maternal deaths), avert 10,823 surgeries, and prevent 634 severe anemia cases in sub-Saharan Africa annually.Conclusions: A low-cost UBT has a strong potential to save lives and reduce morbidity. It can also potentially reduce costly downstream interventions for women who give birth in a health care facility. This technology may be especially useful for meeting global targets for reducing maternal mortality as identified in Sustainable Development Goal 3. [ABSTRACT FROM AUTHOR]

Sub-Saharan Africa (SSA) lagged furthest behind in achieving targets for the millennium development goals (MDG). We investigate the hypothesis that its slow progress is influenced by political factors. Longitudinal data on three health MDG indicators: under-five mortality, maternal mortality and HIV prevalence rates were collated from 1990 to 2012 in 48 countries. Countries were grouped into geo-political and eco-political groups. Groupings were based on conflict trends in geographical regions and the International Monetary Fund’s classification of SSA countries based on gross national income and development assistance respectively. Cumulative progress in each group was derived and main effects tested using ANOVA. Correlation analysis was conducted between political variables – POLITY 2, fragile state index (FSI), voter turnout rates, civil liberty scores (CLS) and the health variables. Our results suggest a significant main effect of eco-political and geo-political groups on some of the health variables. Political conflict as measured by FSI and political participation as measured by CLS were stronger predictors of slow progress in reducing under-five mortality rates and maternal mortality ratios. Our findings highlight the need for further research on political determinants of mortality in SSA. Cohesive effort should focus on strengthening countries’ political, economic and social capacities in order to achieve sustainable goals beyond 2015. [ABSTRACT FROM PUBLISHER]