Dataset Information

Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021).

ABSTRACT: Energy system modeling can be used to develop internally-consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modeling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modeling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

SUBMITTER: Allington L

PROVIDER: S-EPMC8943422 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021).

Allington Lucy L Cannone Carla C Pappis Ioannis I Cervantes Barron Karla K Usher Will W Pye Steve S Brown Edward E Howells Mark M Zachau Walker Miriam M Ahsan Aniq A Charbonnier Flora F Halloran Claire C Hirmer Stephanie S Cronin Jennifer J Taliotis Constantinos C Sundin Caroline C Sridharan Vignesh V Ramos Eunice E Brinkerink Maarten M Deane Paul P Gritsevskyi Andrii A Moura Gustavo G Rouget Arnaud A Wogan David D Barcelona Edito E Niet Taco T Rogner Holger H Bock Franziska F Quirós-Tortós Jairo J Angulo-Paniagua Jam J Krishnamurthy Satheesh S Harrison John J To Long Seng LS

Data in brief 20220309

Energy system modeling can be used to develop internally-consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used ...[more]

PMID: 35341031

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Project description:Coronavirus (COVID-19) has continued to be a global threat to public health. As the matter of fact, it needs unreserved effort to monitor the prevalence of the virus. However, applying an effective prediction of the prevalence is thought to be the fundamental requirement to effectively control the spreading rate. Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease. This study, therefore, aimed to apply the Autoregressive Integrated Moving Average (ARIMA) modeling approach for projecting coronavirus (COVID-19) prevalence patterns in East Africa Countries, mainly Ethiopia, Djibouti, Sudan and Somalia. The data for the study were obtained from the reports of confirmed COVID-19 cases by the official website of Johns Hopkins University from 13th March, 2020 to 30th June, 2020.The results of the study, then, showed that in the coming four month, the number of COVID-19 positive people in Ethiopia may reach up to 56,610 from 5,846 on June 30, 2020 in average-rate scenario. However, in worst case scenario forecast, the model showed that the cases will be around 84,497. The analysis further depicted that with average interventions and control scenario, cumulative number of infected persons in Djibouti, Somalia and Sudan will increase from 4,656, 2,904 and 9,258 respectively at the end of June to 8,336, 3,961 and 21,388, which is by the end of October, 2020, after four-months. But, with insufficient intervention, the number of infected persons may grow quickly and reach up to 14,072, 10,037 and 38,174 in Djibouti, Somalia and Sudan respectively. Generally, the extent of the coronavirus spreading was increased from time to time in the past four month, until 30 th June, 2020, and it is expected to continue quicker than before for the coming 4-month, until the end of October, 2020, in Ethiopia, Djibouti, Somalia, and Sudan and more rapidly than before in Sudan and Ethiopia, while the peak will remain unknown yet. Therefore, an effective implementation of the preventive measures and a rigorous compliance by avoiding negligence with the rules such as prohibiting public gatherings, travel restrictions, personal protection measures, and social distancing may alleviate the spreading rates of the virus, particularly, Sudan and Ethiopia. Moreover, more efforts should be exerted on Ethiopian side to control the population movement across all the border areas and to strengthen border quarantining. Further, through updating more new data with continuous reconsideration of predictive model, provide useful and more precise prediction. Applying, ARIMAX-Transfer Function model in region-wise by take in to consideration of climatic data like temperature and humidity in each countries looking spatial pattern for reliable measure of COVID-19 prevalence.

Project description:BackgroundGlobally, millions of people have been affected by fraudulent pharmaceutical products, particularly those in developing countries. Although the problem of falsified and substandard drugs is acknowledged, the extent of the issue is ever-changing, has a dynamic nature, and should be quantified and captured in a recent snapshot.ObjectiveThis systematic review seeks to examine the data that can quantify and provide a current snapshot of the prevalence of SF antimicrobials in selected east Africa countries.MethodsScientific studies on antimicrobial quality were searched in PubMed, Embase, Scopus, and Google Scholar from 2017 to February 2023. The search strategy focused on scientific articles published in peer-reviewed scientific journals written in English and the studies exclusively done in any of the selected countries of east Africa. The articles were carefully reviewed by two individuals for inclusion independently, first by title followed by abstract and the full-text retrieval. To minimize bias associated with the methodology used for data collection, the quality of the studies was assessed for quality according to the Medicine Quality Assessment Reporting Guidelines (MEDQUARG). The reporting of this systematic review was done following Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA).ResultsFifteen studies that estimated the prevalence of poor-quality antimicrobial medicines in selected four east African countries were included. The overall percentage of samples of antimicrobials that failed at least one quality test was 22.6% (151/669) with each class's prevalence of 17% in antibiotics (73/432), 24% in antimalarial (41/171), and 56% in anthelmintics (37/66). Quality control parameters of API content were the most commonly examined in the included studies, accounting for 14/15 (93%) studies. Fifty (33.1%) of the failing samples failed assay API- content determination, while 26.5% (n = 40) failed the visual inspection and packaging analysis; 19.2% (29) failed dissolution; 14% (n = 21) flawed hardness or friability; 4%(n = 6) failed uniformity, as well as 3.2% (n = 5) failed disintegration test of the quality control parameter.ConclusionIt was found that this review was general in these selected east African countries and was a catalyst for combating the menace of poor-quality medications that affect millions of lives.

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Dataset Information

Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021).

Publications

Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021).

Similar Datasets

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