An Efficient Supply Chain Data Warehousing Model For Big Data Analytics

This research work is aimed at developing a supply chain data warehousing model for big data analytics that will be used for reporting and analysis purposes. Objected-Oriented Design methodology was adopted for the study. A big data supply chain dataset of a retail outlet from a real world business transaction was used for data analysis. Google storage bucket was created in Google BigQuery for storage and analysis of the data.  Data was uploaded into Google storage in the cloud, after which the supply chain data table was created using SQL query.  Star Schema dimensional model was created for integrating data into the cloud.  For descriptive and diagnostic analytics including feature engineering, the integrated datasets, advanced feature engineering techniques were applied to create derived variables that enhanced the model interpretability and predictive power.  Google big Query was linked to Google collab for big data analytics, after which a preliminary analysis was conducted in Google collab showing the first row of the dataset.  There was then a decomposition of the time series analysis into trend, seasonality, residuals and original.  To perform predictive analytics, the processed dataset was split into training and test datasets to prevent over-fitting.  To optimize the model performance, the hyperparameters were adjusted.  The forecasting model was implemented within the dashboard using ARIMA and Prophet time series forecasting methods  in training the models; and Random forest regression machine learning model in order to implement the most important features that drives sales as well as demand.  MAPE and RMSE were used as model evaluation metrics for the predictive analytics of the proposed model.  After cross validation of the performance metrics, the study revealed that incorporating advanced Prophet, ARIMA and Random Forest models enhanced the predictive capabilities of the proposed system, leading to more precise inventory management.  In conclusion, the proposed system offers better improvements with respect to reliability, performance, scalability, and recoverability because it is designed to handle complex, large scale data operations which are very crucial in modern business environments. The proposed supply chain data warehousing model for big data analytics is highly recommended for supply chain management/managers in inventory management, as the model will help in optimizing the inventory levels as well as improving the supply chain business. Keywords: Supply Chain, Data Warehousing, Big Data, Analytic

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