Predicting Infectious Diseases: A Bibliometric Review on Africa.

Africa has a long history of novel and re-emerging infectious disease outbreaks. This reality has attracted the attention of researchers interested in the general research theme of predicting infectious diseases. However, a knowledge mapping analysis of literature to reveal the research trends, gaps, and hotspots in predicting Africa's infectious diseases using bibliometric tools has not been conducted. A bibliometric analysis of 247 published papers on predicting infectious diseases in Africa, published in the Web of Science core collection databases, is presented in this study. The results indicate that the severe outbreaks of infectious diseases in Africa have increased scientific publications during the past decade. The results also reveal that African researchers are highly underrepresented in these publications and that the United States of America (USA) is the most productive and collaborative country. The relevant hotspots in this research field include malaria, models, classification, associations, COVID-19, and cost-effectiveness. Furthermore, weather-based prediction using meteorological factors is an emerging theme, and very few studies have used the fourth industrial revolution (4IR) technologies. Therefore, there is a need to explore 4IR predicting tools such as machine learning and consider integrated approaches that are pivotal to developing robust prediction systems for infectious diseases, especially in Africa. This review paper provides a useful resource for researchers, practitioners, and research funding agencies interested in the research theme-the prediction of infectious diseases in Africa-by capturing the current research hotspots and trends.

Chemical Contamination Pathways and the Food Safety Implications along the Various Stages of Food Production: A Review.

Historically, chemicals exceeding maximum allowable exposure levels have been disastrous to underdeveloped countries. The global food industry is primarily affected by toxic chemical substances because of natural and anthropogenic factors. Food safety is therefore threatened due to contamination by chemicals throughout the various stages of food production. Persistent Organic Pollutants (POPs) in the form of pesticides and other chemical substances such as Polychlorinated Biphenyls (PCBs) have a widely documented negative impact due to their long-lasting effect on the environment. This present review focuses on the chemical contamination pathways along the various stages of food production until the food reaches the consumer. The contamination of food can stem from various sources such as the agricultural sector and pollution from industrialized regions through the air, water, and soil. Therefore, it is imperative to control the application of chemicals during food packaging, the application of pesticides, and antibiotics in the food industry to prevent undesired residues on foodstuffs. Ultimately, the protection of consumers from food-related chemical toxicity depends on stringent efforts from regulatory authorities both in developed and underdeveloped nations.

A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring.

In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical 'batch' processing-extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework.