Abstract
In this chapter, we explore the integration of artificial intelligence (AI) in enhancing environmental monitoring and managing the COVID-19 pandemic in Morocco. Utilizing AI-based event detection techniques, the authors extend their investigation from analyzing COVID-19 trends to applying predictive algorithms for environmental surveillance. This approach not only demonstrates AI's capacity in public health realms but also underscores its potential in environmental stewardship. Through detailed analysis, the authors illustrate how machine learning and data analytics significantly improve response strategies against pandemic threats and environmental degradation. Their findings reveal AI's critical role in developing resilient and sustainable monitoring systems, offering valuable insights for policymakers, researchers, and practitioners. This comprehensive study highlights the synergy between technological innovation and multifaceted crisis management, positioning AI as a cornerstone in advancing smart technologies for the betterment of public health and environmental sustainability in Morocco.
TopLiterature Review
Overview of Scholarly Work on COVID-19
The advent of COVID-19 has prompted an unprecedented surge in research, with the scholarly community producing a robust corpus of literature to understand and combat this global health emergency. A meticulous review of the Scopus database reveals a diversification of document types, with journal articles representing the largest share, as shown in Figure 1. This pattern underscores a rapid scholarly response, albeit with a reduced presence of conference papers, likely due to the pandemic-induced cancellations of many scientific gatherings (Scopus & Eltoukhy, 2020).
Figure 1. Distribution of published document types on COVID-19
Figure 2. Domain-based classifications of COVID-19 publications
The breadth of research on COVID-19, while anchored primarily in medical science, spans across disciplines, reflecting the virus's multifaceted impact. As depicted in Figure 2, a significant portion of research is dedicated to medicine, but substantial inquiries also emerge from biochemistry, social sciences, and engineering, signaling the interdisciplinary nature of pandemic-related studies.
Key Terms in this Chapter
AI-Based Event Detection: Method using AI to automatically identify significant incidents or anomalies in environmental or public health data.
SIR Model (Susceptible-Infected-Recovered): Mathematical model used to predict the spread of infectious diseases by categorizing the population into three states: susceptible, infected, and recovered.
Environmental Surveillance: The process of monitoring and analyzing environmental factors to prevent and control public health risks.
Machine Learning: A branch of AI that allows machines to learn from data and improve their predictions or decisions without being explicitly programmed for each task.
Trend Analysis: Data analysis methodology used to identify patterns or trends in data sets over time, aiding evidence-based decision-making.
Artificial Intelligence (AI): Technology that mimics human intelligence in machines, enabling the automation of complex tasks and enhancing data-driven decision-making.