Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies.

In infectious disease outbreak modeling, there remains a gap in addressing spatiotemporal challenges present in established models. This study addresses this gap by evaluating four established hybrid neural network models for predicting influenza outbreaks. These models were analyzed by employing time series data from eight different countries to challenge the models with imposed spatial difficulties, in a month-on-month structure.

Greenhouse gas emission, GDP, tertiary education, and rule of law: A comparative study between high-income and lower-middle income countries.

Global climate change is a pressing concern, particularly in underdeveloped countries. Because greenhouse gases are a key cause of climate change and economic growth is tied to emissions. The study aimed to determine how the Gross Domestic Product (GDP), Tertiary Education, and Rule of Law could be utilized more effectively to reduce greenhouse gas emissions.