Visualizing Scholarly Trends in Stochastic Models for Disease Prediction.

Area of Science:

• Epidemiology and Biostatistics
• Computational Biology
• Public Health

Background:

• Stochastic models are crucial for understanding randomness in biological systems and disease dynamics.
• Accurate disease prediction is vital for effective public health interventions and resource allocation.
• Bibliometric analysis provides insights into research trends and impact.

Purpose of the Study:

• To visualize and analyze the trends in the application of stochastic models for disease prediction from 1990 to 2024.
• To identify key drivers of research growth and emerging research gaps.
• To highlight the potential of integrating advanced computational techniques with stochastic modeling.

Main Methods:

• Bibliometric analysis of publications indexed in Scopus database.
• Trend analysis of research output and citation patterns related to stochastic models in disease prediction.
• Qualitative assessment of research focus and identified gaps.

Main Results:

• Significant increase in research on stochastic models for disease prediction observed after 2014.
• Global health crises, notably COVID-19, have accelerated research in this field.
• Identified gaps in the application of these models to non-communicable diseases and low-resource settings.

Conclusions:

• Stochastic models are a growing area of research for disease prediction, with substantial potential for future development.
• Integration with machine learning and artificial intelligence can enhance predictive capabilities.
• Further interdisciplinary and international collaboration is needed to address current limitations and improve global health outcomes.