CASE-CF: Context Aware Smart Epidemic Control Framework.

Area of Science:

• Artificial Intelligence
• Epidemiology
• Public Health

Background:

• The COVID-19 pandemic significantly impacted global economies, necessitating careful decisions regarding the reopening of public spaces.
• Delays in reopening due to health risks led to severe economic downturns.
• Existing reopening strategies often lack real-time, region-specific data integration.

Purpose of the Study:

• To propose a novel context-aware framework for determining the optimal timing for reopening public spaces.
• To leverage deep neural network models for generating region-specific reopening recommendations.
• To develop a versatile framework applicable to various pandemic scenarios and geographical scales.

Main Methods:

• Utilized a series of deep neural network models for data analysis and recommendation generation.
• Integrated multiple, real-time, and government-accessible data inputs specific to a region.
• Tested the framework using open-source COVID-19 data from 22 districts in Haryana, India.

Main Results:

• The proposed framework demonstrated efficiency in generating context-specific reopening recommendations.
• Experimental results validated the framework's capability to utilize readily available government data.
• The system proved effective in a real-world pandemic scenario.

Conclusions:

• The developed framework offers a data-driven approach to balance public health and economic considerations during pandemics.
• It provides a scalable solution for regional decision-making in pandemic management.
• The framework's adaptability makes it a valuable tool for future public health crises.