[Interactive, semi-automatized and open source computational model applied to respiratory viruses surveillance]

Area of Science:

• Public Health
• Infectious Disease Epidemiology
• Bioinformatics

Background:

• Acute respiratory infections (ARI) pose a significant global health burden, particularly for children and the elderly.
• Effective surveillance of respiratory viruses is crucial for reducing healthcare costs and optimizing public health responses.
• Existing surveillance systems often face challenges with data management, analysis, and timely dissemination.

Purpose of the Study:

• To develop an interactive and user-friendly web platform for the efficient gathering, analysis, and publication of respiratory virus surveillance data.
• To reduce the time and cost associated with data upload and analysis in viral surveillance networks.
• To enhance the accessibility and impact of surveillance data for public health decision-making.

Main Methods:

• Development of a web-based platform utilizing the R programming language and its Shiny package.
• Integration of historical data (since 2006) from the Chilean metropolitan respiratory viruses surveillance network.
• Implementation of features for rapid data upload, interactive analysis, and immediate online publication.

Main Results:

• The platform allows researchers to upload data in under one minute.
• Data analysis and publication are immediate and accessible to internet-connected users.
• The system significantly decreases data processing time and increases the availability and impact of surveillance information.
• The platform was developed with very low cost and in a short timeframe.

Conclusions:

• The developed R and Shiny platform offers a cost-effective and efficient solution for respiratory virus surveillance.
• The interactive and accessible nature of the platform enhances the utility of surveillance data for public health.
• This approach can be instrumental in optimizing health responses to acute respiratory infections globally.