PathoMap

A weekly updated pathogen tracking platform that empowers users to monitor outbreaks, assess local risk, and stay protected through accurate, data-driven insights.

The spread and mortality rate of COVID illustrate the dangerous impact infectious diseases can have. With over 704 million cases and 7 million deaths, transparency and awareness are a must.

The spread and mortality rate of COVID-19 illustrate the devastating impact infectious diseases can have on global health. With over 704 million cases and 7 million deaths worldwide, the pandemic revealed how essential transparency, awareness, and rapid information access are in preventing further outbreaks. PathoMap was created to provide that visibility — helping communities stay informed and proactive against emerging infectious threats.

PathoMap tracks and visualizes real-time data on key infectious diseases — influenza, measles, and Mpox — by collecting and processing information from verified public health APIs. Users can:

Click on a region (e.g., Santa Clara County) to see current cases and disease trends.

View detailed disease profiles including symptoms, spread, prevention, duration, and fatality rate.

Receive email or in-app notifications when a sudden increase in reported cases occurs in their area.

Self-report symptoms anonymously to contribute to broader community surveillance and early detection.

We used public health data APIs from organizations such as the CDC and WHO to collect verified disease reports. The backend was built to process this data, calculate infection intensity by location, and assign a local “infection score.” The frontend integrates an interactive map interface where users can explore outbreaks visually. A notification and mailing system was implemented to alert subscribers of significant changes in disease activity. A self-report form was developed to crowdsource potential case information for enhanced accuracy.

Limited access to real-time, town-level disease data for specific illnesses.

Data standardization — different APIs use varying formats and reporting intervals.

Balancing data privacy with meaningful self-reporting contributions.

Implementing a responsive alert system that detects unusual spikes without false positives.

Successfully integrated multiple public health datasets into a unified, real-time visualization tool.

Created an interactive and intuitive map interface that displays outbreak severity at a glance.

Developed a functional alert system that delivers timely notifications of potential disease surges.

Designed a self-report mechanism to enhance public participation in disease surveillance.

Global and local disease reporting networks vary widely in update frequency and data resolution.

Reliable disease tracking requires both institutional data and community input.

Effective public health communication depends on presenting complex data in a clear, actionable format.

Privacy and transparency can coexist through anonymous data contribution systems.

Expand to include additional diseases such as COVID-19, RSV, and dengue.

Partner with local health departments to improve data granularity and validation.

Incorporate predictive modeling using historical trends to forecast outbreaks.

Develop a mobile app version for easier access and real-time notifications.

Integrate AI anomaly detection to identify early signs of emerging pathogens.