Our solution is an Early Warning System for Health based on participatory data gathering. A common, real-time framework for disease collection will help countries identify and forecast outbreaks faster and more effectively.

Crowdsourced data is gathered directly from citizens, then aggregated, anonymized, and processed in a cloud-based data lake. Our high-performance computing architecture analyzes the data and creates valuable disease spread models, which in turn provide alerts and notifications to participating countries and helps public health authorities make evidence-based decisions.


Digital Participatory Surveillance

Putting citizens first


Disease Forecasting Models

Anticipating outbreaks


Decision-Making Dashboard

Supporting decision-making

01. Digital Participatory Surveillance

Digital Participatory Surveillance (DPS) is a proven methodology for knowledge sharing and gathering health-related information directly from citizens. It relies on citizens regularly self-reporting their health status and symptoms through online surveys, which can be calibrated to a range of illnesses. There are several active DPS networks in operation such as Influenzanet and Flu Near You.

DPS can:

  • Identify outbreak trends earlier than official sources.

  • Collect data from healthy patients, including household information (children; elderly).

  • Collect information about animal health, which is essential considering that most outbreaks are attributable to zoonoses.

  • Offer detailed profile data, allowing individual-level epidemiological analyses generally not possible in standard systems.

  • Provide a common standardized framework for data comparison across countries.

  • Deliver results without requiring a large user base (although it needs recurrent users).

  • Correct potential data biases during analysis.

DPS has been in use in Europe, North America, and Australia for >10 years:

02. Disease Forecasting Models

We combine the unique, high-quality data gathered through DPS (combined with other publicly-available data sets) to identify outbreaks and forecast their spread. Spatio-temporal CAR/Bayesian and transmission-dynamic mathematical models allow us to capture both broad and detailed information on the outbreak spread at different scales.

03. Decision-making Dashboard

Public health authorities need the right information in the right format at the right time to contain the spread of an infectious diseases outbreak. We will provide National Health Institutes participating in AfyaNet with actionable information and tools to explore data on the status of both ongoing and forecasted outbreaks.