DengAI: Predicting Disease Spread

Introduction

DengAI: Predicting Disease Spread is a practice competition organized by DrivenData, focusing on the problem of predicting the weekly number of dengue cases in San Juan, Puerto Rico, and Iquitos, Peru. The problem uses environmental data collected by U.S. federal agencies, related to temperature, precipitation, vegetation, and other variables.

The competition is of intermediate difficulty and is designed for learning and exploration purposes. The problem highlights the relationship between climate and dengue transmission dynamics, thereby serving research and the allocation of medical resources.

Target Audience

• Participants interested in data science, machine learning, and predictive analytics.
• Students looking to practice with an intermediate-level problem.

Benefits

• Participate in a practice competition to hone skills in prediction using environmental data.
• Opportunity to work with datasets related to public health and epidemiology.
• According to the competition page, there have been 17,531 participants.

Requirements

• Predict the number of dengue cases reported each week in San Juan, Puerto Rico, and Iquitos, Peru.
• Use environmental data to build predictive models for each location.
• The competition is marked as intermediate level.

How to Register

• Visit the DrivenData competition page for detailed information.
• Competition Page

Important Milestones

• The competition is noted to be open for another year.
• Specific registration deadlines are not explicitly stated in the extracted content.