Global disease monitoring and forecasting with Wikipedia

TL;DR

This paper explores using Wikipedia access logs as a cost-effective, timely data source for monitoring and forecasting infectious diseases globally, demonstrating promising results with simple models.

Contribution

It introduces a novel approach leveraging Wikipedia data for disease monitoring, addressing challenges of breadth, peer review, and forecasting in social internet data-based methods.

Findings

Models achieved up to 0.92 in R-squared.

Forecasting was effective up to 28 days ahead.

Transferability of models between locations is feasible.

Abstract

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data such as social media and search queries are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with $r^2$ up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.