Mind the scales: Harnessing spatial big data for infectious disease surveillance and inference

TL;DR

This paper reviews how spatial big data from digital sources can enhance infectious disease surveillance, discusses challenges, and explores implications for public health strategies and policy.

Contribution

It provides a comprehensive overview of the use, challenges, and potential of spatial big data in infectious disease epidemiology.

Findings

Spatial big data improves disease monitoring and prediction.

Technical, practical, and ethical challenges are significant.

Implications for health communication and policy are profound.

Abstract

Spatial big data have the "velocity," "volume," and "variety" of big data sources and additional geographic information about the record. Digital data sources, such as medical claims, mobile phone call data records, and geo-tagged tweets, have entered infectious disease epidemiology as novel sources of data to complement traditional infectious disease surveillance. In this work, we provide examples of how spatial big data have been used thus far in epidemiological analyses and describe opportunities for these sources to improve public health coordination and disease mitigation strategies. In addition, we consider the technical, practical, and ethical challenges with the use of spatial big data in infectious disease surveillance and inference. Finally, we discuss the implications of the rising use of spatial big data in epidemiology to health risk communications, across-scale public health coordination, and public health policy recommendation.