Enhancing disease surveillance with novel data streams: challenges and opportunities

Abstract

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

“…However, it has been suggested that the results of models using internet search queries need to be further validated by more advanced studies to control the relevant covariates (such as media basis, socio-economic and demographic factors) [50]. …”

Design Strategies for Efficient Arbovirus Surveillance

“…The method we present can also be used to redesign existing surveillance systems by manually including or excluding providers during optimization. Additionally, the method is well suited to integrating diverse data streams, such as climatic, mosquito vector, pharmacy, or digital data ( 9 ). …”

Evaluation of Nowcasting for Detecting and Predicting Local Influenza Epidemics, Sweden, 2009–2014

“…In the setting of our study, the performance was most likely decreased by the assumption that telenursing precedes influenza diagnosis by 14 days is applicable to all situations. Althouse et al reported that methods underpinning the use of big data sources (e.g., search query logs) need regular upkeep to maintain their accuracy ( 12 ). In future versions of nowcasting methods, regular updates and syndromic sources that are more stable than telenursing data (regarding time lag to influenza diagnosis data) may become available and can be used to improve the peak-timing predictions.…”