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. Author manuscript; available in PMC: 2021 Apr 2.
Published in final edited form as: Annu Rev Public Health. 2020 Jan 6;41:101–118. doi: 10.1146/annurev-publhealth-040119-094402

Table 1:

Glossary of terms

Public health surveillance “Systematic and continuous collection, analysis, and interpretation of data, closely integrated with the timely and coherent dissemination of results and assessment to those who have the right to know so that action can be taken”*
Digital public health surveillance Public health surveillance with the inclusion of digital data, particularly from social media or other internet-based sources.
Nowcast Short-term forecasting meant to provide near real-time information.
Cloud-based Adjective describing data that is stored, processed, or analyzed on-demand via remote servers hosted made available through the Internet.
Machine learning Algorithmic approaches that adapt to patterns in data without explicitly programming the prediction task.†
Supervised machine learning Machine learning algorithms where the outcome variable for prediction is explicitly observed and focus is on accurate predictions of that outcome.†
Search Query-based digital surveillance Digital public health surveillance systems that use aggregate search query data to monitor disease trends. Examples include Google Flu Trends and Google Dengue Trends.
Social Media-based digital surveillance Digital public health surveillance systems that use social media posts to monitor disease trends. Social media-based surveillance requires pre-processing of the data, such as keyword searches or natural language processing.
Crowd-based digital surveillance Digital public health surveillance systems where participants voluntarily provide health-relevant information through potentially repeated web-based surveys to monitor disease trends. Examples include Flu Near You and Influenzanet.
Hybrid digital surveillance Integration of digital surveillance data with traditional public health surveillance data, or multiple sources of digital along with traditional public health surveillance data to monitor disease trends.
Google Flu Trends Publicly available site that used aggregate Google search trend data to forecast influenza-like illness incidence. After failure to predict the 2009 pandemic and overestimating the peak intensity of 2012–2013 influenza season, the publicly available site was removed.‡
Flu Near You Crowd-sourced participatory surveillance system to track influenza-like illness in the United States via weekly surveys completed by participants.#
FluSight The United States Centers for Disease Control and Prevention’s “Forecast the Influenza Season Collaborative Challenge”. An annual competition in which researchers compete to develop the most accurate weekly influenza-like illness predictions with some form of digital data.**
*

Porta M. 2008. A Dictionary of Epidemiology. Oxford University Press. 320 pp

†

Mooney SJ, Pejaver V. 2018. Big Data in Public Health: Terminology, Machine Learning, and Privacy. Annu. Rev. Public Health 39:95–112

‡

Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. 2009. Detecting influenza epidemics using search engine query data. Nature 457:1012–4

#

Smolinski MS, Crawley AW, Baltrusaitis K, Chunara R, Olsen JM, et al. 2015. Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons. Am. J. Public Health 105:2124–30

**

Biggerstaff M, Alper D, Dredze M, Fox S, Fung IC-H, et al. 2016. Results from the Centers for Disease Control and Prevention’s predict the 2013–2014 Influenza Season Challenge. BMC Infect. Dis. 16:357