ArticleJournal of the American Medical Informatics Association : JAMIA
Finding leading indicators for disease outbreaks: filtering, cross-correlation, and caveats.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Recommendations for the analysis of leading indicators in epidemic surveillance.PLOS digital health · 2026Review
- Trends Analysis of Sexually Transmitted Infections Before and After Human Immunodeficiency Virus Pre-exposure Prophylaxis in the United States 2001-2022.Open forum infectious diseases · 2025Article
- Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework.JMIR public health and surveillance · 2025Article
- Sustaining Antimicrobial Stewardship in a High-Antibiotic Resistance Setting.JAMA network open · 2022Article
- Evaluating the Increased Burden of Cardiorespiratory Illness Visits to Adult Emergency Departments During Flu and Bronchiolitis Outbreaks in the Pediatric Population: Retrospective Multicentric Time Series Analysis.JMIR public health and surveillance · 2022Article
- Making waves: Defining the lead time of wastewater-based epidemiology for COVID-19.Water research · 2021Review
- Which Environmental Factor Is Correlated with Long-Term Multiple Sclerosis Incidence Trends: Ultraviolet B Radiation or Geomagnetic Disturbances?Multiple sclerosis international · 2017Article
- The accuracy and timeliness of neuraminidase inhibitor dispensing data for predicting laboratory-confirmed influenza.Epidemiology and infection · 2016Article
- Correlation of multiple sclerosis (MS) incidence trends with solar and geomagnetic indices: Time to revise the method of reporting MS epidemiological data.Iranian journal of neurology · 2014Article
- Guess who's not coming to dinner? Evaluating online restaurant reservations for disease surveillance.Journal of medical Internet research · 2014Article
- Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Periodic climate cooling enhanced natural disasters and wars in China during AD 10-1900.Proceedings. Biological sciences · 2010Article
- Linkages between animal and human health sentinel data.BMC veterinary research · 2009Article
- Surveillance Sans Frontières: Internet-based emerging infectious disease intelligence and the HealthMap project.PLoS medicine · 2008Article
- Monitoring the impact of influenza by age: emergency department fever and respiratory complaint surveillance in New York City.PLoS medicine · 2007Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bioterrorism and emerging infectious diseases such as influenza have spurred research into rapid outbreak detection. One primary thrust of this research has been to identify data sources that provide early indication of a disease outbreak by being leading indicators relative to other established data sources. Researchers tend to rely on the sample cross-correlation function (CCF) to quantify the association between two data sources. There has been, however, little consideration by medical informatics researchers of the influence of methodological choices on the ability of the CCF to identify a lead-lag relationship between time series. We draw on experience from the econometric and environmental health communities, and we use simulation to demonstrate that the sample CCF is highly prone to bias. Specifically, long-scale phenomena tend to overwhelm the CCF, obscuring phenomena at shorter wave lengths. Researchers seeking lead-lag relationships in surveillance data must therefore stipulate the scale length of the features of interest (e.g., short-scale spikes versus long-scale seasonal fluctuations) and then filter the data appropriately--to diminish the influence of other features, which may mask the features of interest. Otherwise, conclusions drawn from the sample CCF of bi-variate time-series data will inevitably be ambiguous and often altogether misleading.
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