Biosurveillance

Signals and systems

• Laboratory, clinical, syndromic, veterinary, environmental, and wastewater information
• Official reports and structured surveillance systems
• Context about baselines, geography, population, sampling, and reporting delays

Signals are not conclusions

A change may reflect a real event, reporting artifact, behavior, sampling, seasonality, or another cause. Interpret it with domain experts and corroborating evidence.

Operational value

Good biosurveillance supports proportionate questions and timely verification; it should not manufacture certainty or amplify rumors.

A signal chain, not a single dashboard

• Collection: who or what is represented, and who is missing?
• Processing: which definitions, laboratory methods, codes, and quality controls transform the observation?
• Analysis: which baseline, denominator, geography, lag, and threshold are used?
• Interpretation: which alternative explanations and corroborating streams were considered?
• Communication: what does the signal support, and what remains unknown?

Common sources of bias

Care-seeking behavior, test access, reporting participation, coding changes, sample coverage, laboratory methods, seasonality, population movement, delayed records, and system outages can create or hide apparent change.

Evidence and consequence are separate

A weak signal may justify quiet verification when consequence could be high; a strong signal may still have limited local relevance. Record confidence, potential consequence, action, authority, and the trigger for reassessment separately.

Governance matters

Define data stewardship, access, retention, quality review, analytic ownership, escalation, correction, public communication, and the responsible authority before an urgent signal appears.

FAQ

Common questions