What are common data cleaning steps before outbreak data analysis?

• A. Check for missing data, correct coding, remove duplicates, standardize formats, handle inconsistent dates, and verify entries against source documents.
• B. Skip cleaning to save time.
• C. Only fix missing data.
• D. Delete all outliers.

Answer: (D)

Cleaning outbreak data starts with ensuring data quality so analyses reflect real patterns, not errors. The common steps are checking for missing data to understand gaps and decide on imputation or exclusion, correcting incorrect or inconsistent coding so categories and labels align across sources, removing duplicates to avoid double counting, standardizing formats for dates and units so data from multiple systems can be combined reliably, and verifying entries against source documents or original data systems to catch transcription mistakes. These actions improve accuracy, traceability, and the reliability of trend analyses and outbreak signals. Outliers shouldn’t be deleted automatically; each one should be examined to determine if it’s a data entry error, a measurement issue, or a true extreme event, and then handled appropriately—through correction, transformation, robust methods, or documented exclusion if justified. Skipping cleaning, or cleaning too narrowly, risks biased results and distorted conclusions.