Analysing and interpreting data for disease surveillance is a systematic process of collecting, organizing, examining, and evaluating health-related information to identify disease patterns, detect outbreaks, monitor trends, and guide public health action. It converts raw health data into meaningful evidence for decision-making, enabling timely interventions and effective pandemic preparedness and response. The first step in disease surveillance is data collection from reliable sources such as hospitals, primary health centres, laboratories, clinics, community health workers, pharmacies, death registration systems, and digital reporting platforms. Data may include demographic characteristics, symptoms, laboratory results, vaccination status, hospitalization records, mortality data, travel history, and environmental factors. Ensuring completeness, accuracy, timeliness, and consistency of data is essential before analysis. Data management involves cleaning and validating the collected information. Errors such as duplicate records, missing values, inconsistent coding, and incorrect entries are identified and corrected. Standard case definitions and uniform reporting formats improve data quality and facilitate comparison across regions and time periods. Data analysis focuses on identifying trends and patterns using epidemiological methods. Descriptive analysis examines disease occurrence according to person, place, and time. Person-based analysis considers age, sex, occupation, and other demographic variables. Place-based analysis identifies geographical distribution through maps and geographic information systems (GIS). Time-based analysis studies seasonal variations, epidemic curves, and long-term trends to detect unusual increases in disease incidence. Measures such as incidence rate, prevalence rate, attack