1 S Sudarshan, 2 V Benny, 3 M Manvith kumar, 4 G Mohan Krishna, 5 S Dinesh 03 09 The Disease Case Count Trend Tracker is a datadriven monitoring and visualization system designed to collect analyze and display disease case information over different periods Monitoring disease cases is important for understanding how the number of reported cases changes across time and geographical areas When case information is maintained in separate records or manually prepared reports identifying trends and changes can become difficult The proposed system provides a centralized platform for organizing diseaserelated casecount information The system focuses on measurable indicators such as total reported cases new cases recovered cases active cases deaths disease categories geographical distribution and timebased trends depending on the available dataset The collected information is processed and grouped according to date location and disease type This allows users to observe increases decreases and recurring patterns in reported disease cases The dashboard presents the processed information using interactive charts graphs maps tables and summary cards Users can select a particular disease date range or geographical area to examine the corresponding case trends Historical comparisons can help users understand how reported cases have changed between different periods The visual presentation makes large datasets easier to interpret than manually examining individual records The system is intended primarily as an information and monitoring tool rather than a diagnostic or treatment system It provides summarized casecount information that can support publichealth reporting research planning and administrative analysis Appropriate data validation and access controls can be applied to maintain the quality and privacy of the information used by the system Overall the Disease Case Count Trend Tracker provides a centralized approach to diseasecase monitoring and trend analysis By converting collected records into understandable visual insights the system can reduce manual reporting effort and improve awareness of changing case patterns Future versions can incorporate automated data updates geographical visualization anomaly detection forecasting and integration with authorized healthinformation sources Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026