This research aims to 1) investigate and compare the performance of seven database schema designs, and 2) evaluate the impact of data storage strategies on the performance of web dashboards for large-scale infectious disease data. The experiment utilized the PostgreSQL database management system, and the web application was developed using Next.js in conjunction with Prisma. The evaluation was conducted under identical conditions utilizing the same dataset, frontend codebase, and testing procedures across three main web pages with 30 repeated iterations per method. Performance was assessed using Google Lighthouse. Statistical analysis via One-way ANOVA revealed that Method F (Denormalized without Index and Partition) achieved the highest average Performance Score of 84.40 on the overview page, differing significantly from the other methods (p < 0.001). This was followed by Method G (Denormalized with Index without Partition) at 82.60; while Method A (Normalized with Index and Partition); and Method E (Materialized View) both scored 80.33. Meanwhile, the scores for Accessibility, Best Practices, and Search Engine Optimization (SEO) remained similar across all methods. The results indicate that the database schema design significantly affects dashboard performance. These experimental findings, specific to the dataset context and environment studied, can serve as a preliminary guideline for designing large-scale infectious disease surveillance systems in the future.