Visualizing disease severity and patient counts: Multi-view analysis for informed decision-making and public health awareness

In the age where data reigns paramount and health is crucial, making use of the power of visualization is not just an option, but a must. In our globally wired world, exposing the insights hidden in health data to the public's attention through visualization helps people make wise decisions, spurs innovation, improves healthcare provision, and ultimately saves lives. Data visualization facilitates preventative steps to prevent health emergencies by assisting with future planning and preparation. Yet it is challenging to determine severity appropriately without inciting panic or negligence since current techniques might not accurately reflect the intricate details of the case. To overcome the difficulty, this thesis proposes a novel approach that involves: (i) using a data-driven Severity Score, which provides an unbiased, objective evaluation of severity. (ii) Introducing a New Patient Count feature to monitor the rate at which illnesses spread and provide a significant understanding of epidemic trends. By the visual presentation of these aspects from various angles, the technique helps decision-makers make well-informed choices instead of impulsive ones. With the use of many graphical representations, our approach makes it easier for audiences with varying technical backgrounds to understand the health situation from multiple perspectives. This reduces the dangers associated with impulsive decisions by giving a broader audience comprehensive knowledge about the situation and allowing stakeholders to make informed decisions. To obtain the intended result, data is taken from a JSON file. MySQL is utilized in the analysis of data. After that, HTML, CSS, and JavaScript are used for visualization, with the addition of the Chart.js and D3.js libraries. Mock data is used in this work.