Epidemic Analysis of Covid-19 using LSTM

The COVID-19 pandemic has had a profound global impact, particularly on countries that serve as major international travel and tourism hubs. Controlling the rapid transmission of this highly contagious disease requires early detection and localized monitoring at regional levels. Prompt identification of potential outbreaks is critical for enabling both governmental and local health authorities to implement effective containment and mitigation strategies. Most of the existing applications designed to track COVID-19 spread primarily focus on retrospective data analysis and lack mechanisms for real-time prevention or region-specific adaptability. The proposed system seeks to address these limitations by providing instantaneous, data-driven insights and recommending preventive actions. Recurrent waves of the pandemic have repeatedly revealed the inadequacies in current surveillance frameworks, underscoring the necessity for more responsive and adaptive systems. Using updated COVID-19 datasets, this study conducts a detailed analysis to enhance situational awareness for potential travelers. The results are presented through a visual and interactive interface, allowing users to easily interpret regional health conditions. At the system’s interface, users can select specific states or union territories to access relevant information and risk assessments. Moreover, the system suggests precautionary measures aimed at reducing the probability of infection. This initiative thus serves as a proactive decision-support tool for individuals, enabling them to evaluate epidemiological conditions prior to travel and aiding authorities in promoting safer mobility.