<p dir="ltr">Cardiac disease is one of the prominent reasons of deaths worldwide. The timely de-<br>tection of arrhythmias, one of the highly prevalent cardiac abnormalities, is very important<br>and promising for treatment. Electrocardiography (ECG) is well applied to probe the car-<br>diac dynamics, nevertheless, it is still challenging to robustly detect the arrhythmia with<br>automatic algorithms, especially when the noise may contaminate the signal to some extent.<br>In this research study, we have not only built and assessed different neural network models<br>to understand their capability in terms of ECE-based arrhythmia detection, but also com-<br>prehensively investigated the detection under different kinds of signal-to-noise ratio (SNR).<br>Both Long Short-Term Memory (LSTM) model and Multi-Layer Perception (MLP) model<br>have been developed in the study. Further, we have studied the necessity of fine-tuning<br>of the neural network models, which are pre-trained on other data and demonstrated that<br>it is very important to boost the performance when ECG is contaminated by noise. In<br>the experiments, the LSTM model achieves an accuracy of 99.0%, F1 score of 97.9%, and<br>high precision and recall, with the clean ECE signal. Further, in the high SNR scenario,<br>the LSTM maintains an attractive performance. With the low SNR scenario, though there<br>is some performance drop, the fine-tuning approach helps performance improvement criti-<br>cally. Overall, this study has built the neural network models, and investigated different<br>kinds of signal fidelity including clean, high-SNR, and low-SNR, towards robust arrhythmia<br>detection.</p>

<p dir="ltr">Understanding disease transmission is a complex and challenging task as it encompasses a wide range of intricate interactions involving pathogens, hosts, and the environment. Numerous factors, including genetics, behavior, immunity, social dynamics, and environmental conditions, contribute to the complexity. Furthermore, diseases exhibit significant variability in transmission patterns, including variations in the mode of transmission (e.g., respiratory, oral, touch-based, vector-borne), incubation period, and infectiousness. The dynamic nature of disease transmission compounds the existing challenges by introducing temporal variability and environmental variations, thereby intensifying the complexity of the study. Therefore, understanding disease transmission requires comprehensive research, integrated models, and a multidisciplinary approach to decipher the intricate web of interactions and factors involved. This dissertation aims to bridge the use and scalability gap between different levels of transmission models through the utilization of multi-paradigm modeling methods, incorporating varying levels of abstraction, to gain comprehensive insights into disease transmission. The first goal focuses on enhancing pandemic resiliency by analyzing the impact of varying parameters of heating, ventilation, and air conditioning (HVAC) on the dynamics of exhaled droplets and aerosols in the indoor environment using computational fluid dynamics (CFD) modeling. This goal operates at a micro-level of modeling, examining the detailed fluid dynamics and particle dispersion within indoor spaces. By simulating the movement of droplets under different HVAC configurations, this goal provides insights into the effectiveness of ventilation systems and optimizes parameter configurations in controlling disease transmission. The second goal of this dissertation is to aid organizations in evaluating potential policies to mitigate contact-caused risks in indoor spaces during a pandemic. This goal utilizes an ensemble of agent-based simulation (ABS) models, which operate at a higher level of abstraction. These models consider the behaviors and interactions of individuals within indoor environments, such as classrooms or meeting rooms, while incorporating physical distancing guidelines and seating policies. The third goal aims to improve pandemic prediction capabilities by developing a multivariate, spatiotemporal, deep-learning model that predicts COVID-19 hospitalization based on historical cases and evaluates the impact of state-level policy changes. This goal operates at the highest level of abstraction by utilizing deep learning techniques to analyze large-scale, publicly available data. The model captures temporal dependencies using long short-term memory (LSTM) networks and spatial dependencies using graph convolutional networks (GCN), graph attention networks (GAT), and graph transformer networks (GTN). By considering variables such as daily hospitalization and various policy changes, this approach provides a comprehensive framework for forecasting hospitalization cases and assessing policy impacts at the state level. This integrated, abstraction-based approach provides a more holistic understanding of disease transmission, allowing for the exploration of complex scenarios and the assessment of intervention impacts across different scales. This integrated architecture enables policymakers and public health professionals to develop targeted, effective strategies to mitigate the spread of diseases, allocate resources efficiently, and minimize the overall impact on public health. </p>