Network-based modeling for risk assessment of infectious disease transmission

Abstract

Infectious disease modeling is crucial to optimize surveillance, preventative measures, and resource allocation. Simulation with infectious disease models is very convenient when the resource requirement for data collection and experimental studies are prohibitively high or even unethical. A vast number of approaches have been proposed to model infectious disease transmission from different perspectives. In this dissertation, we investigate network-based disease models for efficient resource allocation, effective mitigation measures, and accurate risk assessment. We also investigate a filtering-based parameter estimation and forecasting framework, usable when proper incidence data is available. First, we provide a guideline for developing a network-based model and simulation framework for any infectious diseases. As an example, we provide a step-by-step method for developing a spatially explicit model for infectious diseases with host demographic data. We show how to devise effective mitigation strategies from simulation results using the spatially explicit model. Our second contribution is developing a parameter estimation framework using a sequential Monte Carlo filter, a compartmental disease model, and historical incidence data. Parameter estimation for any infectious disease model is crucial for accurately informing resource allocation and control measures. Our method is particularly important for its adaptability to the availability of new incidence data of any epidemic. This parameter estimation framework is not limited to epidemic models; rather, it can be used for any systems with a state-space model. Third, we propose an ensemble Kalman filter that provides dual state-parameter estimates for infectious diseases. As an online inferential method, the ensemble Kalman Filter can perform real-time forecast during an outbreak. The framework is capable of accurate short to mid-term forecasts. Fourth, we develop a risk assessment framework for infectious diseases with a comprehensive two-layer network— a permanent layer representing permanent contacts among individuals, and a data-driven layer for temporary contacts due to movements. We combine the two-layer network with a compartmental model and implement a Gillespie algorithm to identify the disease evolution and assess the spatial spreading risk. The proposed risk assessment framework suggests some focal points (spatial) for disease preparedness, providing critical directions to inform interventions in the field. Finally, we investigate the strong correlation of the arthropod abundance and host interaction with vector-borne pathogen transmission, and we developed a risk assessment framework using climate (average temperature and rainfall) and host demographic (host density and movement) data, particularly suitable for regions with unreported or under-reported incidence data. This framework consisted of a spatiotemporal network-based approach coupled with a compartmental disease model and a non-homogeneous Gillespie algorithm. We have identified the spatiotemporal suitability map, the spatial risk map, the significant-incidence window, and peak incidence period. The outcomes of the framework comprise of weather-dependent spatiotemporal suitability maps and probabilistic risk maps for spatial infection transmission. This framework is capable of vector-borne disease risk assessment without historical incidence data and can be a useful tool for preparedness with accurate human movement data.