Application of an Improved Epidemic Forest Model in Preventing and Controlling Communicable Diseases: A case study of COVID-19 Epidemic in Nanchang, China

Authors: Meifang Li*, Dartmouth College
Topics: Medical and Health Geography, Spatial Analysis & Modeling, Geographic Information Science and Systems
Keywords: Spatiotemporal model, Epidemic Forest, COVID-19, China, Big data
Session Type: Virtual Paper
Presentation File: No File Uploaded

As Coronavirus Disease 2019 (COVID-19) spreading around the world and the whole world fighting against it, geography information technology plays an important role in controlling and preventing the spreading. Epidemic forest model, proposed in recent years, is a novel method to spatiotemporally modelling the transmission relationships between individuals, which is very helpful and useful to track the disease spreading when the individual information is detailed and available. In this study, using the COVID-19 epidemic in Nanchang, China as an example, we proposed an improved epidemic forest model, which is a comprehensive research framework of applying the epidemic forest model to preventing and controlling the spread of a communicable disease in a real world. The framework includes three general steps: 1) data collection (we proposed a method to extract and model individual trajectory from aggregated and available data), 2) building epidemic forest with epidemic forest model (Based on the individual trajectory generated from step 1, we modeled the transmission relationships between individuals with epidemic forest model), and 3) analyzing the built epidemic forest (Based on the built individual transmission relationships, we explored and analyzed the pattern of it, and further guided us to prevent and control its spread). With this improved epidemic forest model, we have tracked each of all local patients in Nanchang for his/her infectors, and quarantined many close contacts before they had symptoms. We hope this method can be applied to other areas and other communicable diseases.

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