Paper Info
Title
An Adaptive and Dynamic Biosensor Epidemic Model for COVID-19
Abstract
The impact of the COVID-19 global pandemic has required governments across the world to develop effective public health policies using epidemiological models. Unfortunately, as a result of limited testing ability, these models often rely on lagged rather than real-time data, and cannot be adapted to small geographies to provide localized forecasts. This study proposes ADBio, a multi-level adaptive and dynamic biosensor-based model that can be used to predict the risk of infection with COVID-19 from the individual level to the county level, providing more timely and accurate estimates of virus exposure at all levels. The model is evaluated using diagnosis simulation based on current COVID-19 cases as well as GPS movement data for Massachusetts and New York, where COVID-19 hotspots had previously been observed. Results demonstrate that lagged testing data is indeed a major detriment to current modeling efforts, and that unlike the standard SEIR model, ADBio is able to adapt to arbitrarily small geographic regions and provide reasonable forecasts of COVID-19 cases. The features of this model enable greater national pandemic preparedness and provide local town and county governments a valuable tool for decision-making during a pandemic.
Year
DOI
Venue
2020
10.1109/IRI49571.2020.00051
2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)
Keywords
DocType
ISBN
dynamic biosensor epidemic model,COVID-19 global pandemic,public health policies,epidemiological models,testing ability,ADBio,multilevel adaptive,GPS movement data,standard SEIR model,county governments,localized forecasting,diagnosis simulation,decision-making,Massachusetts,New York
Conference
978-1-7281-1055-4
Citations 
PageRank 
References 
0
0.34
7
Authors
4
Name
Order
Citations
PageRank
Salvador V. Balkus100.34
Joshua Rumbut201.35
Honggang Wang31365124.06
Hua Fang434332.48