Title

An Adaptive and Dynamic Biosensor Epidemic Model for COVID-19

UMMS Affiliation

Department of Population and Quantitative Health Sciences

Publication Date

2020-08-11

Document Type

Conference Proceeding

Disciplines

Biomedical Devices and Instrumentation | Epidemiology | Infectious Disease | Statistics and Probability | Virus Diseases

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.

Keywords

COVID-19, Biological system modeling, Data models, wearable biosensors, Adaptation models, Viruses (medical), Temperature sensors

DOI of Published Version

10.1109/IRI49571.2020.00051

Source

S. V. Balkus, J. Rumbut, H. Wang and H. Fang, "An Adaptive and Dynamic Biosensor Epidemic Model for COVID-19," 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, NV, USA, 2020, pp. 306-313, doi: 10.1109/IRI49571.2020.00051.

Journal/Book/Conference Title

2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)

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