1. Overcoming Challenges for Estimating Virus Spread Dynamics from Data
- Author
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Henrik Sandberg, Damir Vrabac, Philip E. Pare, and Karl Henrik Johansson
- Subjects
Discrete time and continuous time ,Computer science ,Homogeneous ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Quantization (signal processing) ,Network structure ,Model parameters ,Data mining ,Time series ,computer.software_genre ,Missing data ,computer - Abstract
In this paper we investigate estimating the parameters of a discrete time networked virus spread model from time series data. We explore the effect of multiple challenges on the estimation process including system noise, missing data, time-varying network structure, and quantization of the measurements. We also demonstrate how well a heterogeneous model can be captured by homogeneous model parameters. We further illustrate these challenges by employing recent data collected from the ongoing 2019 novel coronavirus (2019-nCoV) outbreak, motivating future work.
- Published
- 2020
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