Inverse problem solver for epidemiological geographic profiling

Yoshiharu Maeno ()
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Yoshiharu Maeno: Meiji University

Evolutionary and Institutional Economics Review, 2024, vol. 21, issue 2, No 7, 237-248

Abstract: Abstract This work studies machine-learning-based inverse problem solvers for a reaction–diffusion process. The study focus is on the performance of a state-of-the-art convolutional neural network in discovering the source of disease spreading. This problem is called epidemiological geographic profiling. The performance is investigated with synthetic datasets for SIR epidemiological compartments on a square grid geo-space. The convolutional neural network works effectively in discovering a single source and achieves the largest time average of accuracy for growing infection in a heterogeneous geo-space. The hit score remains near the lower bound over time. Discovering multiple sources is feasible potentially as well by learning the dataset for a single source.

Keywords: Convolutional neural network; Geographic profiling; Infectious disease; Inverse problem; Reaction–diffusion process (search for similar items in EconPapers)
JEL-codes: C13 C18 I10 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s40844-024-00281-3

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