Traditional methods to infer compartmental epidemic models with time-varying dynamics can only capture continuous changes in the dynamic.However, many changes are discontinuous due to sudden interventions, such as city lockdown and opening of field hospitals . To model the discontinuities, this study introduces the tool of total variationregularization , which regulates the temporal changes of the dynamic parameters, such as the transmission rate. To recover the ground truth dynamic, this study designs a novel yet straightforward optimization algorithm, dubbed iterative Nelder-Mead , which repeatedly applies the Nelder-Mead algorithm. Experiments  on the simulated data show that the proposed approach can qualitatively reproduce the discontinuities of the underlying dynamics. To extend this research to real data as well as to help researchers worldwide to fight against COVID-19, the author releases his research platform as an open-source package .
Compartmental epidemic models are among the most popular ones in epidemiology. For the parameters (e.g., the transmission rate) characterizing these models, the majority of researchers simplify them as constants, while some others manage to detect their continuous variations. In this paper, we aim at capturing, on the other hand, discontinuous variations, which better describe the impact of many noteworthy events, such as city lockdowns, the opening of field hospitals , and the mutation of the virus, whose effect should be instant. To achieve this, we balance the model's likelihood by total variation , which regulates the temporal variations of the model parameters. To infer these parameters, instead of using Monte Carlo methods, we design a novel yet straightforward optimization algorithm, dubbed Iterated Nelder--Mead , which repeatedly applies the Nelder--Mead algorithm. Experiments conducted on the simulated data demonstrate that our approach can reproduce these discontinuities and precisely depict the epidemics .
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Traditional methods to infer compartmental epidemic models with time-varying dynamics can only capture continuous changes in the dynamic.However, many changes are discontinuous due to sudden interventions,
Compartmental epidemic models are among the most popular ones in epidemiology. For the parameters (e.g., the transmission rate) characterizing these models, the majority of researchers simplify them as constants, while some others manage to detect their continuous variations. In this paper, we aim at capturing, on the other hand, discontinuous variations, which better describe the impact of many noteworthy events,
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lockdown and
lockdowns, the
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. To model the discontinuities, this study introduces the tool of total variationregularization
, and the mutation of the virus, whose effect should be instant. To achieve this, we balance the model's likelihood by total variation
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changes of the dynamic parameters, such as the transmission rate. To recover the ground truth dynamic, this study designs
variations of the model parameters. To infer these parameters, instead of using Monte Carlo methods, we design
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iterative Nelder-Mead
Iterated Nelder--Mead
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Nelder-Mead
Nelder--Mead
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conducted
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show that the proposed approach can qualitatively reproduce the discontinuities of the underlying dynamics. To extend this research to real data as well as to help researchers worldwide to fight against COVID-19, the author releases his research platform as an open-source package
demonstrate that our approach can reproduce these discontinuities and precisely depict the epidemics