The daily fluctuations in the released number of Covid-19 cases played a big role at the beginning of the pandemic, when local authorities in Italy had to decide whether imposing restrictive policies. When an increase/decrease was communicated, especially a large one, it was difficult to understand if it was due to a change in the epidemic evolution or if it was a fluctuation due to other reasons, such as an increase in the number of swabs or a delay in the swab processing. The aim of this paper is both to model the main trend of the outbreak evolution in the number of confirmed cases and to describe the daily fluctuations strongly dependent on the daily number of swabs. For our analysis, we propose a nonlinear asymmetric diffusion model, which includes information on the daily number of swabs, to describe daily fluctuations in the number of confirmed cases in addition to the main trend of the outbreak evolution. The proposed model is found to be the more efficient for prediction, as compared to 6 already existing models, including the SIRD and the logistic models. The new model combines the properties of innovation diffusion models with a parsimonious way to exploit information about swabs.

The Effect of Swabs on Modeling the First Wave of the COVID-19 Pandemic in Italy

Furlan, Claudia
;
Mortarino, Cinzia
2021

Abstract

The daily fluctuations in the released number of Covid-19 cases played a big role at the beginning of the pandemic, when local authorities in Italy had to decide whether imposing restrictive policies. When an increase/decrease was communicated, especially a large one, it was difficult to understand if it was due to a change in the epidemic evolution or if it was a fluctuation due to other reasons, such as an increase in the number of swabs or a delay in the swab processing. The aim of this paper is both to model the main trend of the outbreak evolution in the number of confirmed cases and to describe the daily fluctuations strongly dependent on the daily number of swabs. For our analysis, we propose a nonlinear asymmetric diffusion model, which includes information on the daily number of swabs, to describe daily fluctuations in the number of confirmed cases in addition to the main trend of the outbreak evolution. The proposed model is found to be the more efficient for prediction, as compared to 6 already existing models, including the SIRD and the logistic models. The new model combines the properties of innovation diffusion models with a parsimonious way to exploit information about swabs.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3391304
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