Affiliation:
1. Universidad Nacional de Tres de Febrero , Caseros , Argentina
Abstract
Abstract
Objectives
To introduce a novel way of measuring the spreading speed of an epidemic.
Methods
We propose to use the mean time between infections (MTBI) metric obtained from a recently introduced nonhomogeneous Markov stochastic model. Different types of parameter calibration are performed. We estimate the MTBI using data from different time windows and from the whole stage history and compare the results. In order to detect waves and stages in the input data, a preprocessing filtering technique is applied.
Results
The results of applying this indicator to the COVID-19 reported data of infections from Argentina, Germany and the United States are shown. We find that the MTBI behaves similarly with respect to the different data inputs, whereas the model parameters completely change their behaviour. Evolution over time of the parameters and the MTBI indicator is also shown.
Conclusions
We show evidence to support the claim that the MTBI is a rather good indicator in order to measure the spreading speed of an epidemic, having similar values whatever the input data size.
Funder
Universidad Nacional de Tres de Febrero
Subject
Applied Mathematics,Epidemiology
Reference32 articles.
1. Ali, I., and S. U. Khan. 2020. “Analysis of Stochastic Delayed SIRS Model with Exponential Birth and Saturated Incidence Rate.” Chaos, Solitons & Fractals 138: 110008. https://doi.org/10.1016/j.chaos.2020.110008.
2. Al-Ani, B. G. 2021. “Statistical Modeling of the Novel COVID-19 Epidemic in Iraq.” Epidemiologic Methods 10 (s1): 20200025. https://doi.org/10.1515/em-2020-0025.
3. Barraza, N. R., G. Pena, and V. Moreno. 2020. “A Non-homogeneous Markov Early Epidemic Growth Dynamics Model. Application to the SARS-CoV-2 Pandemic.” Chaos, Solitons & Fractals 139: 110297. https://doi.org/10.1016/j.chaos.2020.110297.
4. Cao, Z., Y. Shi, X. Wen, H. Su, and X. Li. 2020. “Dynamic Behaviors of a Two-Group Stochastic SIRS Epidemic Model with Standard Incidence Rates.” Physica A: Statistical Mechanics and its Applications 554: 124628. https://doi.org/10.1016/j.physa.2020.124628.
5. Capobianco, G., R. Cobiaga, W. Reartes, and F. Turpaud. 2021. “The SIR Model in the COVID-19 Pandemic.” In VIII Congreso de Matemática Aplicada, Computacional e Industrial, 715–8. La Plata, Argentina: ASAMACI. https://asamaci.org.ar/wp-content/uploads/2021/07/MACI-Vol-8-2021.pdf.