Demystifying the spreading of pandemics I: The fractal kinetics SI model quantifies the dynamics of COVID-19

Macheras, Panos; Kosmidis, Kosmas

doi:10.1101/2020.11.15.20232132

Cited by 1 publication

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“…In our previous studies 7 , 32 on COVID-19 data analysis, we applied the fractal kinetic SI model ( Eq. 3 ) assuming that fractal kinetics commences at time zero.…”

Section: Resultsmentioning

confidence: 99%

Interpreting airborne pandemics spreading using fractal kinetics’ principles

Macheras

Tsekouras

2021

F1000Res

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Introduction The reaction between susceptible and infected subjects has been studied under the well-mixed hypothesis for almost a century. Here, we present a consistent analysis for a not well-mixed system using fractal kinetics’ principles. Methods We analyzed COVID-19 data to get insights on the disease spreading in absence/presence of preventive measures. We derived a three-parameter model and show that the “fractal” exponent h of time larger than unity can capture the impact of preventive measures affecting population mobility. Results The h=1 case, which is a power of time model, accurately describes the situation without such measures in line with a herd immunity policy. The pandemic spread in four model countries (France, Greece, Italy and Spain) for the first 10 months has gone through four stages: stages 1 and 3 with limited to no measures, stages 2 and 4 with varying lockdown conditions. For each stage and country two or three model parameters have been determined using appropriate fitting procedures. The fractal kinetics model was found to be more akin to real life. Conclusion Model predictions and their implications lead to the conclusion that the fractal kinetics model can be used as a prototype for the analysis of all contagious airborne pandemics.

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“…In our previous studies 7 , 32 on COVID-19 data analysis, we applied the fractal kinetic SI model ( Eq. 3 ) assuming that fractal kinetics commences at time zero.…”

Section: Resultsmentioning

confidence: 99%