Abstract
Building an effective and highly usable epidemiology model presents two main challenges: finding the appropriate, realistic enough model that takes into account complex biological, social and environmental parameters and efficiently estimating the parameter values with which the model can accurately match the available outbreak data, provide useful projections. The reproduction number of the novel coronavirus (SARS-CoV-2) has been found to vary over time, potentially being influenced by a multitude of factors such as varying control strategies, changes in public awareness and reaction or, as a recent study suggests, sensitivity to temperature or humidity changes. To take into consideration these constantly evolving factors, the paper introduces a time dynamic, humidity-dependent SEIR-type extended epidemiological model with range-defined parameters. Using primarily the historical data of the outbreak from Northern and Southern Italy and with the help of stochastic global optimization algorithms, we are able to determine a model parameter estimation that provides a high-quality fit to the data. The time-dependent contact rate showed a quick drop to a value slightly below 2. Applying the model for the COVID-19 outbreak in the northern region of Italy, we obtained parameters that suggest a slower shrinkage of the contact rate to a value slightly above 4. These findings indicate that model fitting and validation, even on a limited amount of available data, can provide useful insights and projections, uncover aspects that upon improvement might help mitigate the disease spreading.
Highlights
In November 2019 the virus named Severe Acute Respiratory Syndrome (SARS)-CoV-2 appeared in Wuhan, the capital city of Hubei province, a metropolis with 11 million inhabitants
On January 22 2020 an outbreak took place, with a massive infection count, that was later declared as a pandemic by the World Health Organization (WHO)
We propose an iterated Particle Swarm Optimization (Kennedy & Eberhart, 1995) method (IPSO) where in each iteration the method explores only the vicinity of the best parameter estimation found previously
Summary
In November 2019 the virus named SARS-CoV-2 appeared in Wuhan, the capital city of Hubei province, a metropolis with 11 million inhabitants. On January 22 2020 an outbreak took place, with a massive infection count, that was later declared as a pandemic by the World Health Organization (WHO). Physicists, epidemiologists, and mathematicians are trying to model the evolution of the currently raging outbreak, considering various models from basic propagation ones like. How to cite this article Farkas C, Iclanzan D, Olteán-Péter B, Vekov G. Estimation of parameters for a humidity-dependent compartmental model of the COVID-19 outbreak. SIR, IDEA to more sophisticated models like SEIR, SEIRS, statistical mechanics of open systems and the extended versions of the above
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