Researchers of the Faculty of Maritime Studies and Transport of the University of Ljubljana are involved in researching the influence of the coronavirus pandemic on transport.
An economically oriented approach has led to mathematical modelling of the course of the pandemic. Two models have been developed, the logistics and the classic SIR model. The two Matlab programs were named fitVirus and fitVirusCOVID19. Initially, they were almost exclusively based on data from China. It was shown that the prognosis of the course of infections significantly overlapped with the actual course of the pandemic. Since then, the models have been applied to other countries and regions around the world. The models have proven to be very accurate especially for countries that have introduced a strict quarantine. In fact, the model assumes in particular a permanent population, even degree of mixing of people, and an equal probability of recovery among the infected. The model is based on data, so its forecast may be as good as the input data. With new or changed data, the forecast will also change. The model has been developed for educational purposes only.
References:
M. Batista, Estimation of the final size of the coronavirus epidemic by the logistic model (Feb 2020)
M. Batista, Estimation of the final size of the coronavirus epidemic by the SIR model. (Feb 2020),
M. Batista, Estimation of the final size of the COVID-19 epidemic,
M. Batista, Estimation of the final size of the second phase of the coronavirus epidemic by the logistic model,
M. Batista, Estimation of a state of Corona 19 epidemic in August 2020 by multistage logistic model: a case of EU, USA, and World (Update September 2020),
M. Batista, On the Reproduction Number in Epidemics, Journal of Biological Dynamics, Volume 15, Issue 1, 22 November 2021) Pages 623–634.
Software:
SIR model:
Compartmental models in epidemiology
The Mathematics of Infectious Diseases
epiEstim model:
Anne Cori, Neil M. Ferguson, Christophe Fraser, Simon Cauchemez , A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics, American Journal of Epidemiology, Volume 178, Issue 9, 1 November 2013, Pages 1505–1512
R.N. Thompson, J.E. Stockwin, R.D. van Gaalen, J.A. Polonsky, Z.N. Kamvar, P.A. Demarsh, E. Dahlqwist, S. Li, E. Miguel, T. Jombart, J. Lessler, S. Cauchemez, A. Cori, Improved inference of time-varying reproduction numbers during infectious disease outbreaks, Epidemics, Volume 29, 2019
Serial interval:
Hiroshi Nishiura, Natalie M Linton, Andrei R. Akhmetzhanov, Serial interval of novel coronavirus (2019-nCoV) infections
Zhanwei Du et al., Serial Interval of COVID-19 among Publicly Reported Confirmed Cases
Sheikh Taslim Ali et al. , Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions
Data:
Additional information:
Milan Batista: On the reproduction number in epidemics, Journal of Biological Dynamics, Volume 15, Issue 1, Pages 623-634
Other opinions:
Institute of Global Health, Faculty of Medicine, University of Geneva
ECDC covid19 country overviews
Johns Hopkins University of Medicine