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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/4542| Dynamic Model Predictions of Covid-19 Pandemic Mitigation Scenarios | |
| Jenny, Patrick. Jenny, David. Gorji, Hossein. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| http://infoscience.epfl.ch/record/276369/files/Corona%20Growth.pdf | |
| Relevant pandemic-spread scenario simulations provide guiding principles for containment and mitigation policy developments. In this report we discuss model predictions for infected, recovered and dead people during a pandemic. The model consists of a simple reaction mechanism, which considers the population size, reported and unreported infections, reported and unreported recoveries and number of deaths. It is assumed that recovered persons are immune (e.g. mutations are not considered), that the virus can only be passed on by unreported infected persons and that no vaccine will be found within the considered periods. Moreover, the model does not take into account age dependency, but considers higher mortality rates due to temporary shortage of emergency rooms. Some of the model parameters have been fitted to the reported cases outside of China1 from January 22 to March 12 of the 2020 Covid-19 pandemic. The other parameters were chosen in a plausible range and sensitivity studies were performed; among all tested parameter values, those leading to the lowest number of deaths were chosen for the presented case studies, i.e., the predictions are rather optimistic. Scenarios in which the infection rate per person is reduced for periods of 680 and 183 days (”social distancing phase”) and scenarios in which the testing frequency is increased for 680 days (”frequent testing phase”) are studied. While the predicted number of deaths can dramatically be reduced during the ”social distancing” and ”frequent testing” phases, the fatality numbers increase again some time after these phases end; for all these scenarios the predicted final number of deaths lies between 3% and 5% of the initial population (not much different from the base case). It is interesting, however, that more frequent testing seems to be much more effective than social distancing and may cause less economic damage. The currently low detection rate is obviously linked to the long incubation period typical for Covid-19. Incubation for typical flu is much shorter, and as soon people fall ill and stay at home, they are effectively detected and do not further spread the virus. | |
| http://infoscience.epfl.ch/record/276369 | |
| 2020 | |
| Artículo | |
| http://infoscience.epfl.ch/record/276369/files/Corona%20Growth.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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