Difference between revisions of "Covid 19"

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(Epidemiological models)
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|Model uses dataset acquired by tutu.ru for movement statistics between nearly 170 000 Russian cities, villages and other inhabited localities. It investigates effects of restrictions on transition between cities on Covid19 spread. For each city simple SIR model is used.
 
|Model uses dataset acquired by tutu.ru for movement statistics between nearly 170 000 Russian cities, villages and other inhabited localities. It investigates effects of restrictions on transition between cities on Covid19 spread. For each city simple SIR model is used.
 
|Due to highly uneven population density (high in the western part and low in the eastern) it is possible to change epidemic timing by restricting movement between main transport hubs which will probably be economically justified in the future.
 
|Due to highly uneven population density (high in the western part and low in the eastern) it is possible to change epidemic timing by restricting movement between main transport hubs which will probably be economically justified in the future.
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|Chang et al.<ref>Chang S.L., Harding N., Zachreson C., Cliff O.M., Prokopenko M. Modelling transmission and control of the COVID-19 pandemic in Australia // arxiv preprint 2020. {{doi|arxiv-2003.10218}}</ref>
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|02.04.2020
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|Agent-based
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Revision as of 13:20, 17 April 2020

Epidemiological models

Authors Publication date Geographical region Type Description Predictions
Chen et al[1] 28.01.2020 Wuhan, China SEIR (several species) Model described virus transmission between bets (possible source), unknown host and human. For every species SEIR model is implemented. Simplified model with only human species is also considered. The value of R0 was estimated of 2.30 from reservoir to person and 3.58 from person to person which means that the expected number of secondary infections that result from introducing a single infected individual into an otherwise susceptible population was 3.58.
Danon et al.[2] 14.02.2020 England and Whales SEIR+Spatial Does not take into account mortality. CoVID-19 outbreak will peak 126 to 147 days (~4 months) after the start of person-to-person transmission in England and Wales in the absence of controls, epidemic peak is predicted in June.
Westerhoff and Kolodkin[3] 30.03.2020 Not specified modified SEIR Model distinguished between tested and non-testes subjects and takes into consideration adaptive government-induces social distancing policy Strategies aiming for herd immunity are unacceptable and that a much stronger lockdown is required. Results suggest that the measures taken by many policy makers will be insufficient to quench the epidemic. Some Western policy makers engage in an adaptive lock down strategy but one of insufficient strength: model results suggest that their slowly increasing lock down strategy will not be effective. What is necessary is a strong lock down, which may then be softened as the number of infected individuals begins to decrease with time.
Tutu.ru[4] 30.03.2020 Russian Federation SIR + Spatial Model uses dataset acquired by tutu.ru for movement statistics between nearly 170 000 Russian cities, villages and other inhabited localities. It investigates effects of restrictions on transition between cities on Covid19 spread. For each city simple SIR model is used. Due to highly uneven population density (high in the western part and low in the eastern) it is possible to change epidemic timing by restricting movement between main transport hubs which will probably be economically justified in the future.
Chang et al.[5] 02.04.2020 Australia Agent-based

References

  1. Chen T-M.,Rui J.,Wang Q-P.,Zhao Z., Cui J., Yin L. A mathematical model for simulating the phase-based transmissibility of a novel coronavirus // Infect Dis Poverty 9:24 (2020). doi:https://doi.org/10.1186/s40249-020-00640-3
  2. Danon L., Brooks-Pollock E., Bailey M., Keeling M. A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing // medRxiv preprint 2020. doi:https://doi.org/10.1101/2020.02.12.20022566
  3. Westerhoff H. V., Kolodkin A.N. Advice from a systems-biology model of the Corona epidemics// medRxiv preprint 2020. doi:https://doi.org/10.1101/2020.03.29.20045039
  4. ttps://github.com/ods-ai-ml4sg/covid19-tutu
  5. Chang S.L., Harding N., Zachreson C., Cliff O.M., Prokopenko M. Modelling transmission and control of the COVID-19 pandemic in Australia // arxiv preprint 2020. doi:arxiv-2003.10218
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