Covid 19
From BioUML platform
Revision as of 13:25, 17 April 2020 by Ilya Kiselev (Talk | contribs)
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 | Effectiveness of school closures is limited, producing a two-week delay in epidemic peak, without a significant impact on the magnitude of the peak, in terms of incidence or prevalence. The temporal benefit of the two-week delay may be offset not only by logistical complications, but also by some increases in the fractions of both children and older adults during the period around the incidence peak. Social distancing (SD) strategy showed no benefit for lower levels of compliance (at 70% or less). Increasing a compliance level just by 10%,from 70% to 80%, may effectively control the spread of COVID-19 in Australia (during the suppression period). |
References
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ ttps://github.com/ods-ai-ml4sg/covid19-tutu
- ↑ 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
