Difference between revisions of "Covid 19"
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54. [https://science.sciencemag.org/content/sci/early/2020/07/10/science.abc6027.full.pdf Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients] | 54. [https://science.sciencemag.org/content/sci/early/2020/07/10/science.abc6027.full.pdf Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients] | ||
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| + | 55. [https://www.pnas.org/content/pnas/early/2020/07/07/2008410117.full.pdf BCG vaccine protection from severe coronavirus disease 2019 (COVID-19)] | ||
==Lectures and talks== | ==Lectures and talks== | ||
Revision as of 11:03, 16 July 2020
Contents |
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. |
| Wu et al.[2] | 31.01.2020 | Wuhan / China | SEIR | Model was created to reflect data obtained in Wuhan, then the model was extrapolated on China overall, taking into account cities connection with Wuhan and major routes of air and train transport | R0 for 2019-nCoV is 2.68, 75 815 individuals have been infected in Wuhan as of Jan 25, 2020. The epidemic doubling time was 6.4 days. Chongqing, Beijing, Shanghai, Guangzhou, and Shenzhen had imported 461, 113, 98, 111, and 80 infections from Wuhan, respectively. Epidemics are already growing exponentially in multiple major cities of China with a lag time behind the Wuhan of about 1–2 weeks. On the present trajectory, 2019-nCoV could be about to become a global epidemic in the absence of mitigation.To possibly succeed, substantial, even draconian measures that limit population mobility should be seriously and immediately considered in affected areas. |
| Danon et al.[3] | 14.02.2020 | England and Whales | SEIR+Spatial | Authors used preexisted spatial metapopulation model to describe population movement between regions in England and Whales and standard SEIR model to describe Covid-19 spread in each particular region. | 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[4] | 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[5] | 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.[6] | 02.04.2020 | Australia | Agent-based | AceMod (The Australian Census-based Epidemic Model), containing 24 million agents, each of which represents individual with his own characteristics (gender, occupation, susceptibility) and social context. AceMod was previously developed and validated for simulations of pandemic influenza in Australia. Currently it was calibrated to describe covid-19 pandemic. | 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). |
| Адарченко и др. | 29.05.2020 | Россия | SEIRD and Agent-based | Проведено моделирование развития эпидемии COVID-19 с целью предсказания ее параметров в зависимости от принимаемых мер противодействия: средней длительности эпидемии, наличия второго и/или последующих пиков заболеваемости, максимальной нагрузки на систему здравоохранения. Рассмотрено два подхода: детерминистский и статистический, которые используют методы теории нелинейных дифференциальных уравнений и «агентного» моделирования на основе метода Монте-Карло. Приводятся результаты моделирования развития эпидемии в Москве, Ухане и Нью-Йорке. | Учет асимптомных зараженных в детерменированной модели позволит показать, что расчеты модели принципиально не изменились, но максимумы пиков заражения стали меньше по сравнению с моделью без учета асимптомных в популяции. Полученные расчетные данные агентной модели показывают, что принимаемые властями ограничительные меры дают свой эффект, и в ряде регионов число заражений уже идет на спад. Однако это не означает, что после отмены карантинов эпидемия не может вспыхнуть вновь. |
| Westerhoff&Kolodkin [7] | 12.06.2020 | The Netherlands | SEIR | Using standard systems biology methodologies a 14-compartment dynamic model was developed for the Coronavirus epidemic. | The model predicts that: (i) it will be impossible to limit lockdown intensity such that sufficient herd immunity develops for this epidemic to die down, (ii) the death toll from the SARS-CoV-2 virus decreases very strongly with increasing intensity of the lockdown, but (iii) the duration of the epidemic increases at first with that intensity and then decreases again, such that (iv) it may be best to begin with selecting a lockdown intensity beyond the intensity that leads to the maximum duration, (v) an intermittent lockdown strategy should also work and might be more acceptable socially and economically, (vi) an initially intensive but adaptive lockdown strategy should be most efficient, both in terms of its low number of casualties and shorter duration, (vii) such an adaptive lockdown strategy offers the advantage of being robust to unexpected imports of the virus, e.g. due to international travel, (viii) the eradication strategy may still be superior as it leads to even fewer deaths and a shorter period of economic downturn, but should have the adaptive strategy as backup in case of unexpected infection imports, (ix) earlier detection of infections is the most effective way in which the epidemic can be controlled, whilst waiting for vaccines.. |
Web apps for Covid-19 simulations and data
1. Covid-19 statistics and forecast developed by Biouml.Ru;
4. A web application serves as a planning tool for COVID-19 outbreaks in communities across the world;
5. Los Alamos National Lab prediction;
6. University of Melburn prediction;
7. REINA (Realistic Epidemic Interaction Network Agent Model);
10. Sberindex;
11. Moscow map for coronavirus;
14. Covid-19 predictions for Kazakhstan
Covid-19 statistics
2. UniOxford statistics and research;
3. Worldmeter;
6. OpenSAFELY: a secure health analytics platform covering 40% of all patients in England
7. COVID-19 Pandemic Planning Scenarios
Useful articles
1. Coronavirus research updates ;
2. A Global Effort to Define the Human Genetics f Protective Immunity to SARS-CoV-2 Infection;
4. Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses;
7. Temporal dynamics in viral shedding and transmissibility of COVID-19;
9. Cross-reactive Antibody Response between SARS-CoV-2 and SARS-CoV Infections;
10. These Scenarios Show What a Second Wave of COVID-19 Could Look Like;
11. Herd Immunity: Understanding COVID-19;
12. The race for coronavirus vaccines: a graphical guide;
14. Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19;
15. Reducing transmission of SARS-CoV-2;
16. Epidemiological and Clinical Characteristics of COVID-19 in Adolescents and Young Adults;
17. Positive COVID-19 Test Doesn't Automatically Equate to Virulence;
18. Why herd immunity to COVID-19 is reached much earlier than thought;
19. A study on infectivity of asymptomatic SARS-CoV-2 carriers;
20. Why do some COVID-19 patients infect many others, whereas most don’t spread the virus at all?;
21. Calibration of individual-based models to epidemiological data: A systematic review
22. Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19.
23. Broad neutralization of SARS-related viruses by human monoclonal antibodies;
26. Natural History of Asymptomatic SARS-CoV-2 Infection
27. COVID-19: What proportion are asymptomatic?
28. Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections
31. Economic and social consequences of human mobility restrictions under COVID-19
32. Rapid Generation of Neutralizing Antibody Responses in COVID-19 Patients
34. Intrafamilial Exposure to SARS-CoV-2 Induces Cellular Immune Response without Seroconversion
35. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing
37. Identifying airborne transmission as the dominant route for the spread of COVID-19
38. Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19
39. SARS-CoV-2 productively infects human gut enterocytes
42. The challenges of modeling and forecasting the spread of COVID-19
44. SARS‐CoV‐2 coinfections: Could influenza and the common cold be beneficial?
45. The Pandemic’s Big Mystery: How Deadly Is the Coronavirus?
46. Estimating the burden of SARS-CoV-2 in France
47. Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions
49. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera
50. OpenSAFELY: factors associated with COVID-19 death in 17 million patients
51. Pre-existing immunity to SARS-CoV-2: the knowns and unknowns
52. Viral dynamics in mild and severe cases of COVID-19
53. Spiking Pandemic Potential: Structural and Immunological Aspects of SARS-CoV-2
54. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients
55. BCG vaccine protection from severe coronavirus disease 2019 (COVID-19)
Lectures and talks
2. Происхождение нового коронавируса. Сергей Нетёсов;
3. Коронавирус: Новые данные. Лекция Сергея Нетёсова;
4. Справилась ли Россия с пандемией коронавируса? С. Нетесов;
5. Маргарита Романенко"Тот самый вирус: все что вы хотели знать о COVID19, но стеснялись спросить";
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
- ↑ Wu J.T., Leung K., Leung G.M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study // Lancet. 2020. 395: 689–97.doi:https://doi.org/10.1016/S0140-6736(20)30260-9
- ↑ 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
- ↑ Westerhoff H.V., Kolodkin A.N. Advice from a systems-biology model of the corona epidemics // npj Syst Biol Appl 6, 18, 2020 doi:https://doi.org/10.1038/s41540-020-0138-8
