Difference between revisions of "Covid 19"

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115. [https://doi.org/10.1093/bioinformatics/btaa834 COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology]
 
115. [https://doi.org/10.1093/bioinformatics/btaa834 COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology]
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116. [https://science.sciencemag.org/content/sci/early/2020/09/28/science.abd4250.full.pdf Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity]
  
 
==Lectures and talks==
 
==Lectures and talks==

Revision as of 10:31, 30 September 2020

Contents

Epidemiological models

Authors Publication date Geographical region Type Description Predictions
Ferguson et al[1] 16.03.2020 UK and USA Stochastic model CovidSim models the transmission dynamics and severity of COVID-19 infections throughout a spatially and socially structured population over time. It enables modelling of how intervention policies and healthcare provision affect the spread of COVID-19. It is used to inform health policy by making quantitative forecasts of (for example) cases, deaths and hospitalisations, and how these will vary depending on which specific interventions, such as social distancing, are enacted. With parameter changes, it can be used to model other respiratory viruses, such as influenza. In the absence of control measures Ferguson et al. estimated the peak mortality would occur after approximately 3 months (estimated R0 of 2.4) with an 81% of the GB and US populations infected over the course of the epidemic. In an unmitigated epidemic, they predict approximately 510,000 deaths in GB and 2.2 million in the US would occur.
Chen et al[2] 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.[3] 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.[4] 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[5] 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[6] 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.[7] 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 [8] 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.
Teslya et al.[9] 21.07.2020 The Netherlands and Portugal SEIR Authors developed a deterministic compartmental transmission model of SARS-CoV-2 in a population stratified by disease status (susceptible, exposed, infectious with mild or severe disease, diagnosed, and recovered) and disease awareness status (aware and unaware) due to the spread of COVID-19. Self-imposed measures were assumed to be taken by disease aware individuals and included handwashing, mask-wearing, and social distancing. Government-imposed social distancing reduced the contact rate of individuals irrespective of their disease or awareness status. The model was parameterized using current best estimates of key epidemiological parameters from COVID-19 clinical studies. The model outcomes included the peak number of diagnoses, attack rate, and time until the peak number of diagnoses. For fast awareness spread in the population, self-imposed measures can significantly reduce the attack rate and diminish and postpone the peak number of diagnoses. They estimate that a large epidemic can be prevented if the efficacy of these measures exceeds. 50%. For slow awareness spread, self-imposed measures reduce the peak number of diagnoses and attack rate but do not affect the timing of the peak. Early implementation of short-term government-imposed social distancing alone is estimated to delay (by at most 7 months for a 3-month intervention) but not to reduce the peak. The delay can be even longer and the height of the peak can be additionally reduced if this intervention is combined with self-imposed measures that are continued after government-imposed social distancing has been lifted. Our analyses are limited in that they do not account for stochasticity, demographics, heterogeneities in contact patterns or mixing, spatial effects, imperfect isolation of individuals with severe disease, and reinfection with COVID-19.
Paiva et al. [10] 31.07.2020 China, Italy, Spain, France, Germany, and the USA SEIR The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. The developed formula for R0 shows a strong influence on the human-to-human transmission rate β, followed by the relative transmissibility of hospitalized patients ℓ and of the asymptomatic infected ℓa. By evaluating the estimated values authorities can grasp which actions are more likely to yield more meaningful results. For example, considering that for a country the value of β has stalled, whereas ℓa is still relatively high, measures to reduce the social contact of asymptomatic individuals appear as promising alternatives, instead of simply isolating the symptomatic individuals.
Canabarro et al. [11] 30.07.2020 Brazil SIRD A data-driven age-structured census-based SIRD-like epidemiological model capable of forecasting the spread of COVID-19 in Brazil has been proposed. The developed model demonstrates the early NPI (non-pharmaceutical interventions) measures taken by states and cities such as the total closure

of schools, universities and non-essential services, the social distancing and isolation of individuals above 60 years and the voluntary home quarantine have already lead to a significant reduction in the number of infections as well as delaying the time for the peak of contamination. Thus, these measures have been extremely important to give the authorities the necessary time for adapting and preparing before the peak of the epidemy. The model predicts that even if the current NPIs are not relaxed, as early as mid-April the number of severe cases requiring hospitalization will surpass the current number of available ICUs, starting the collapse of the health system. However, an intense quarantine, if implemented in the following days, can rapidly change the increase in the number of infections and keep the demand for ICUs below the threshold, amounting to hundreds of thousands of saved lives. On the other hand, the model simulations demonstrate that the relaxation of the already imposed control measures in the next days, as currently debated at some sphere of the Brazilian federal government, would be catastrophic, with a total death toll passing the one million mark.

Dobrovolny [12] 10.08.2020 California, Florida, New York and Texas SAIRD In this study, a compartmental mathematical model of a viral epidemic that includes asymptomatic infection to examine the role of asymptomatic individuals in the spread of the infection has been developed. The developed model predicts that asymptomatic infections far outnumber reported symptomatic infections at the peak of the epidemic in all four states. The model suggests that relaxing of social distancing measures too quickly could lead to a rapid rise in the number of cases, driven in part by asymptomatic infections.
Lyra et al. [13] 02.09.2020 Brazil modified SEIR Authours developed a modified SEIR model, including confinement, asymptomatic transmission, quarantine and hospitalization. The population is subdivided into 9 age groups, resulting in a system of 72 coupled nonlinear differential equations. The rate of transmission is dynamic and derived from the observed delayed fatality rate; the parameters of the epidemics are derived with a Markov chain Monte Carlo algorithm. We used Brazil as an example of the middle-income country, but the results are easily generalizable to other countries considering a similar strategy. Authours found that starting from 60% horizontal confinement, an exit strategy on May 1st of confinement of individuals older than 60 years old and full release of the younger population results in 400 000 hospitalizations, 50 000 ICU cases, and 120 000 deaths in the 50-60 years old age group alone. Sensitivity analysis shows the 95% confidence interval brackets an order of magnitude in cases or three weeks in time. The health care system avoids collapse if the 50-60 years old are also confined, but the model assumes an idealized lockdown where the confined are perfectly insulated from contamination, so our numbers are a conservative lower bound. Our results

discourage confinement by age as an exit strategy.

Barbarossa et al. [14] 04.09.2020 Germany SEIR type is extended to account for undetected infections, stages of infection, and age groups In this work, mathematical models are used to reproduce data of the early evolution of the COVID19 outbreak in Germany, taking into account the effect of actual and hypothetical non-pharmaceutical interventions. The models are calibrated on data until April 5. Data from April 6 to 14 are used for model validation. Authors simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. The developed model predicts that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases, and reduced contact to risk groups.
Perkins et al. [15] 21.08.2020 USA Stochastic simulation model Authors developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the United States through 12 March, authors estimated that 108,689 (95% posterior predictive interval [95% PPI]: 1,023 to 14,182,310) infections occurred in the United States by this date. By comparing the model’s predictions of symptomatic infections with local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as an exponential growth of infections outpaced increases in testing. Between 24 February and 12 March, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.98; 95% PPI: 0.66 to 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the United States.
Saad-Roy et al. [16] 21.09.2020 NA SIR(S) Authors developed simple epidemiological models to explore estimates for the magnitude and timing of future Covid-19 cases given different protective efficacy and duration of the adaptive immune response to SARS-CoV-2, as well as its interaction with vaccines and nonpharmaceutical interventions. The model analysis has shown that variations in the immune response to primary SARS-CoV-2 infections and a potential vaccine can lead to dramatically different immune landscapes and burdens of critically severe cases, ranging from sustained epidemics to near elimination. These findings illustrate likely complexities in future Covid-19 dynamics, and highlight the importance of immunological characterization beyond the measurement of active infections for adequately projecting the immune landscape generated by SARS-CoV-2 infections.
Bretta&Rohani [17] 22.09.2020 UK deterministic age-structured SEIR Using an age-structured transmission model, parameterized to simulate SARS-CoV-2 transmission in the United Kingdom, authors assessed the long-term prospects of success using both of these approaches. Authors simulated a range of different nonpharmaceutical intervention scenarios incorporating social distancing applied to different age groups. The model simulations confirmed that suppression of SARS-CoV-2 transmission is possible with plausible levels of social distancing over a period of months, consistent with observed trends. Notably, the model analysis did not support achieving herd immunity as a practical objective, requiring an unlikely balancing of multiple poorly defined forces. Specifically, authors found that 1) social distancing must initially reduce the transmission rate to within a narrow range, 2) to compensate for susceptible depletion, the extent of social distancing must be adaptive over time in a precise yet unfeasible way, and 3) social distancing must be maintained for an extended period to ensure the healthcare system is not overwhelmed.
Wilder et al. [18] 24.09.2020 Hubei, Lombardy, and New York City agent-based model An individual-level model of severe acute respiratory syndrome coronavirus 2 transmission that accounts for population-specific factors such as age distributions, comorbidities, household structures, and contact patterns has been developed. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted “salutary sheltering” by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population.

Web apps for Covid-19 simulations and data

1. Covid-19 statistics and forecast developed by Biouml.Ru;

2. COVID-19 Scenario Analysis Tool (MRC Centre for Global Infectious Disease Analysis, Imperial College London);

3. MIT simulator of Covid-19;

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);

8. German Covid simulator;

9. Open Source QSP model describing SARS-CoV-2 virus and host cell life cycles, immune response and therapeutic treatments;

10. Sberindex;

11. Moscow map for coronavirus;

12. Web-based viewer for 3D visualization and analysis of the SARS-CoV-2 protein structures with respect to the CoV-2 mutational patterns;

13. These apps provide up-to-date visualizations of data tracking the global spread of Coronavirus Disease 2019 (COVID-19), easy access to the World Health Organization's daily situation reports on it and an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic;

14. Covid-19 predictions for Kazakhstan;

15. Human Cell Atlas research on COVID-19;

16. CovidSim microsimulation model developed by the MRC Centre for Global Infectious Disease Analysis hosted at Imperial College, London;

17. COVID-19 Disease Map;

18. SARS2020: An integrated platform for identification of novel coronavirus by a consensus sequence-function model

19. BBMRI-ERIC’s contributions to research and knowledge exchange on COVID-19

20. 91-DIVOC is home to many data-forward, high-quality, interactive, and informative visualizations

21. Dynamics of SARS-CoV-2 over the next five

22. COVID-19 Pathways Portal on WikiPathways

23. Pathway figures related to COVID-19

24. COVID-19 Biomedical Knowledge Miner

Covid-19 statistics

1. JH Institute data;

2. UniOxford statistics and research;

3. Worldmeter;

4. Статистика по России;

5. Стопкоронавирус.рф;

6. OpenSAFELY: a secure health analytics platform covering 40% of all patients in England;

7. COVID-19 Pandemic Planning Scenarios;

8. Covid19 timeseries data store;

9. 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE;

Useful articles

1. Coronavirus research updates ;

2. A Global Effort to Define the Human Genetics of Protective Immunity to SARS-CoV-2 Infection;

3. A noncompeting pair of human neutralizing antibodies block COVID-19 virus binding to its receptor ACE2;

4. Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses;

5. Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals;

6. A Novel Bat Coronavirus Closely Related to SARSCoV-2 Contains Natural Insertions at the S1/S2 Cleavage Site of the Spike Protein;

7. Temporal dynamics in viral shedding and transmissibility of COVID-19;

8. Seroprevalence of SARS-CoV-2 in Hong Kong and in residents evacuated from Hubei province, China: a multicohort study;

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;

13. SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues;

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;

24. Antibody cocktail to SARS-CoV-2 spike protein prevents rapid mutational escape seen with individual antibodies;

25. Structural and Biochemical Characterization of the nsp12-nsp7-nsp8 Core Polymerase Complex from SARS-CoV-2

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

29. Using influenza surveillance networks to estimate statespecific prevalence of SARS-CoV-2 in the United States

30. A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2

31. Economic and social consequences of human mobility restrictions under COVID-19

32. Rapid Generation of Neutralizing Antibody Responses in COVID-19 Patients

33. Comparative replication and immune activation profiles of SARS-CoV-2 and SARS-CoV in human lungs: an ex vivo study with implications for the pathogenesis of COVID-19

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

36. A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates

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

40. Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices

41. Critical Role of Type III Interferon in Controlling SARS-CoV-2 Infection in Human Intestinal Epithelial Cells

42. The challenges of modeling and forecasting the spread of COVID-19

43. Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study

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

48. Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients’ B Cells

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)

56. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

57. The pandemic virus is slowly mutating. But does it matter?

58. Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic

59. The network effect: studying COVID-19 pathology with the Human Cell Atlas

60. Potential Causes and Consequences of Gastrointestinal Disorders during a SARS-CoV-2 Infection

61. Remdesivir Inhibits SARS-CoV-2 in Human Lung Cells and Chimeric SARS-CoV Expressing the SARS-CoV-2 RNA Polymerase in Mice

62. Contact Tracing during Coronavirus Disease Outbreak, South Korea, 2020

63. SnapShot: COVID-19

64. Structural Basis for RNA Replication by the SARS-CoV-2 Polymerase

65. Mathematical models to guide pandemic response

66. Ranking the global impact of the coronavirus pandemic, country by country

67. SARS-CoV-2 Reverse Genetics Reveals a Variable Infection Gradient in the Respiratory Tract

68. The impact of COVID-19 on small business outcomes and expectations

69. The implications of silent transmission for the control of COVID-19 outbreaks

70. How does SARS-CoV-2 cause COVID-19?

71. The protein expression profile of ACE2 in human tissues

72. In vitro and in vivo identification of clinically approved drugs that modify ACE2 expression

73. A data-driven model to describe and forecast the dynamics of COVID-19 transmission

74. Data-driven study of the COVID-19 pandemic via age-structured modelling and prediction of the health system failure in Brazil amid diverse intervention strategies

75. Social distancing responses to COVID-19 emergency declarations strongly differentiated by income

76. The infection fatality rate of COVID-19 inferred from seroprevalence data

77. Selective and cross-reactive SARS-CoV-2 T cell epitopes in unexposed humans

78. In-Hospital Use of Statins Is Associated with a Reduced Risk of Mortality among Individuals with COVID-19

79. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand (Ferguson et al., 2020)

80. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

81. Timing social distancing to avert unmanageable COVID-19 hospital surges

82. Prevalence of Asymptomatic SARS-CoV-2 Infection

83. COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms

84. A molecular pore spans the double membrane of the coronavirus replication organelle

85. The Global Phosphorylation Landscape of SARSCoV-2 Infection

86. Evolving social contact patterns during the COVID-19 crisis in Luxembourg

87. Monitoring Italian COVID-19 spread by a forced SEIRD model

88. Change in global transmission rates of COVID19 through May 6 2020

89. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans

90. Longitudinal evaluation and decline of antibody responses in SARS-CoV-2 infection

91. A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2

92. Pathogenetic profiling of COVID-19 and SARS-like viruses

93. Neutralizing antibodies correlate with protection from SARS-CoV-2 in humans during a fishery vessel outbreak with high attack rate

94. Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models

95. Systems-Level Immunomonitoring from Acute to Recovery Phase of Severe COVID-19

96. Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding

97. The Impact of Mutations in SARS-CoV-2 Spike on Viral Infectivity and Antigenicity

98. Emerging Pandemic Diseases:How We Got to COVID-19

99. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans

100. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

101. How many people has the coronavirus killed?

102. SARS2020: An integrated platform for identification of novel coronavirus by a consensus sequence-function model

103. Substantial underestimation of SARS-CoV-2 infection in the United States

104. Comparative host–pathogen protein–protein interaction analysis of recent coronavirus outbreaks and important host targets identification

105. In vivo antiviral host transcriptional response to SARS-CoV-2 by viral load, sex, and age

106. Open access data from the largest proteomics study on COVID-19 to date

107. Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection

108. Multiorgan and Renal Tropism of SARS-CoV-2

109. Antigen-specific adaptive immunity to SARS-CoV-2 in acute COVID-19 and associations with age and disease severity

110. The emergence of SARS-CoV-2 in Europe and North America

111. Severe acute respiratory syndrome coronavirus persistence in Vero cells

112. Human Coronavirus: Host-Pathogen Interaction

113. Integrative analyses of SARS-CoV-2 genomes from different geographical locations reveal unique features potentially consequential to host-virus interaction, pathogenesis and clues for novel therapies

114. SARS-CoV-2 infection severity is linked to superior humoral immunity against the spike

115. COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology

116. Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity

Lectures and talks

1. Ancha Baranova’s channel;

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";

6. Маргарита Романенко "Вакцина нашей надежды"

7. Viral Issue Crucial Update Sept 8th: the Science, Logic and Data Explained!

References

  1. ↑ Ferguson N., Laydon D., Nedjati Gilani G., Imai N., Ainslie K., Baguelin M., Bhatia S., Boonyasiri A., Cucunuba Perez Z.U., Cuomo-Dannenburg G., Dighe A. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. // Imperial College COVID-19 Response Team (2020). doi:https://dsprdpub.cc.ic.ac.uk:8443/bitstream/10044/1/77482/14/2020-03-16-COVID19-Report-9.pdf
  2. ↑ 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
  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
  4. ↑ 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
  5. ↑ 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
  6. ↑ ttps://github.com/ods-ai-ml4sg/covid19-tutu
  7. ↑ 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
  8. ↑ 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
  9. ↑ Teslya A, Pham TM, Godijk NG, Kretzschmar ME, Bootsma MCJ, Rozhnova G (2020) Impact of self-imposed prevention measures and short-term government-imposed social distancing on mitigating and delaying a COVID-19 epidemic: A modelling study. PLoS Med 17(7): e1003166. doi:https://doi.org/10.1371/journal.pmed.1003166
  10. ↑ Paiva H.M., Afonso R.J.M., de Oliveira I.L., Garcia G.F. A data-driven model to describe and forecast the dynamics of COVID-19 transmission// PLOS One 15, 5, 2020 doi:https://doi.org/10.1371/journal.pone.0236386
  11. ↑ Canabarro A., Teno´rio E., Martins R., Martins L., Brito S., Chaves R. Data-driven study of the COVID-19 pandemic via age-structured modelling and prediction of the health system failure in Brazil amid diverse intervention strategies// PLOS One 15, 5, 2020 doi:https://doi.org/10.1371/journal.pone.0236310
  12. ↑ Dobrovolny H.M. Modeling the role of asymptomatics in infection spread with application to SARS-CoV-2// PLoS ONE 15, 8, 2020: e0236976. doi:https://doi.org/10.1371/journal.pone.0236976
  13. ↑ Lyra W., do Nascimento J-D., Jr., Belkhiria J., de Almeida L., Chrispim P.P.M., de Andrade I. (2020) COVID-19 pandemics modeling with modified determinist SEIR, social distancing, and age stratification. The effect of vertical confinement and release in Brazil // PLoS ONE 15, 9, 2020: e0237627. doi:https://doi.org/10.1371/journal.pone.0237627
  14. ↑ Barbarossa M.V., Fuhrmann J., Meinke J.H.,Krieg S., Varma H.V., Castelletti N., et al. Modeling the spread of COVID-19 in Germany: Early assessment and possible scenarios // PLoS ONE 15, 9,2020: e0238559. doi:https://doi.org/10.1371/journal.pone.0238559
  15. ↑ Perkins T.A., Cavany S.M., Moore S.M., Oidtman R.J., Lerch A., Poterek M. Estimating unobserved SARS-CoV-2 infections in the United States // PNAS 202005476, 2020, doi:10.1073/pnas.2005476117. doi:https://doi.org/10.1073/pnas.2005476117
  16. ↑ Saad-Roy, C.M., Wagner, C.E., Baker, R.E., Morris, S.E., Farrar, J., Graham, A.L., Levin, S.A., Metcalf, C.J.E. and Grenfell, B.T., 2020. Immune life-history, vaccination and the dynamics of SARS-CoV-2 over the next five years // Science eabd7343, 2020, doi:10.1126/science.abd7343. doi:https://doi.org/10.1126/science.abd7343
  17. ↑ Brett, T.S., Rohani, P., 2020. Transmission dynamics reveal the impracticality of COVID-19 herd immunity strategies // PNAS, 2020, doi:10.1073/pnas.2008087117. doi:https://doi.org/10.1073/pnas.2008087117
  18. ↑ Wilder, B., Charpignon, M., Killian, J.A., Ou, H.C., Mate, A., Jabbari, S., Perrault, A., Desai, A.N., Tambe, M. and Majumder, M.S. Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City. // PNAS, 2020, doi:10.1073/pnas.2010651117. doi:https://doi.org/10.1073/pnas.2010651117
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