Difference between revisions of "Covid 19"

Latest revision as of 12:44, 28 July 2022

[edit] 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.
Waltemath et al. ^[19]	02.10.2020	N/A	different types	In a collaboration between the University of Greifswald, the Humboldt-University Berlin, code ahoi, and the BioModels database at EMBL-EBI, authours aim to rapidly disseminate simulation studies of COVID-19 models to the research community, in interoperable formats and in high quality..	N/A.
McCombs&Kadelka ^[20]	15.10.2020	N/A	Stochastic compartmental network model	A stochastic compartmental network model of SARS-CoV-2 spread explores the simultaneous effects of policy choices in three domains: social distancing, hospital triaging, and testing. Considering policy domains together provides insight into how different policy decisions interact. The model incorporates important characteristics of COVID-19, the disease caused by SARS-CoV-2, such as heterogeneous risk factors and asymptomatic transmission, and enables a reliable qualitative comparison of policy choices despite the current uncertainty in key virus and disease parameters.	Results suggest possible refinements to current policies, including emphasizing the need to reduce random encounters more than personal contacts, and testing low-risk symptomatic individuals before high-risk symptomatic individuals. The strength of social distancing of symptomatic individuals affects the degree to which asymptomatic cases drive the epidemic as well as the level of population-wide contact reduction needed to keep hospitals below capacity. The relative importance of testing and triaging also depends on the overall level of social distancing.
Russo et al. ^[21]	30.10.2020	Lombardy, Italy	SEIIRD	To deal with the uncertainty in the number of the actual asymptomatic infected cases in the total population Volpert et al. (2020), authors addressed a modified compartmental Susceptible/Exposed/ Infectious Asymptomatic/ Infected Symptomatic/ Recovered/ Dead (SEIIRD) model with two compartments of infectious persons: one modelling the cases in the population that are asymptomatic or experience very mild symptoms and another modelling the infected cases with mild to severe symptoms. The parameters of the model corresponding to the recovery period, the time from the onset of symptoms to death and the time from exposure to the time that an individual starts to be infectious, have been set as reported from clinical studies on COVID-19. For the estimation of the day-zero of the outbreak in Lombardy, as well as of the “effective” per-day transmission rate for which no clinical data are available, we have used the proposed SEIIRD simulator to fit the numbers of new daily cases from February 21 to the 8th of March.	Based on the proposed methodological procedure, authors estimated that the expected day-zero was January 14 (min-max rage: January 5 to January 23, interquartile range: January 11 to January 18). The actual cumulative number of asymptomatic infected cases in the total population in Lombardy on March 8 was of the order of 15 times the confirmed cumulative number of infected cases, while the expected value of the basic reproduction number R0 was found to be 4.53 (min-max range: 4.40- 4.65). On May 4, the date on which relaxation of the measures commenced the effective reproduction number was found to be 0.987 (interquartiles: 0.857, 1.133). The model approximated adequately two months ahead of time the evolution of reported cases of infected until May 4, the day on which the phase I of the relaxation of measures was implemented over all of Italy. Furthermore, the model predicted that until May 4, around 20% of the population in Lombardy has recovered (interquartile range: ~10% to ~30%).
Zhan et al. ^[22]	30.10.2020	China	SEIR	This study integrates the daily intercity migration data with the classic Susceptible-ExposedInfected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from an official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months.	The model results showed that the number of infections in most cities in China would peak between mid-February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.
Chang et al. ^[23]	11.11.2020	Australia	Agent-based modelling	Here authors report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. Authors applied the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures.	School closures are not found to bring decisive benefits unless coupled with a high level of social distancing compliance. Authors report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13–14 weeks, when coupled with effective case isolation and international travel restrictions.
Català et al. ^[24]	09.12.2020	several European countries	Gompertz model	Gompertz model has been shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate showing the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity and it allows short-term predictions and longer-term estimations. This model has been employed to fit the cumulative cases of Covid-19 from several European countries.	The modelling results show that there are systematic differences in spreading velocity among countries. The model predictions provide a reliable picture of the short-term evolution in countries that are in the initial stages of the Covid-19 outbreak, and may permit researchers to uncover some characteristics of the long-term evolution. These predictions can also be generalized to calculate short-term hospital and intensive care units (ICU) requirements.

[edit] 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

25. COVID-19 Pandemic Resources at UCSC

26. WashU Virus Genome Browser

27. COVID-3D: An online resource to explore the structural distribution of genetic variation in SARS-CoV-2 and its implication on therapeutic development

28. COVID-19 Projections for Russian Federation made by IHME

29. Web tools to fight pandemics: the COVID-19 experience

30. Tracking Cause of Death by State

31. European Virus Bioinformatics Center

32. COVID-19 Event Risk Assessment Planning Tool

33. LitCovid: an open database of COVID-19 literature

34. COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database

35. PAGER-CoV: a comprehensive collection of pathways, annotated gene-lists and gene signatures for coronavirus disease studies

36. COVID-19 mapper

37. COVID-19 Forecasts

38. Taxameter, frequencies of SARS-CoV-2 variants and mutations in regions of Russia

39. CoVariants, SARS-CoV-2 variants and mutations

40. Nextstrain, Genomic epidemiology of novel coronavirus

41. GLEAM Global epidemic and mobility model

42. VGARus (Virus Genome Aggregator of Russia)

43. COVID-19 Spread Mapper

[edit] 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;

10. WHO Coronavirus Disease (COVID-19) Dashboard

11. Coronavirus (COVID-19) disease pandemic- Statistics & Facts

[edit] 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

117. Epidemiology and transmission dynamics of COVID-19 in two Indian states

118. The UCSC SARS-CoV-2 Genome Browser

119. Exploring the coronavirus pandemic with the WashU Virus Genome Browser

120. Exploring the structural distribution of genetic variation in SARS-CoV-2 with the COVID-3D online resource

121. Pediatric SARS-CoV-2: Clinical Presentation, Infectivity, and Immune Responses

122. Clinical Course and Molecular Viral Shedding Among Asymptomatic and Symptomatic Patients With SARS-CoV-2 Infection in a Community Treatment Center in the Republic of Korea

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

124. Loss of Bcl-6-Expressing T Follicular Helper Cells and Germinal Centers in COVID-19

125. Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19

126. COVID-19 Makes B Cells Forget, but T Cells Remember

127. Rethinking Covid-19 Test Sensitivity — A Strategy for Containment

128. Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study

129. Epidemiological characteristics of COVID-19 cases in Italy and estimates of the reproductive numbers one month into the epidemic

130. Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers During the COVID-19 Pandemic: A Modeling Analysis

131. Genomic evidence for reinfection with SARS-CoV-2: a case study

132. Will SARS-CoV-2 become endemic?

133. Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study

134. Infection fatality rate of COVID-19 inferred from seroprevalence data

135. Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms

136. Coinfection with other respiratory pathogens in COVID-19 patients

137. COVID-19 and Excess All-Cause Mortality in the US and 18 Comparison Countries

138. Transcriptional and proteomic insights into the host response in fatal COVID-19 cases

139. Initial economic damage from the COVID-19 pandemic in the United States is more widespread across ages and geographies than initial mortality impacts

140. Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections

141. The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries

142. Genomic evidence for reinfection with SARS-CoV-2: a case study

143. Robust neutralizing antibodies to SARS-CoV-2 infection persist for months

144. Emergence and spread of a SARS-CoV-2 variant through Europe in the summer of 2020

145. Trends in COVID-19 Risk-Adjusted Mortality Rates

146. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

147. Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis

148. Estimation of Individual Probabilities of COVID-19 Infection, Hospitalization, and 2 Death From A County-level Contact of Unknown infection Status

149. Preexisting and de novo humoral immunity to SARS-CoV-2 in humans

150. Proportion of asymptomatic infection among COVID-19 positive persons and their transmission potential: A systematic review and meta-analysis

151. A SARS-CoV-2 vaccine candidate would likely match all currently circulating variants

152. The circulating SARS-CoV-2 spike variant N439K maintains fitness while evading antibody-mediated immunity

153. A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome

154. Unexpected detection of SARS-CoV-2 antibodies in the prepandemic period in Italy

155. Prothrombotic autoantibodies in serum from patients hospitalized with COVID-19

156. Robust estimates of the true (population) infection rate for COVID-19: a backcasting approach

157. Post-lockdown SARS-CoV-2 nucleic acid screening in nearly ten million residents of Wuhan, China

158. Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2

159. Evolution of Antibody Immunity to SARS-CoV-2

160. SARS-CoV-2, SARS-CoV, and MERS-CoV viral load dynamics, duration of viral shedding, and infectiousness: a systematic review and meta-analysis

161. Case Study: Prolonged Infectious SARS-CoV-2 Shedding from an Asymptomatic Immunocompromised Individual with Cancer

162. A compromised specific humoral immune response against the SARS-CoV-2 receptor-binding domain is related to viral persistence and periodic shedding in the gastrointestinal tract

163. SARS-CoV-2 epitopes are recognized by a public and diverse repertoire of human T cell receptors

164. No evidence for increased transmissibility from recurrent mutations in SARS-CoV-2

165. COVID-19 and cardiovascular diseases

166. Higher viral loads in asymptomatic COVID-19 patients might be the invisible part of the iceberg

167. Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms

168. Androgen Signaling Regulates SARS-CoV-2 Receptor Levels and Is Associated with Severe COVID-19 Symptoms in Men

169. Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic

170. Face masks considerably reduce COVID-19 cases in Germany

171. Acute SARS-CoV-2 Infection Impairs Dendritic Cell and T Cell Responses

171. Contribution of monocytes and macrophages to the local tissue inflammation and cytokine storm in COVID-19: Lessons from SARS and MERS, and potential therapeutic interventions

172. Amplification-free detection of SARS-CoV-2 withCRISPR-Cas13a and mobile phone microscopy

173. Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19

174. Quick COVID-19 Healers Sustain Anti-SARS-CoV-2 Antibody Production

175. Practical considerations for measuring the effective reproductive number, Rt

176. Inferring the effectiveness of government interventions against COVID-19

177. Public policy and economic dynamics of COVID-19 spread: A mathematical modeling study

178. Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection

179. Cell-Type-Specific Immune Dysregulation in Severely Ill COVID-19 Patients

180. Mathematical model of COVID-19 intervention scenarios for São Paulo—Brazil

181. In silico dynamics of COVID-19 phenotypes for optimizing clinical management

182. Model-informed COVID-19 vaccine prioritization strategies by age and serostatus

183. COVID-19-neutralizing antibodies predict disease severity and survival

184. INFEKTA—An agent-based model for transmission of infectious diseases: The COVID-19 case in Bogotá, Colombia

185. Development of an interactive, agent-based local stochastic model of COVID-19 transmission and evaluation of mitigation strategies illustrated for the state of Massachusetts, USA

186. Evaluating epidemic forecasts in an interval format

[edit] 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!

8. Prof. Paul Marik, COVID 19: A Clinical Update

[edit] Tutorials

Подготовленные в рамках проекта учебно-методические материалы выложены тут

[edit] Models developed in the project

Список созданных и воспроизведенных в рамках проекта математических моделей представлен тут

[edit] References

↑ 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
↑ 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
↑ 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
↑ 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
↑ 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
↑ 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
↑ 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
↑ 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
↑ 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
↑ 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
↑ 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
↑ 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
↑ Reproducible simulation studies targeting COVID-19 // BiomodelsDB 2020. doi:https://wwwdev.ebi.ac.uk/biomodels/covid-19
↑ McCombs A., Kadelka C. A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies. // PLoS Comput Biol, 2020, 16(10): e1008388. doi:https://doi.org/10.1371/journal.pcbi.1008388
↑ Russo L., Anastassopoulou C., Tsakris A., Bifulco G.N., Campana E.F., Toraldo G., et al. Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach.// PLoS ONE, 2020, 15(10): e0240649. doi:https://doi.org/10.1371/journal.pone.0240649
↑ Zhan C., Tse C.K., Fu Y., Lai Z., Zhang H. Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data.// PLoS ONE, 2020, 15(10): e0241171. doi:https://doi.org/10.1371/journal.pone.0241171
↑ Chang, S.L., Harding, N., Zachreson, C. et al. Modelling transmission and control of the COVID-19 pandemic in Australia.// Nat Commun, 2020, 11(5710) doi:https://doi.org/10.1038/s41467-020-19393-6
↑ Català, M., Alonso, S., Alvarez-Lacalle, E., Lopez, D., Cardona, P-J., Prats, C. Empirical model for short-time prediction of COVID-19 spreading.// PLoS Comput Biol., 2020, 16(12): e1008431. doi:https://doi.org/10.1371/journal.pcbi.1008431

[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

[19] Reproducible simulation studies targeting COVID-19 // BiomodelsDB 2020. doi:https://wwwdev.ebi.ac.uk/biomodels/covid-19

[20] McCombs A., Kadelka C. A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies. // PLoS Comput Biol, 2020, 16(10): e1008388. doi:https://doi.org/10.1371/journal.pcbi.1008388

[21] Russo L., Anastassopoulou C., Tsakris A., Bifulco G.N., Campana E.F., Toraldo G., et al. Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach.// PLoS ONE, 2020, 15(10): e0240649. doi:https://doi.org/10.1371/journal.pone.0240649

[22] Zhan C., Tse C.K., Fu Y., Lai Z., Zhang H. Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data.// PLoS ONE, 2020, 15(10): e0241171. doi:https://doi.org/10.1371/journal.pone.0241171

[23] Chang, S.L., Harding, N., Zachreson, C. et al. Modelling transmission and control of the COVID-19 pandemic in Australia.// Nat Commun, 2020, 11(5710) doi:https://doi.org/10.1038/s41467-020-19393-6

[24] Català, M., Alonso, S., Alvarez-Lacalle, E., Lopez, D., Cardona, P-J., Prats, C. Empirical model for short-time prediction of COVID-19 spreading.// PLoS Comput Biol., 2020, 16(12): e1008431. doi:https://doi.org/10.1371/journal.pcbi.1008431

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Difference between revisions of "Covid 19"

Latest revision as of 12:44, 28 July 2022

Contents

[edit] Epidemiological models

[edit] Web apps for Covid-19 simulations and data

[edit] Covid-19 statistics

[edit] Useful articles

[edit] Lectures and talks

[edit] Tutorials

[edit] Models developed in the project

[edit] References

Personal tools

Namespaces

Variants

Views

Actions

Search

BioUML platform

Community

Modelling

Analysis & Workflows

Collaborative research

Development

Virtual biology

Wiki

Toolbox

@@ Line 4: / Line 4: @@
 !Authors!!Publication date!!Geographical region!!Type!!Description!!Predictions
 |- style="background-color: Lavender"
+|Ferguson et al<ref> 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}}</ref>
+|16.03.2020
+|UK and USA
+|Stochastic model
+|[https://github.com/mrc-ide/covid-sim 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 [https://www.cebm.net/wp-content/uploads/2020/03/Impact-assessment-of-non-pharmaceutical-interventions-NPIs-to-reduce-COVID-19-mortality-and-healthcare-demand-.pdf 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<ref>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}}</ref>
 |28.01.2020
@@ Line 66: / Line 73: @@
 |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. <ref>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}}</ref>
+|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. <ref>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}}</ref>
+|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 <ref> 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}}</ref>
+|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. <ref> 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}}</ref>
+|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. <ref> 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}}</ref>
+|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. <ref> 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}}</ref>
+|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. <ref> 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}}</ref>
+|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 <ref> 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}}</ref>
+|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. <ref> 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}}</ref>
+|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.
+|-
+|Waltemath et al. <ref> Reproducible simulation studies targeting COVID-19 // BiomodelsDB 2020.  {{doi|https://wwwdev.ebi.ac.uk/biomodels/covid-19}}</ref>
+|02.10.2020
+|N/A
+|different types
+|In a collaboration between the University of Greifswald, the Humboldt-University Berlin, code ahoi, and the BioModels database at EMBL-EBI, authours aim to rapidly disseminate simulation studies of COVID-19 models to the research community, in interoperable formats and in high quality..
+|N/A.
+|-
+|McCombs&Kadelka <ref> McCombs A., Kadelka C. A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies. // PLoS Comput Biol, 2020, 16(10): e1008388.  {{doi|https://doi.org/10.1371/journal.pcbi.1008388}}</ref>
+|15.10.2020
+|N/A
+|Stochastic compartmental network model
+|A stochastic compartmental network model of SARS-CoV-2 spread explores the simultaneous effects of policy choices in three domains: social distancing, hospital triaging, and testing. Considering policy domains together provides insight into how different policy decisions interact. The model incorporates important characteristics of COVID-19, the disease caused by SARS-CoV-2, such as heterogeneous risk factors and asymptomatic transmission, and enables a reliable qualitative comparison of policy choices despite the current uncertainty in key virus and disease parameters.
+|Results suggest possible refinements to current policies, including emphasizing the need to reduce random encounters more than personal contacts, and testing low-risk symptomatic individuals before high-risk symptomatic individuals. The strength of social distancing of symptomatic individuals affects the degree to which asymptomatic cases drive the epidemic as well as the level of population-wide contact reduction needed to keep hospitals below capacity. The relative importance of testing and triaging also depends on the overall level of social distancing.
+|-
+|Russo et al. <ref> Russo L., Anastassopoulou C., Tsakris A., Bifulco G.N., Campana E.F., Toraldo G., et al. Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach.// PLoS ONE, 2020, 15(10): e0240649. {{doi|https://doi.org/10.1371/journal.pone.0240649}}</ref>
+|30.10.2020
+|Lombardy, Italy
+|SEIIRD
+|To deal with the uncertainty in the number of the actual asymptomatic infected cases in the total population Volpert et al. (2020), authors addressed a modified compartmental Susceptible/Exposed/ Infectious Asymptomatic/ Infected Symptomatic/ Recovered/ Dead (SEIIRD) model with two compartments of infectious persons: one modelling the cases in the population that are asymptomatic or experience very mild symptoms and another modelling the infected cases with mild to severe symptoms. The parameters of the model corresponding to the recovery period, the time from the onset of symptoms to death and the time from exposure to the time that an individual starts to be infectious, have been set as reported from clinical studies on COVID-19. For the estimation of the day-zero of the outbreak in Lombardy, as well as of the “effective” per-day transmission rate for which no clinical data are available, we have used the proposed SEIIRD simulator to fit the numbers of new daily cases from February 21 to the 8th of March.
+|Based on the proposed methodological procedure, authors estimated that the expected day-zero was January 14 (min-max rage: January 5 to January 23, interquartile range: January 11 to January 18). The actual cumulative number of asymptomatic infected cases in the total population in Lombardy on March 8 was of the order of 15 times the confirmed cumulative number of infected cases, while the expected value of the basic reproduction number R0 was found to be 4.53 (min-max range: 4.40- 4.65). On May 4, the date on which relaxation of the measures commenced the effective reproduction number was found to be 0.987 (interquartiles: 0.857, 1.133). The model approximated adequately two months ahead of time the evolution of reported cases of infected until May 4, the day on which the phase I of the relaxation of measures was implemented over all of Italy. Furthermore, the model predicted that until May 4, around 20% of the population in Lombardy has recovered (interquartile range: ~10% to ~30%).
+|-
+|Zhan et al. <ref> Zhan C., Tse C.K., Fu Y., Lai Z., Zhang H. Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data.// PLoS ONE, 2020, 15(10): e0241171. {{doi|https://doi.org/10.1371/journal.pone.0241171}}</ref>
+|30.10.2020
+|China
+|SEIR
+|This study integrates the daily intercity migration data with the classic Susceptible-ExposedInfected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from an official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months.
+|The model results showed that the number of infections in most cities in China would peak between mid-February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.
+|-
+|Chang et al. <ref> Chang, S.L., Harding, N., Zachreson, C. et al. Modelling transmission and control of the COVID-19 pandemic in Australia.// Nat Commun, 2020, 11(5710) {{doi|https://doi.org/10.1038/s41467-020-19393-6}}</ref>
+|11.11.2020
+|Australia
+|Agent-based modelling
+|Here authors report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. Authors applied the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures.
+|School closures are not found to bring decisive benefits unless coupled with a high level of social distancing compliance. Authors report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13–14 weeks, when coupled with effective case isolation and international travel restrictions.
+|-
+|Català et al. <ref> Català, M., Alonso, S., Alvarez-Lacalle, E., Lopez, D., Cardona, P-J., Prats, C. Empirical model for short-time prediction of COVID-19 spreading.// PLoS Comput Biol., 2020, 16(12): e1008431. {{doi|https://doi.org/10.1371/journal.pcbi.1008431}}</ref>
+|09.12.2020
+|several European countries
+|Gompertz model
+|Gompertz model has been shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate showing the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity and it allows short-term predictions and longer-term estimations. This model has been employed to fit the cumulative cases of Covid-19 from several European countries.
+|The modelling results show that there are systematic differences in spreading velocity among countries. The model predictions provide a reliable picture of the short-term evolution in countries that are in the initial stages of the Covid-19 outbreak, and may permit researchers to uncover some characteristics of the long-term evolution. These predictions can also be generalized to calculate short-term hospital and intensive care units (ICU) requirements.
 |}
 </center>
@@ Line 94: / Line 208: @@
 . [https://coronavirus3d.org/index.html Web-based viewer for 3D visualization and analysis of the SARS-CoV-2 protein structures with respect to the CoV-2 mutational patterns];
-. [http://nasqar.abudhabi.nyu.edu/#epid 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]
+. [http://nasqar.abudhabi.nyu.edu/#epid 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];
-. [http://covid19.mmay.info/ Covid-19 predictions for Kazakhstan]
+. [http://covid19.mmay.info/ Covid-19 predictions for Kazakhstan];
-. [https://www.humancellatlas.org/covid-19/ Human Cell Atlas research on COVID-19]
+. [https://www.humancellatlas.org/covid-19/ Human Cell Atlas research on COVID-19];
+. [https://github.com/mrc-ide/covid-sim CovidSim microsimulation model developed by the MRC Centre for Global Infectious Disease Analysis hosted at Imperial College, London];
+. [https://covid.pages.uni.lu/ COVID-19 Disease Map];
+. [http://design.rxnfinder.org/sars2020/ SARS2020: An integrated platform for identification of novel coronavirus by a consensus sequence-function model]
+. [https://www.nature.com/articles/s41431-020-0634-8.pdf BBMRI-ERIC’s contributions to research and knowledge exchange on COVID-19]
+. [https://91-divoc.com/ 91-DIVOC is home to many data-forward, high-quality, interactive, and informative visualizations]
+. [https://grenfelllab.shinyapps.io/sarscov2/ Dynamics of SARS-CoV-2 over the next five]
+. [https://www.wikipathways.org/index.php/Portal:COVID-19 COVID-19 Pathways Portal on WikiPathways]
+. [https://gladstone-bioinformatics.shinyapps.io/shiny-covidpathways/ Pathway figures related to COVID-19]
+. [https://bikmi.covid19-knowledgespace.de/ COVID-19 Biomedical Knowledge Miner]
+. [https://genome.ucsc.edu/covid19.html COVID-19 Pandemic Resources at UCSC]
+. [https://virusgateway.wustl.edu/ WashU Virus Genome Browser]
+. [http://biosig.unimelb.edu.au/covid3d/ COVID-3D: An online resource to explore the structural distribution of genetic variation in SARS-CoV-2 and its implication on therapeutic development]
+. [https://covid19.healthdata.org/russian-federation?view=total-deaths&tab=trend COVID-19 Projections for Russian Federation made by IHME]
+. [https://doi.org/10.1093/bib/bbaa261 Web tools to fight pandemics: the COVID-19 experience]
+. [https://episphere.github.io/mortalitytracker/ Tracking Cause of Death by State]
+. [http://evbc.uni-jena.de/tools/coronavirus-tools/ European Virus Bioinformatics Center]
+. [https://covid19risk.biosci.gatech.edu/?fbclid=IwAR2Gv628iR07R7fFUDBnbPosJ2MTTT1poBeHuP5G-fr1zG4L7GaFQMtf2oI COVID-19 Event Risk Assessment Planning Tool]
+. [https://www.ncbi.nlm.nih.gov/research/coronavirus/ LitCovid: an open database of COVID-19 literature]
+. [https://doi.org/10.1093/bioinformatics/btaa718 COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database]
+. [http://discovery.informatics.uab.edu/PAGER-CoV/ PAGER-CoV: a comprehensive collection of pathways, annotated gene-lists and gene signatures for coronavirus disease studies]
+. [https://vac-lshtm.shinyapps.io/ncov_tracker/ COVID-19 mapper]
+. [https://viz.covid19forecasthub.org/ COVID-19 Forecasts]
+. [https://taxameter.ru/ Taxameter, frequencies of SARS-CoV-2 variants and mutations in regions of Russia]
+. [https://covariants.org/ CoVariants, SARS-CoV-2 variants and mutations]
+. [https://nextstrain.org/ncov/gisaid/global Nextstrain, Genomic epidemiology of novel coronavirus]
+. [http://www.gleamviz.org/model/ GLEAM Global epidemic and mobility model]
+. [https://genome.crie.ru/app/index VGARus (Virus Genome Aggregator of Russia)]
+. [https://metrics.covid19-analysis.org/ COVID-19 Spread Mapper]
 ==Covid-19 statistics==
@@ Line 109: / Line 279: @@
 . [https://coronavirusstat.ru/ Статистика по России];
-. [https://xn--80aesfpebagmfblc0a.xn--p1ai/ Стопкоронавирус.рф]
+. [https://xn--80aesfpebagmfblc0a.xn--p1ai/ Стопкоронавирус.рф];
+. [https://github.com/opensafely/risk-factors-research OpenSAFELY: a secure health analytics platform covering 40% of all patients in England];
+. [https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html COVID-19 Pandemic Planning Scenarios];
+. [https://github.com/ulklc/covid19-timeseries Covid19 timeseries data store];
+. [https://github.com/CSSEGISandData/COVID-19 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE];
-. [https://github.com/opensafely/risk-factors-research OpenSAFELY: a secure health analytics platform covering 40% of all patients in England]
+. [https://covid19.who.int/ WHO Coronavirus Disease (COVID-19) Dashboard]
-. [https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html COVID-19 Pandemic Planning Scenarios]
+. [https://www.statista.com/topics/5994/the-coronavirus-disease-covid-19-outbreak/ Coronavirus (COVID-19) disease pandemic- Statistics & Facts]
 ==Useful articles==
 . [https://www.nature.com/articles/d41586-020-00502-w?fbclid=IwAR3coGb69jEt_Z-LM4MXlEoJXF_M98pS306eYjX1rAO2cbJrocUyhRqFKUw Coronavirus research updates ];
-. [https://doi.org/10.1016/j.cell.2020.05.016 A Global Effort to Define the Human Genetics f Protective Immunity to SARS-CoV-2 Infection];
+. [https://doi.org/10.1016/j.cell.2020.05.016 A Global Effort to Define the Human Genetics of Protective Immunity to SARS-CoV-2 Infection];
-. [https://www.pnas.org/content/early/2020/06/09/2008176117 https://science.sciencemag.org/content/368/6496/1274/tab-pdf A noncompeting pair of human neutralizing antibodies block COVID-19 virus binding to its receptor ACE2];
+. [https://science.sciencemag.org/content/368/6496/1274/tab-pdf A noncompeting pair of human neutralizing antibodies block COVID-19 virus binding to its receptor ACE2];
 . [https://www.pnas.org/content/early/2020/06/09/2008176117.abstract?etoc Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses];
@@ Line 239: / Line 417: @@
 . [https://wwwnc.cdc.gov/eid/article/26/10/20-1315_article?fbclid=IwAR2yIsJ_cQSlFBSIWPiPClZQdmrlI41E3fF_Xptr-JIvw2hOklcaAJsTWek#r3 Contact Tracing during Coronavirus Disease Outbreak, South Korea, 2020]
+. [https://www.cell.com/action/showPdf?pii=S0092-8674%2820%2930475-X SnapShot: COVID-19]
+. [https://www.cell.com/action/showPdf?pii=S0092-8674%2820%2930629-2 Structural Basis for RNA Replication by the SARS-CoV-2 Polymerase]
+. [https://science.sciencemag.org/content/sci/369/6502/368.full.pdf Mathematical models to guide pandemic response]
+. [https://www.politico.com/interactives/2020/ranking-countries-coronavirus-impact/ Ranking the global impact of the coronavirus pandemic, country by country]
+. [https://www.cell.com/action/showPdf?pii=S0092-8674%2820%2930675-9 SARS-CoV-2 Reverse Genetics Reveals a Variable Infection Gradient in the Respiratory Tract]
+. [https://www.pnas.org/content/pnas/117/30/17656.full.pdf The impact of COVID-19 on small business outcomes and expectations]
+. [https://www.pnas.org/content/pnas/117/30/17513.full.pdf The implications of silent transmission for the control of COVID-19 outbreaks]
+. [https://science.sciencemag.org/content/sci/369/6503/510.full.pdf How does SARS-CoV-2 cause COVID-19?]
+. [https://www.embopress.org/doi/epdf/10.15252/msb.20209610 The protein expression profile of ACE2 in human tissues]
+. [https://www.embopress.org/doi/epdf/10.15252/msb.20209628 In vitro and in vivo identification of clinically approved drugs that modify ACE2 expression]
+. [https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0236386&type=printable A data-driven model to describe and forecast the dynamics of COVID-19 transmission]
+. [https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0236310&type=printable 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]
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+. [https://science.sciencemag.org/content/sci/370/6521/eabe9403.full.pdf Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms]
+. [https://www.cell.com/cell-stem-cell/pdfExtended/S1934-5909(20)30547-6 Androgen Signaling Regulates SARS-CoV-2 Receptor Levels and Is Associated with Severe COVID-19 Symptoms in Men]
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+. [https://science.sciencemag.org/content/sci/early/2020/12/15/science.abd9338.full.pdf Inferring the effectiveness of government interventions against COVID-19]
+. [https://doi.org/10.1371/journal.pone.0244174 Public policy and economic dynamics of COVID-19 spread: A mathematical modeling study]
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+. [https://science.sciencemag.org/content/early/2021/01/21/science.abe6959/tab-pdf Model-informed COVID-19 vaccine prioritization strategies by age and serostatus]
+. [https://www.cell.com/action/showPdf?pii=S0092-8674%2820%2931685-8 COVID-19-neutralizing antibodies predict disease severity and survival]
+. [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245787 INFEKTA—An agent-based model for transmission of infectious diseases: The COVID-19 case in Bogotá, Colombia]
+. [https://doi.org/10.1371/journal.pone.0247182 Development of an interactive, agent-based local stochastic model of COVID-19 transmission and evaluation of mitigation strategies illustrated for the state of Massachusetts, USA]
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 ==Lectures and talks==
@@ Line 250: / Line 678: @@
 . [https://youtu.be/owTw7HDZBhY Маргарита Романенко"Тот самый вирус: все что вы хотели знать о COVID19, но стеснялись спросить"];
+. [https://youtu.be/T4gLKabhjQw Маргарита Романенко "Вакцина нашей надежды"]
+. [https://youtu.be/8UvFhIFzaac Viral Issue Crucial Update Sept 8th: the Science, Logic and Data Explained!]
+. [https://youtu.be/GhL2FPcBHHQ Prof. Paul Marik, COVID 19: A Clinical Update]
+==Tutorials==
+Подготовленные в рамках проекта учебно-методические материалы выложены [http://wiki.biouml.org/index.php/Tutorials тут]
+==Models developed in the project==
+Список созданных и воспроизведенных в рамках проекта математических моделей представлен [http://wiki.biouml.org/index.php/Developed_models тут]
 ==References==
 <references/>