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Coronavirus : publications scientifiques, cartes, statistiques, essais cliniques etc.


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Un preprint a été déposé sur medRXiv :



Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions


This article is a preprint and has not been peer-reviewed [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.




In December 2019, a novel coronavirus (2019-nCoV) is thought to have emerged into the human population in Wuhan, China. The number of identified cases in Wuhan has increased rapidly since, and cases have been identified in other Chinese cities and other countries (as of 23 January 2020).


We fitted a transmission model to reported case information up to 21 January to estimate key epidemiological measures, and to predict the possible course of the epidemic, as the potential impact of travel restrictions into and from Wuhan.


We estimate the basic reproduction number of the infection (R_0) to be 3.8 (95% confidence interval, 3.6-4.0), indicating that 72-75% of transmissions must be prevented by control measures for infections to stop increasing.


We estimate that only 5.1% (95%CI, 4.8-5.5) of infections in Wuhan are identified, and by 21 January a total of 11,341 people (prediction interval, 9,217-14,245) had been infected in Wuhan since the start of the year.


Should the epidemic continue unabated in Wuhan, we predict the epidemic in Wuhan will be substantially larger by 4 February (191,529 infections; prediction interval, 132,751-273,649), infection will be established in other Chinese cities, and importations to other countries will be more frequent.


Our model suggests that travel restrictions from and to Wuhan city are unlikely to be effective in halting transmission across China; with a 99% effective reduction in travel, the size of the epidemic outside of Wuhan may only be reduced by 24.9% on 4 February.


Our findings are critically dependent on the assumptions underpinning our model, and the timing and reporting of confirmed cases, and there is considerable uncertainty associated with the outbreak at this early stage.


With these caveats in mind, our work suggests that a basic reproductive number for this 2019-nCoV outbreak is higher compared to other emergent coronaviruses, suggesting that containment or control of this pathogen may be substantially more difficult.


Profil de l'auteur : https://scholar.google.com/citations?user=0kgrbMEAAAAJ&hl=en&oi=ao

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Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

January 29, 2020




The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP.




We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number.




Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9).




On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)


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  • 4 weeks later...


Il y a 8 heures, Rincevent a dit :

Et sinon, c'est confirmé que la chloroquine marche contre ce virus, ou pas ?


Je viens justement de trouver ça, via Twitter :



Breakthrough: Chloroquine phosphate has shown apparent efficacy in treatment of COVID-19 associated pneumonia in clinical studies.
Gao J1, Tian Z2, Yang X2.




The coronavirus disease 2019 (COVID-19) virus is spreading rapidly, and scientists are endeavoring to discover drugs for its efficacious treatment in China. Chloroquine phosphate, an old drug for treatment of malaria, is shown to have apparent efficacy and acceptable safety against COVID-19 associated pneumonia in multicenter clinical trials conducted in China. The drug is recommended to be included in the next version of the Guidelines for the Prevention, Diagnosis, and Treatment of Pneumonia Caused by COVID-19 issued by the National Health Commission of the People's Republic of China for treatment of COVID-19 infection in larger populations in the future.


Voir aussi : Landscape analysis of therapeutics as 17 February 2020

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Une étude détaillant le tableau clinique de COVID-19 vient de sortir dans le New England Journal of Medicine :



Clinical Characteristics of Coronavirus Disease 2019 in China




Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients.


We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death.


The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission.


During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)



In early December 2019, the first pneumonia cases of unknown origin were identified in Wuhan, the capital city of Hubei province.1 The pathogen has been identified as a novel enveloped RNA betacoronavirus2 that has currently been named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has a phylogenetic similarity to SARS-CoV.3 Patients with the infection have been documented both in hospitals and in family settings.4-8


The World Health Organization (WHO) has recently declared coronavirus disease 2019 (Covid-19) a public health emergency of international concern.9 As of February 25, 2020, a total of 81,109 laboratory-confirmed cases had been documented globally.5,6,9-11 In recent studies, the severity of some cases of Covid-19 mimicked that of SARS-CoV.1,12,13 Given the rapid spread of Covid-19, we determined that an updated analysis of cases throughout China might help identify the defining clinical characteristics and severity of the disease. Here, we describe the results of our analysis of the clinical characteristics of Covid-19 in a selected cohort of patients throughout China.





Study Oversight


The study was supported by National Health Commission of China and designed by the investigators. The study was approved by the institutional review board of the National Health Commission. Written informed consent was waived in light of the urgent need to collect data. Data were analyzed and interpreted by the authors. All the authors reviewed the manuscript and vouch for the accuracy and completeness of the data and for the adherence of the study to the protocol, available with the full text of this article at NEJM.org.


Data Sources


We obtained the medical records and compiled data for hospitalized patients and outpatients with laboratory-confirmed Covid-19, as reported to the National Health Commission between December 11, 2019, and January 29, 2020; the data cutoff for the study was January 31, 2020. Covid-19 was diagnosed on the basis of the WHO interim guidance.14 A confirmed case of Covid-19 was defined as a positive result on high-throughput sequencing or real-time reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assay of nasal and pharyngeal swab specimens.1 Only laboratory-confirmed cases were included in the analysis.

We obtained data regarding cases outside Hubei province from the National Health Commission. Because of the high workload of clinicians, three outside experts from Guangzhou performed raw data extraction at Wuhan Jinyintan Hospital, where many of the patients with Covid-19 in Wuhan were being treated.

We extracted the recent exposure history, clinical symptoms or signs, and laboratory findings on admission from electronic medical records. Radiologic assessments included chest radiography or computed tomography (CT), and all laboratory testing was performed according to the clinical care needs of the patient. We determined the presence of a radiologic abnormality on the basis of the documentation or description in medical charts; if imaging scans were available, they were reviewed by attending physicians in respiratory medicine who extracted the data. Major disagreement between two reviewers was resolved by consultation with a third reviewer. Laboratory assessments consisted of a complete blood count, blood chemical analysis, coagulation testing, assessment of liver and renal function, and measures of electrolytes, C-reactive protein, procalcitonin, lactate dehydrogenase, and creatine kinase. We defined the degree of severity of Covid-19 (severe vs. nonsevere) at the time of admission using the American Thoracic Society guidelines for community-acquired pneumonia.15

All medical records were copied and sent to the data-processing center in Guangzhou, under the coordination of the National Health Commission. A team of experienced respiratory clinicians reviewed and abstracted the data. Data were entered into a computerized database and cross-checked. If the core data were missing, requests for clarification were sent to the coordinators, who subsequently contacted the attending clinicians.


Study Outcomes


The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. These outcomes were used in a previous study to assess the severity of other serious infectious diseases, such as H7N9 infection.16 Secondary end points were the rate of death and the time from symptom onset until the composite end point and until each component of the composite end point.


Study Definitions


The incubation period was defined as the interval between the potential earliest date of contact of the transmission source (wildlife or person with suspected or confirmed case) and the potential earliest date of symptom onset (i.e., cough, fever, fatigue, or myalgia). We excluded incubation periods of less than 1 day because some patients had continuous exposure to contamination sources; in these cases, the latest date of exposure was recorded. The summary statistics of incubation periods were calculated on the basis of 291 patients who had clear information regarding the specific date of exposure.

Fever was defined as an axillary temperature of 37.5°C or higher. Lymphocytopenia was defined as a lymphocyte count of less than 1500 cells per cubic millimeter. Thrombocytopenia was defined as a platelet count of less than 150,000 per cubic millimeter. Additional definitions — including exposure to wildlife, acute respiratory distress syndrome (ARDS), pneumonia, acute kidney failure, acute heart failure, and rhabdomyolysis — are provided in the Supplementary Appendix, available at NEJM.org.


Laboratory Confirmation


Laboratory confirmation of SARS-CoV-2 was performed at the Chinese Center for Disease Prevention and Control before January 23, 2020, and subsequently in certified tertiary care hospitals. RT-PCR assays were performed in accordance with the protocol established by the WHO.17 Details regarding laboratory confirmation processes are provided in the Supplementary Appendix.


Statistical Analysis


Continuous variables were expressed as medians and interquartile ranges or simple ranges, as appropriate. Categorical variables were summarized as counts and percentages. No imputation was made for missing data. Because the cohort of patients in our study was not derived from random selection, all statistics are deemed to be descriptive only. We used ArcGIS, version 10.2.2, to plot the numbers of patients with reportedly confirmed cases on a map. All the analyses were performed with the use of R software, version 3.6.2 (R Foundation for Statistical Computing).





Demographic and Clinical Characteristics



Figure 1. Distribution of Patients with Covid-19 across China.


Of the 7736 patients with Covid-19 who had been hospitalized at 552 sites as of January 29, 2020, we obtained data regarding clinical symptoms and outcomes for 1099 patients (14.2%). The largest number of patients (132) had been admitted to Wuhan Jinyintan Hospital. The hospitals that were included in this study accounted for 29.7% of the 1856 designated hospitals where patients with Covid-19 could be admitted in 30 provinces, autonomous regions, or municipalities across China (Figure 1).



Table 1. Clinical Characteristics of the Study Patients, According to Disease Severity and the Presence or Absence of the Primary Composite End Point.


The demographic and clinical characteristics of the patients are shown in Table 1. A total of 3.5% were health care workers, and a history of contact with wildlife was documented in 1.9%; 483 patients (43.9%) were residents of Wuhan. Among the patients who lived outside Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city; 25.9% of nonresidents had neither visited the city nor had contact with Wuhan residents.

The median incubation period was 4 days (interquartile range, 2 to 7). The median age of the patients was 47 years (interquartile range, 35 to 58); 0.9% of the patients were younger than 15 years of age. A total of 41.9% were female. Fever was present in 43.8% of the patients on admission but developed in 88.7% during hospitalization. The second most common symptom was cough (67.8%); nausea or vomiting (5.0%) and diarrhea (3.8%) were uncommon. Among the overall population, 23.7% had at least one coexisting illness (e.g., hypertension and chronic obstructive pulmonary disease).

On admission, the degree of severity of Covid-19 was categorized as nonsevere in 926 patients and severe in 173 patients. Patients with severe disease were older than those with nonsevere disease by a median of 7 years. Moreover, the presence of any coexisting illness was more common among patients with severe disease than among those with nonsevere disease (38.7% vs. 21.0%). However, the exposure history between the two groups of disease severity was similar.

Radiologic and Laboratory Findings




Table 2. Radiographic and Laboratory Findings.


Table 2 shows the radiologic and laboratory findings on admission. Of 975 CT scans that were performed at the time of admission, 86.2% revealed abnormal results. The most common patterns on chest CT were ground-glass opacity (56.4%) and bilateral patchy shadowing (51.8%). Representative radiologic findings in two patients with nonsevere Covid-19 and in another two patients with severe Covid-19 are provided in Figure S1 in the Supplementary Appendix. No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease.

On admission, lymphocytopenia was present in 83.2% of the patients, thrombocytopenia in 36.2%, and leukopenia in 33.7%. Most of the patients had elevated levels of C-reactive protein; less common were elevated levels of alanine aminotransferase, aspartate aminotransferase, creatine kinase, and d-dimer. Patients with severe disease had more prominent laboratory abnormalities (including lymphocytopenia and leukopenia) than those with nonsevere disease.


Clinical Outcomes




Table 3. Complications, Treatments, and Clinical Outcomes.


None of the 1099 patients were lost to follow-up during the study. A primary composite end-point event occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died (Table 3). Among the 173 patients with severe disease, a primary composite end-point event occurred in 43 patients (24.9%). Among all the patients, the cumulative risk of the composite end point was 3.6%; among those with severe disease, the cumulative risk was 20.6%.

Treatment and Complications


A majority of the patients (58.0%) received intravenous antibiotic therapy, and 35.8% received oseltamivir therapy; oxygen therapy was administered in 41.3% and mechanical ventilation in 6.1%; higher percentages of patients with severe disease received these therapies (Table 3). Mechanical ventilation was initiated in more patients with severe disease than in those with nonsevere disease (noninvasive ventilation, 32.4% vs. 0%; invasive ventilation, 14.5% vs. 0%). Systemic glucocorticoids were given to 204 patients (18.6%), with a higher percentage among those with severe disease than nonsevere disease (44.5% vs. 13.7%). Of these 204 patients, 33 (16.2%) were admitted to the ICU, 17 (8.3%) underwent invasive ventilation, and 5 (2.5%) died. Extracorporeal membrane oxygenation was performed in 5 patients (0.5%) with severe disease.


The median duration of hospitalization was 12.0 days (mean, 12.8). During hospital admission, most of the patients received a diagnosis of pneumonia from a physician (91.1%), followed by ARDS (3.4%) and shock (1.1%). Patients with severe disease had a higher incidence of physician-diagnosed pneumonia than those with nonsevere disease (99.4% vs. 89.5%).





During the initial phase of the Covid-19 outbreak, the diagnosis of the disease was complicated by the diversity in symptoms and imaging findings and in the severity of disease at the time of presentation. Fever was identified in 43.8% of the patients on presentation but developed in 88.7% after hospitalization. Severe illness occurred in 15.7% of the patients after admission to a hospital. No radiologic abnormalities were noted on initial presentation in 2.9% of the patients with severe disease and in 17.9% of those with nonsevere disease. Despite the number of deaths associated with Covid-19, SARS-CoV-2 appears to have a lower case fatality rate than either SARS-CoV or Middle East respiratory syndrome–related coronavirus (MERS-CoV). Compromised respiratory status on admission (the primary driver of disease severity) was associated with worse outcomes.


Approximately 2% of the patients had a history of direct contact with wildlife, whereas more than three quarters were either residents of Wuhan, had visited the city, or had contact with city residents. These findings echo the latest reports, including the outbreak of a family cluster,4 transmission from an asymptomatic patient,6 and the three-phase outbreak patterns.8 Our study cannot preclude the presence of patients who have been termed “super-spreaders.”


Conventional routes of transmission of SARS-CoV, MERS-CoV, and highly pathogenic influenza consist of respiratory droplets and direct contact,18-20 mechanisms that probably occur with SARS-CoV-2 as well. Because SARS-CoV-2 can be detected in the gastrointestinal tract, saliva, and urine, these routes of potential transmission need to be investigated21 (Tables S1 and S2).


The term Covid-19 has been applied to patients who have laboratory-confirmed symptomatic cases without apparent radiologic manifestations. A better understanding of the spectrum of the disease is needed, since in 8.9% of the patients, SARS-CoV-2 infection was detected before the development of viral pneumonia or viral pneumonia did not develop.


In concert with recent studies,1,8,12 we found that the clinical characteristics of Covid-19 mimic those of SARS-CoV. Fever and cough were the dominant symptoms and gastrointestinal symptoms were uncommon, which suggests a difference in viral tropism as compared with SARS-CoV, MERS-CoV, and seasonal influenza.22,23 The absence of fever in Covid-19 is more frequent than in SARS-CoV (1%) and MERS-CoV infection (2%),20 so afebrile patients may be missed if the surveillance case definition focuses on fever detection.14 Lymphocytopenia was common and, in some cases, severe, a finding that was consistent with the results of two recent reports.1,12 We found a lower case fatality rate (1.4%) than the rate that was recently reportedly,1,12 probably because of the difference in sample sizes and case inclusion criteria. Our findings were more similar to the national official statistics, which showed a rate of death of 3.2% among 51,857 cases of Covid-19 as of February 16, 2020.11,24 Since patients who were mildly ill and who did not seek medical attention were not included in our study, the case fatality rate in a real-world scenario might be even lower. Early isolation, early diagnosis, and early management might have collectively contributed to the reduction in mortality in Guangdong.


Despite the phylogenetic homogeneity between SARS-CoV-2 and SARS-CoV, there are some clinical characteristics that differentiate Covid-19 from SARS-CoV, MERS-CoV, and seasonal influenza infections. (For example, seasonal influenza has been more common in respiratory outpatient clinics and wards.) Some additional characteristics that are unique to Covid-19 are detailed in Table S3.


Our study has some notable limitations. First, some cases had incomplete documentation of the exposure history and laboratory testing, given the variation in the structure of electronic databases among different participating sites and the urgent timeline for data extraction. Some cases were diagnosed in outpatient settings where medical information was briefly documented and incomplete laboratory testing was performed, along with a shortage of infrastructure and training of medical staff in nonspecialty hospitals. Second, we could estimate the incubation period in only 291 of the study patients who had documented information. The uncertainty of the exact dates (recall bias) might have inevitably affected our assessment. Third, because many patients remained in the hospital and the outcomes were unknown at the time of data cutoff, we censored the data regarding their clinical outcomes as of the time of our analysis. Fourth, we no doubt missed patients who were asymptomatic or had mild cases and who were treated at home, so our study cohort may represent the more severe end of Covid-19. Fifth, many patients did not undergo sputum bacteriologic or fungal assessment on admission because, in some hospitals, medical resources were overwhelmed. Sixth, data generation was clinically driven and not systematic.


Covid-19 has spread rapidly since it was first identified in Wuhan and has been shown to have a wide spectrum of severity. Some patients with Covid-19 do not have fever or radiologic abnormalities on initial presentation, which has complicated the diagnosis.



TLDR : l'étude porte sur 1099 patients chinois suivis jusqu'au 29/01/20. Les résultats sont détaillés dans ce tableau (note : primary composite end point correspond à l'admission en soins intensifs, l'utilisation d'un respirateur ou le décès) :




En résumé :


- les plus de 50 ans sont les plus touchés (scoop)

- l'âge médian est 47 ans. Les enfants ne sont pratiquement pas malades, les femmes un peu moins que les hommes.

- le tabac accroît le risque de complications

- la période d'incubation moyenne est de 4 jours

- 44% avaient de la fièvre mais la plupart ne dépassaient pas 37,5° lors de leur admission

- les principaux symptômes sont : toux (68%), sécrétions (34%), épuisement (38%), souffle court (19%). Quelquefois des frissons et/ou des céphalées.

- la majorité des cas graves avait des antécédents, en particulier le diabète (et dans une moindre mesure l'hypertension).

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Traitements et issues :




- les patients ont développé une pneumonie (91% des cas) en moyenne 3 jours après l'apparition des symptômes.

- la durée d'hospitalisation moyenne était de 12 jours

- 58% ont été mis sous antibios, 6% sous un respirateur artificiel

- seulement 15 décès (<1.5%)

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Une intéressante étude sur la dispersion du taux de reproduction de base qui a une connexion avec la loi de Pareto: https://hopkinsidd.github.io/nCoV-Sandbox/DispersionExploration.html

en gros tout le monde ne contamine pas autant de gens et seule une minorité de contaminés est responsable de la contamination ce qui fait qu'en s'attaquant à 20% des causes, on résout 80% du problème:


(A) onward transmission of the virus is less likely outside of China, presumably due to case finding paired with isolation and quarantine, i.e. the effective reproductive number Re is reduced; or (B) transmission of COVID-19 is in general overdispersed, i.e., the majority of transmission is due to a few superspreading events, while the vast majority of infected individuals do not transmit the virus. Perhaps most likely is that we are seeing some combination of these two effects.

We then find the optimal dispersion parameter, θ, that best makes each assumed simulated data set consistent with a random value of Re drawn from a plausible range (0.1 - 3). Individual variation in infectiousness implies outbreaks are rarer but more explosive. Interpreting the θ parameter is eased by framing it in terms of the fraction of individuals responsible for 80% of onward transmission (by analogy with the 20/80 rule).

This suggests we should be cautious about assuming the relative lack of COVID-19 transmission outside of China is the result of effective control measures, or some other fundamental difference in COVID-19 transmission outside of Wuhan (and China more broadly).

La mise à jour des données déforme un peu la loi des 80/20 (vers 80/10).

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@Tipiak Effectivement, il est tentant de prendre la courbe de l'épidémie en Chine et de la calquer en Europe en l'ajustant à la population. Néanmoins, il y a un écueil : les mesures prises par les autorités chinoises ont été précoces et extrêmement draconiennes. Par exemple, la province de Hubei (57 millions de personnes) a été placée en quarantaine le 23/01 alors qu'il y avait 550 cas en Chine (source). Plusieurs pays ont dépassé ce stade (Japon, Corée du Sud, Italie, Iran, peut-être les États-Unis si des cas sont passés sous le radar) et leur population n'est pas confinée. À mon avis, rien ne permet d'affirmer que tout va se passer comme en Chine. Quant à la charge des hôpitaux, je n'en ai pas la moindre idée... le personnel hospitalier se plaint déjà d'être en sous-effectif.


Autre chose, comme @Vilfredo Pareto le souligne dans son message : s'il y a des superspreaders qu'on ne bloque pas à temps cela change aussi la donne.

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Un site actualise chaque jour le nombre de cas de coronavirus à travers le monde. Il comptabilise aussi les personnes décédées ainsi que les personnes guéries.



Un site comptabilise le nombre de personnes contaminées, décédées et guéries du coronavirus 2019n-CoV.


Le bilan de l'épidémie du coronavirus 2019n-CoV évolue sans arrêt. À l'heure où cet article est écrit, on compte plus de 80.000 cas et plus de 2.700 décès [bilan daté du 26 février à 11h45]. Pour avoir toute la situation sous les yeux, l'Université Johns Hopkins, plus précisément le Center for systems science and engineering (CSSE) a mis au point un site internet qui dénombre en direct le nombre de cas de contamination, de décès mais aussi de guérisons à travers le monde. Sous forme d'un tableau de bord, le site permet de comprendre la situation en un coup d'œil. 


Un décompte ville par ville des cas de coronavirus

Le tableau de bord comprend une carte qui permet de voir continent par continent, les pays et même les villes qui ont été touchées. Il propose aussi une courbe sur l'évolution de la situation depuis le 20 janvier 2020. Sur une note plus positive, il décompte aussi, en vert, le nombre de personnes qui sont officiellement guéries du coronavirus à travers le monde. Sur des colonnes de part et d'autres de la carte, les décès, contaminations et guérisons sont détaillées ville par ville à travers la planète


Et le site:


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il y a 35 minutes, Nick de Cusa a dit :

surprenant ; demande fort d'être recoupé et vérifié


Intéressant. Au sujet du recoupement, il y a ce preprint :



The digestive system is a potential route of 2019-nCov infection: a bioinformatics analysis based on single-cell transcriptomes

Posted January 31, 2020




Since December 2019, a newly identified coronavirus (2019 novel coronavirus, 2019-nCov) is causing outbreak of pneumonia in one of largest cities, Wuhan, in Hubei province of China and has draw significant public health attention. The same as severe acute respiratory syndrome coronavirus (SARS-CoV), 2019-nCov enters into host cells via cell receptor angiotensin converting enzyme II (ACE2). In order to dissect the ACE2-expressing cell composition and proportion and explore a potential route of the 2019-nCov infection in digestive system infection, 4 datasets with single-cell transcriptomes of lung, esophagus, gastric, ileum and colon were analyzed. The data showed that ACE2 was not only highly expressed in the lung AT2 cells, esophagus upper and stratified epithelial cells but also in absorptive enterocytes from ileum and colon. These results indicated along with respiratory systems, digestive system is a potential routes for 2019-nCov infection. In conclusion, this study has provided the bioinformatics evidence of the potential route for infection of 2019-nCov in digestive system along with respiratory tract and may have significant impact for our healthy policy setting regards to prevention of 2019-nCoV infection.


Cet article :



Whopping big viruses prey on human gut bacteria

January 28, 2019


Viruses plague bacteria just as viruses like influenza plague humans.


Some of the largest of these so-called bacteriophages have now been found in the human gut, where they periodically devastate bacteria just as seasonal outbreaks of flu lay humans low, according to a new study led by UC Berkeley scientists.


Et j'ai l'impression que c'est cet auteur qui en parle : https://scholar.google.com/citations?hl=en&user=6ZmR8DsAAAAJ&view_op=list_works&sortby=pubdate


Il est bioinformaticien à UC Davis. Voici ce qu'il écrit sur Twitter :



Et voici ses articles (non publiés pour l'instant) :



The 2019 Wuhan outbreak could be caused by the bacteria Prevotella, which is aided by the coronavirus, possibly to adhere to epithelial cells-Prevotella is present in huge…

Authors: Sandeep Chakraborty
Publication date: 2020/2
Publisher: OSF Preprints
A hitherto unknown cause of the Wuhan coronavirus outbreak [1–3] is reported here-a bacteria from the Prevotella genus. The number of Wuhan coronavirus deaths in mainland China has overtaken the SARS epidemic in the country. The high mortality is being caused by targeting only the virus (which is also present). This is a two pronged attack, as previously noted in ‘infection with human coronavirus NL63 enhances streptococcal adherence to epithelial cells ‘[6]. Prevotella is a well known pathogen, and can induce ‘Severe Bacteremic Pneumococcal Pneumonia in Mice with Upregulated Platelet-Activating Factor Receptor Expression’[7]. The RNA-seq data from Wuhan, China (PRJNA603194) has millions of reads of Prevotella proteins, and a few thousands from 2019-nCoV (Table 1). Similarly, the DNA sequences (PRJNA601630) of 6 patients from the same family in Hong Kong [3] shows significant presence of this bacteria. These sequences can be found at SI: China. RNA-seq/SampleSequences. fa (n= 480K) and SI: HongKong/ALLsequences. fa (n= 50k). Finally, the expression levels (Table 2) shows that the elongation factor Tu is the most expressed.‘Elon-gation factor Tu (Tuf) is a new virulence factor of Streptococcus pneumoniae that binds human complement factors, aids in immune evasion and host tissue invasion’[8]. These are the only two studies I could find. Detection of the Prevotella in other samples will add more credence to this theory. Detection of the nCoV can be made very specific by looking for a 500bp in the spike protein [4], which would be a good candidate for vaccine development, protein-inhibition and diagnosis …



The Wuhan coronavirus has integrated in Prevotella, which possibly causes the observed extreme virulence-as sequencing data from 2 different studies in China and Hong-Kong…

Authors: Sandeep Chakraborty
Publication date: 2020/2
Publisher: OSF Preprints
The death toll from the coronavirus outbreak originating in Wuhan (nCov [1–3]) has not abated, rising to 490, more than deaths in SARS outbreak of 2002-2003 in mainland China [4]. Previously, the existence of Prevotella and nCoV reads in copious amounts in sequencing data from Wuhan, China (PRJNA603194) and from sequences of 6 patients from the same family in Hong Kong [3] was established [5]. This also showed elevated levels of the virulence elongation factor Tu [6]. Here, I show that nCoV has integrated in the Prevotella genome, which interestingly has two chromosomes (https://www. ncbi. nlm. nih. gov/assembly/GCA 002849795.1),


Authors: Sandeep Chakraborty
Publication date: 2020/2
Publisher: OSF Preprints
The Wuhan outbreak is widely assumed to be caused by a coronavirus [1, 2]. I have reported the presence of Prevotella in all three available sequencing data sets till date [3]-2 from China [4, 5], and one from Hong-Kong [6]. SI Tables in the paper submitted data shows the abundance of the bacteria, but there is mention of this in the paper [4]. Similarly, another paper from China does not report Prevotella among the metagenomic bacteria, but it is clearly present [5]. But the biggest proof that this is the cause of outbreak is the integration of the nCov and Prevotella, at the exact same place, from data in China and Hong-Kong [7]. And this is exactly the reason for the very high false negatives. We are looking for RNA (see the CDC test details given below)-which will be detected when the bacteria makes RNA out of the regions where the nCoV is, and will be high only when the bacterial concentration is sufficiently high. But, then it is too late. Whistle blower Dr Li Wenliang, who tragically passed away, took 20 days for a+ ve nCoV test [8].


Authors: Sandeep Chakraborty
Publication date: 2020/2
Publisher: OSF Preprints
I have hypothesized based on sequencing data-two from China [1, 2], and one from Hong-Kong [3]-that the SARS-CoV-2 has integrated in the Prevotella genome [4, 4, 5]. The only fly in the ointment in this theory is chimeric reads arising from 16S integrations [6]. These chimeric reads in that region, although some of the are within proteins (SI. plasmid: chemeric. inprotein. fa). Apart from this, this hypothesis (a chimeric bacteria/virus) explains many of the intriguing observations-the extremely high false negatives (this is now DNA, while we are looking for RNA)[7, 8], high incubation periods [9], abdominal problems presenting before respiratory problems [10]. All these, taken one-by-one, probably happen in many viral diseases. But the combination of all observations strongly indicates this is a bacteria+ virus. Here, I report plasmid reads encoding β-lactamases in patient sequencing data from China [2] and Hong-Kong [3]. It is not there in the other Chinese study [1]. It is a distant possibility that these reads are from bacteria, given the high homology to plasmids (Fig 1). The origin of these reads, very unlikely to be contamination, in two different geographical locations needs serious investigation. It might also help in choosing the anti-biotics being prescribed. These sequences can be obtained from SI. plasmid/Study2. HK. blase. fa (N= 62) and SI. plasmid/Study3. China. blase. fa (N= 35).


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Il y a 16 heures, Alchimi a dit :


C'est le site le plus consulté par les personnes qui s'intéressent à l'épidémie et, je crois, le premier de la sorte mis en ligne.

Pour ceux que ça intéresse, il y en d'autres :


Celui de l'Université de Washington permet de suivre l'évolution pays par pays :



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  • 2 weeks later...

First known person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the USA





Coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first detected in China in December, 2019. In January, 2020, state, local, and federal public health agencies investigated the first case of COVID-19 in Illinois, USA.




Patients with confirmed COVID-19 were defined as those with a positive SARS-CoV-2 test. Contacts were people with exposure to a patient with COVID-19 on or after the patient’s symptom onset date. Contacts underwent active symptom monitoring for 14 days following their last exposure. Contacts who developed fever, cough, or shortness of breath became persons under investigation and were tested for SARS-CoV-2. A convenience sample of 32 asymptomatic health-care personnel contacts were also tested.




Patient 1—a woman in her 60s—returned from China in mid-January, 2020. One week later, she was hospitalised with pneumonia and tested positive for SARS-CoV-2. Her husband (Patient 2) did not travel but had frequent close contact with his wife. He was admitted 8 days later and tested positive for SARS-CoV-2. Overall, 372 contacts of both cases were identified  ; 347 underwent active symptom monitoring, including 152 community contacts and 195 health-care personnel. Of monitored contacts, 43 became persons under investigation, in addition to Patient 2. These 43 persons under investigation and all 32 asymptomatic health-care personnel tested negative for SARS-CoV-2.




Person-to-person transmission of SARS-CoV-2 occurred between two people with prolonged, unprotected exposure while Patient 1 was symptomatic. Despite active symptom monitoring and testing of symptomatic and some asymptomatic contacts, no further transmission was detected.


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Il y a 2 heures, Rübezahl a dit :

Un site pour le suivi (chiffres et graphiques) https://www.worldometers.info/coronavirus/


Pour les stats, il y a aussi : https://github.com/CSSEGISandData/COVID-19

Les données sont celles de Johns Hopkins.


Et aussi : https://github.com/GuangchuangYu/nCov2019

« An R package and a website with real-time data on the COVID-19 coronavirus outbreak »


En python : https://github.com/AaronWard/covid-19-analysis

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Adam Kucharski
Mathematician/epidemiologist at @LSHTM. @WellcomeTrust fellow and @TEDFellow. Author of The Rules of Contagion: http://kucharski.io/books/



I am deeply uncomfortable with the message that UK is actively pursuing ‘herd immunity’ as the main COVID-19 strategy. Our group’s scenario modelling has focused on reducing two main things: peak healthcare demand and deaths... 1/


For me, herd immunity has never been the outright aim, it’s been a tragic consequence of having a virus that - based on current evidence - is unlikely to be fully controllable in long term in the UK. 2/


Sadly, even large-scale changes (like those other European countries are making, and we may very soon) may not control COVID for long. We must flatten the curve as much as possible, but there could still be many infections (and hence immunity). 3/


The communication about COVID science has generally been clear in the UK, but talk of ‘herd immunity as the aim’ is totally wide of the mark. Having large numbers infected isn’t the aim here, even if it may be the outcome. 4/


A lot of modellers around the world are working flat out to find best way to minimise impact on population and healthcare. A side effect may end up being herd immunity, but this is merely a consequence of a very tough option - albeit one that may help prevent another outbreak. 5/


Clearly we cannot finely tune the path of this outbreak. The best we can do is identify actions that have highest chance of effectively and sustainably reducing impact on the population and burden on NHS. 6/


To be clear: we have to reduce impact on UK as much as we can. But we are in this for the long term. A couple of weeks of closed schools and cancelled events won’t solve this - we will have to fundamentally change our lifestyles. 7/


Given the seriousness of the situation, we are obviously working to get our latest modelling analysis out in the public domain as soon as we can. 8/8


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6 minutes ago, Freezbee said:

we will have to fundamentally change our lifestyles

Et c'est là que je ressens comme un malaise, comme une impression que le gars qui écrit ça a une demi-molle à l'idée de pouvoir imposer plein de trucs à plein de gens.

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Professor Ian Donald



Psychologist:Social & Environmental research; behavioural factors in Anti-Microbial Resistance. Emeritus Professor, University of Liverpool. Typos all my own


1. The govt strategy on #Coronavirus is more refined than those used in other countries and potentially very effective. But it is also riskier and based on a number of assumptions. They need to be correct, and the measures they introduce need to work when they are supposed to.


2. This all assumes I'm correct in what I think the govt are doing and why. I could be wrong - and wouldn't be surprised. But it looks to me like. . .


3. A UK starting assumption is that a high number of the population will inevitably get infected whatever is done – up to 80%. As you can’t stop it, so it is best to manage it.

There are limited health resources so the aim is to manage the flow of the seriously ill to these.


4. The Italian model the aims to stop infection. The UKs wants infection BUT of particular categories of people. The aim of the UK is to have as many lower risk people infected as possible. Immune people cannot infect others; the more there are the lower the risk of infection


5. That's herd immunity.

Based on this idea, at the moment the govt wants people to get infected, up until hospitals begin to reach capacity. At that they want to reduce, but not stop infection rate. Ideally they balance it so the numbers entering hospital = the number leaving.


6. That balance is the big risk.

All the time people are being treated, other mildly ill people are recovering and the population grows a higher percent of immune people who can’t infect. They can also return to work and keep things going normally - and go to the pubs.


7.The risk is being able to accurately manage infection flow relative to health case resources. Data on infection rates needs to be accurate, the measures they introduce need to work and at the time they want them to and to the degree they want, or the system is overwhelmed.


8. Schools: Kids generally won’t get very ill, so the govt can use them as a tool to infect others when you want to increase infection. When you need to slow infection, that tap can be turned off – at that point they close the schools. Politically risky for them to say this.


9. The same for large scale events - stop them when you want to slow infection rates; turn another tap off. This means schools etc are closed for a shorter period and disruption generally is therefore for a shorter period, AND with a growing immune population. This is sustainable


10. After a while most of the population is immune, the seriously ill have all received treatment and the country is resistant. The more vulnerable are then less at risk. This is the end state the govt is aiming for and could achieve.


11. BUT a key issue during this process is protection of those for whom the virus is fatal. It's not clear the full measures there are to protect those people. It assumes they can measure infection, that their behavioural expectations are met - people do what they think they will


12. The Italian (and others) strategy is to stop as much infection as possible - or all infection. This is appealing, but then what? The restrictions are not sustainable for months. So the will need to be relaxed. But that will lead to reemergence of infections.


13. Then rates will then start to climb again. So they will have to reintroduce the restrictions each time infection rates rise. That is not a sustainable model and takes much longer to achieve the goal of a largely immune population with low risk of infection of the vulnerable


14. As the government tries to achieve equilibrium between hospitalisations and infections, more interventions will appear. It's perhaps why there are at the moment few public information films on staying at home. They are treading a tight path, but possibly a sensible one.


15. This is probably the best strategy, but they should explain it more clearly. It relies on a lot of assumptions, so it would be good to know what they are - especially behavioural. Most encouraging, it's way too clever for #BorisJohnson to have had any role in developing.


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Très bel article du Washington Post (mais il faut aller sur leur site pour profiter des animations) :





Pourquoi des épidémies comme celle des coronavirus se propagent de manière exponentielle et comment « aplatir la courbe » ?

Par Harry Stevens 14 mars 2020


Après l'annonce du premier cas de covid-19, la maladie causée par la nouvelle souche de coronavirus, aux États-Unis, les rapports faisant état d'autres infections se sont fait attendre. Deux mois plus tard, ce ruissellement s'est transformé en un courant régulier.




Cette courbe dite exponentielle inquiète les experts. Si le nombre de cas devait continuer à doubler tous les trois jours, il y aurait environ cent millions de cas aux États-Unis d'ici le mois de mai.


C'est un calcul, pas une prophétie. La propagation peut être ralentie, disent les professionnels de la santé publique, si les gens pratiquent la « distanciation sociale » en évitant les espaces publics et en limitant généralement leurs déplacements.


Pourtant, sans aucune mesure pour le ralentir, la covid-19 continuera à se propager de façon exponentielle pendant des mois. Pour comprendre pourquoi, il est instructif de simuler la propagation d'une fausse maladie au sein d'une population.


Nous appellerons notre fausse maladie « simulite ». Elle se propage encore plus facilement que la covid-19 : chaque fois qu'une personne saine entre en contact avec une personne malade, la personne saine devient malade elle aussi.




Dans une population de seulement cinq personnes, il n'a pas fallu longtemps pour que tout le monde attrape la simulite.


Dans la vie réelle, bien sûr, les gens finissent par s'en remettre. Une personne guérie ne peut ni transmettre la simulite à une personne saine, ni retomber malade après avoir été en contact avec une personne malade.




Voyons ce qui se passe lorsque la simulite se répand dans une ville de 200 personnes. Nous allons commencer par placer tout le monde dans la ville à un endroit aléatoire, en nous déplaçant à un angle aléatoire, et nous rendrons une personne malade.


Remarquez comment la pente de la courbe rouge, qui représente le nombre de personnes malades, augmente rapidement à mesure que la maladie se répand, puis diminue progressivement à mesure que les gens se rétablissent.




Notre ville de simulation est petite - environ la taille de Whittier, en Alaska - de sorte que la simulite a pu se répandre rapidement dans toute la population. Dans un pays comme les États-Unis, avec ses 330 millions d'habitants, la courbe pourrait s'accentuer pendant longtemps avant de commencer à ralentir.


En ce qui concerne le véritable covid-19, nous préférerions ralentir la propagation du virus avant qu'il n'infecte une grande partie de la population américaine. Pour ralentir la simulite, essayons de créer une quarantaine forcée, comme celle que le gouvernement chinois a imposée à la province de Hubei, le ground zero du covid-19.




Oups ! Comme les experts de la santé s'y attendent, il s'est avéré impossible de séparer complètement la population malade des personnes en bonne santé.


Leana Wen, ancienne commissaire à la santé de la ville de Baltimore, a expliqué au Washington Post, en janvier dernier, l'impraticabilité des quarantaines forcées. « Beaucoup de gens travaillent dans la ville et vivent dans les comtés voisins, et vice versa », a déclaré Wen. « Les gens seraient-ils séparés de leur famille ? Comment toutes les routes seraient-elles bloquées ? Comment les fournitures arriveraient-elles aux habitants ? »


Comme l'a dit Lawrence O. Gostin, professeur de droit de la santé mondiale à l'université de Georgetown : « La vérité est que ce genre de verrouillage est très rare et jamais efficace. »


Heureusement, il existe d'autres moyens de ralentir une épidémie. Avant tout, les responsables de la santé ont encouragé les gens à éviter les rassemblements publics, à rester chez eux plus souvent et à garder leurs distances avec les autres. Si les gens sont moins mobiles et interagissent moins les uns avec les autres, le virus a moins de chances de se propager.


Certaines personnes continueront à sortir. Peut-être ne peuvent-elles pas rester à la maison en raison de leur travail ou d'autres obligations, ou peut-être refusent-elles simplement de tenir compte des avertissements de santé publique. Non seulement ces personnes sont plus susceptibles de tomber malades elles-mêmes, mais elles risquent aussi de propager la simulite.


Voyons ce qui se passe lorsqu'un quart de notre population continue à se déplacer tandis que les trois autres quarts adoptent une stratégie de ce que les experts de la santé appellent la « distanciation sociale ».




Une plus grande distance sociale maintient encore plus de personnes en bonne santé, et les gens peuvent être poussés à quitter les lieux publics en leur enlevant leur attrait.


« Nous contrôlons le désir d'être dans les lieux publics en fermant les espaces publics. L'Italie est en train de fermer tous ses restaurants. La Chine ferme tout, et nous fermons aussi des choses maintenant », a déclaré Drew Harris, chercheur en santé des populations et professeur adjoint à la faculté de santé publique de l'université Thomas Jefferson. « Réduire les possibilités de se réunir aide les gens à garder une distance sociale ».


Pour simuler une plus grande distanciation sociale, au lieu de laisser un quart de la population se déplacer, nous verrons ce qui se passe lorsque nous laissons seulement une personne sur huit se déplacer.




Les quatre simulations que vous venez de voir - une mêlée générale, une tentative de quarantaine, une distanciation sociale modérée et une distanciation sociale importante - étaient aléatoires. Cela signifie que les résultats de chacune d'entre elles étaient uniques à votre lecture de cet article ; si vous faites défiler les simulations vers le haut et les refaites, ou si vous revenez sur cette page plus tard, vos résultats changeront.


Même avec des résultats différents, une distanciation sociale modérée sera généralement plus efficace que la tentative de quarantaine, et une distanciation sociale étendue est généralement la plus efficace. Vous trouverez ci-dessous une comparaison de vos résultats.




La simulite n'est pas une covid-19, et ces simulations simplifient considérablement la complexité de la vie réelle. Pourtant, tout comme les simultis se répandent dans les réseaux de balles rebondissantes sur votre écran, la covid-19 se répand dans nos réseaux humains - dans nos pays, nos villes, nos lieux de travail, nos familles. Et, comme une balle qui rebondit sur l'écran, le comportement d'une seule personne peut provoquer des effets d'entraînement qui touchent des personnes éloignées.


Sur un point crucial, cependant, ces simulations ne ressemblent en rien à la réalité : Contrairement à la simulite, le covid-19 peut tuer. Bien que le taux de mortalité ne soit pas précisément connu, il est clair que les membres âgés de notre communauté sont les plus exposés au risque de mourir de la covid-19.


« Si vous voulez que ce soit plus réaliste », a déclaré M. Harris après avoir vu un aperçu de cette histoire, « certains points devraient disparaître ».


Traduit avec www.DeepL.com/Translator (version gratuite)


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@Rübezahl Je n'ai pas réussi à trouver la source de ce graphique, et l'auteur du tweet a déjà attiré mon attention en répandant de fausses nouvelles (ou de façon prématurée). Ça paraît plausible, mais je préfère attendre d'en savoir plus.


edit - j'ai trouvé : https://tineye.com/search/e321805411857a273c21fbd379df82752ce8520c?page=1



The diagrams and figures reported in the following are based on statistics reported by the Korean news agency news1 (screenshot) and the Italian daily newspaper Corriere della Sera



Coronavirus: Why it’s so deadly in Italy
Demographics and why they are a warning to other countries

Andreas Backhaus

Economist. Reader. Writer. Hiker.
Mar 13 · 9 min read


As we study the numbers on the coronavirus cases and the deaths related to COVID-19, a similar question comes up again and again:


Why is the coronavirus causing so many more deaths in Italy than in other countries?


This question relates both to the absolute number of deaths , which is currently exceeded only in China, and to the case fatality rate, which has risen to 6.6% and exceeds any other country in the world.


To make sure we are all on the same page: The case fatality rate of COVID-19 is the number of confirmed deaths due to COVID-19 divided by the total number of confirmed cases of infections with the coronavirus SARS-CoV-2. The case fatality rate (CFR) should not be confused with the mortality rate or death rate (while it often is confused with them), which is simply the total number of deaths that occur during a specific time frame divided by the number of the total population at approximately the same time. Currently, we are more interested in the CFR because we see the number of cases growing and we want to know how many of these diagnosed cases will result in the death of the patients. The CFR is currently at 0.066 or 6.6% in Italy, 2.1% in France, 0.8% in South Korea, and 0.2% in Germany, according to the latest data collected by worldometer. What explains these immense differences?



The cases behind the case fatality rate


Let us assume every country is equally capable of counting the numerator of the CFR, the fatalities due to COVID-19, and will report them accurately; an assumption that is tolerable if we focus on non-authoritarian high-income countries. What do we then need to know about the denominator, the confirmed cases? The strongest predictors of fatality due to COVID-19 are age and pre-existing conditions of the infected. The number of pre-existing conditions is positively correlated with age, so let us for simplicity only look at the age of the confirmed cases. Clearly, because age is so predictive of death by COVID-19, comparing the case fatality rates across countries only makes sense if the underlying cases of coronavirus have approximately the same age across countries.


What do we know about the age of the people that have been found to be infected with the coronavirus? This information is not easy to find, but it has been popping up in reports and newspapers from the various countries over the past days. The diagrams and figures reported in the following are based on statistics reported by the Korean news agency news1 (screenshot) and the Italian daily newspaper Corriere della Sera.


Grouping the age in ten-year-intervals and comparing the percentage shares of cases that fall into each age group reveals a striking dissimilarity between South Korea (red bars) and Italy (green bars): Recently, 3% of all confirmed cases in South Korea were at least 80 years old. At about the same time, 19.1% of all confirmed cases in Italy were at least 80 years old.




This enormous difference occurred while the absolute numbers of confirmed cases overall were similar in the two countries (8,036 in Italy vs 7,134 in South Korea). Consequently, Italy’s healthcare and hospital system had to take care of a much higher number of infected older patients than the South Korean one — patients that need more intensive care and that are simultaneously more likely to pass away.




A clear implication is that the Italian CFR is not comparable to the Korean CFR — the people infected with the coronavirus that enter the Italian CFR are much older than those that enter the Korean CFR, and as older people are much more likely to die of COVID-19, they push the Italian CFR upwards. Another implication is that explaining the different CFRs with differences in the healthcare and hospital systems between Italy and South Korea might be premature — in the current coronavirus crisis, South Korea’s hospitals and intensive care units have never been tested to the extent that Italy’s currently are.

Which CFR is unusual — Italy’s or South Korea’s?


An obvious question that follows is: Why do these age distributions look so different in the two countries? Many people have already pointed out that Italy has an older population than South Korea. The higher Italian CFR might therefore reflect a higher likelihood that an old person becomes infected with the coronavirus simply because there are more old people among the Italian population. We can easily check the plausibility of this argument by comparing the age structure of the coronavirus cases with the age structure of the total population for both countries. The population data are from the United Nations’ World Population Prospect 2019.


In South Korea, the age structure of the coronavirus cases is remarkably similar to the age structure of the population, in particular for the older age groups. The 20–29-year-olds are still hugely overrepresented among the confirmed cases relative to their population share, but their surplus is balanced by the underrepresentation of cases among the 0–9- and 10–19-year-olds. These three youngest age groups face a very low risk of dying from COVID-19. The South Korean CFR is hence not depressed or exaggerated by an under- or overrepresentation of older Koreans among the confirmed cases.




The same is not true for Italy: The share of confirmed cases at age 70–79 exceeds the population share of this age group by more than a factor of two. Among those aged 80 and more, the case share is almost three times as high as the population share. By contrast, young people and hence low-fatality-risk people are visibly underrepresented among the confirmed cases.




Hence, the question remains why the age distribution of cases is shaped so differently in Italy compared to South Korea. It has also been pointed out that the testing procedures for coronavirus in the countries are very different — Italy has predominantly been testing people with symptoms of a coronavirus infection, while South Korea has been testing basically everyone since the outbreak had become apparent. Consequently, South Korea has detected more asymptomatic, but positive cases of coronavirus than Italy, in particular among young people.


A complementary reason is that the Korean outbreak took place mainly among followers of the Shincheonji megachurch/sect in and around the city of Daegu. Possibly, many followers of this movement are of relatively young age, explaining the unusual spike of cases among the 20–29-year-olds once testing intensified around this group. This might have also prevented the virus from spreading extensively among the Korean elderly so far. With regard to Italy, we do not know who spread the virus among the old population of the North — but the surprisingly high number of tourists that have been diagnosed with coronavirus after returning from trips to Northern Italy suggests that the unnoticed and asymptomatic spread of the virus has probably been going on there for quite some time, building up to then ravage the elderly.


The bottom line is that the coronavirus hit Italy and South Korea very differently in terms of age at around the same time and the same level of the outbreak — at least the level that we noticed in terms of confirmed cases — thereby causing a much higher number of deaths in Italy. An implication is that simply tracing the number of confirmed coronavirus cases by country over time, as many graphs and website currently do, is not telling the full story. The raw number of cases is a rather poor predictor of deaths by COVID-19, at least in the short-run. If the virus spreads predominantly among young people, as appears to have been the case in South Korea, there is no immediate risk of collapse to the hospitals. However, if it spreads to the old population, as in Italy, collapse is looming; and it might be a matter of days. When (not if) this happens is another factor that is hard to predict, as some efforts are underway.


Looking beyond Italy and South Korea


From these two rather polar-opposite cases of Italy and South Korea, what can be learned for other countries? Age aggregates for a subsample of the German confirmed cases of coronavirus have been published by the Robert Koch Institute, which is a German federal government agency responsible for disease control and prevention. Let us assume the subsample is representative. The age aggregates are not the same as in the Italian and Korean data, but cases can still be allocated to two groups: those younger than 60 years and those 60 years or older.




Based on this comparison, Germany has even been a bit “luckier” than South Korea for now, as the coronavirus apparently has been spreading among the younger German population. This finding could be reflected in the currently very low German CFR of 0.2%. The concentration of cases of coronavirus among its younger population might have provided Germany with a bit more time to prepare itself for the moment when the number of infected will rise among its elderly. We have to keep in mind that 29% of Germany’s population is at least 60 years old, according to the Federal Statistical Office.


The French National Health Agency has also published age aggregates of the confirmed cases, but the aggregates are not compatible with those of the other countries. Looking at the French data alone suggests that France represents a scenario somewhere between the Korean and the Italian one, as close to 30% of the French confirmed cases are at least 65 years old.




Again, this pattern could be reflected in the current French CFR of 2.1%, which is ten times higher than the German one, but only 2.5 times higher than the Korean one. In absolute terms, France has already had almost as many deaths as South Korea due to COVID-19, and we should not expect the French death toll to stabilize soon. These are still very few data points and unfortunately, the availability of information on the age of confirmed cases will likely decrease as the case numbers grow and the situation might escalate in more countries.

South Korea provides a useful estimate of the CFR — but no guarantee


We can learn something more that is potentially very useful from the Korean statistics. We have seen above that the age distribution of the confirmed cases corresponds rather closely to the age distribution of the overall population in South Korea if we subsume everyone below age 30 into one group where almost nobody dies from COVID-19. At the time of reporting, 50 of the confirmed 7,134 people infected with the coronavirus had died, implying an aggregate CFR of 0.7%. Since then, the Korean CFR has been creeping up to 0.89%. Hence, 1% seems to be a reasonable estimate of the case fatality rate in a high-income country (!) in the absence of any major failures of the hospital and care system (!). This 1% CFR estimate is close to what Dr. Jeremy Faust has been suggesting based on the Diamond Princess cruise ship case.


Clearly, one of the worst conclusions that could be drawn from this is that the various case fatality rates across countries will settle down at 1% eventually all by themselves. They won’t. Due to the hospital system becoming overwhelmed in Northern Italy, we already have excess mortality there that cannot be undone. Germany, with its low share of individuals infected with the coronavirus at higher age, might have gained some valuable time, but this is just a time lag, it is not a restraint to the coronavirus spreading further to the elderly soon. The relatively high and quickly growing case fatality rates in France and especially in Spain suggest that the virus has already infected a large number of older and vulnerable citizens in these countries. Regarding the US, we are still completely in the dark. Everything that is being said about the need for social distancing and in particular the protection of the elderly remains ever so true.


Update 1 (minor): I corrected a typo: It’s “numerator” and not “nominator”, of course. Thanks to Wei-Hwa Huang for pointing me to it.



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a day ago, 7 tweets, 3 min read


I’m sharing a simulation tool I put together for studying COVID19 dynamics and generating visualizations without having to do a bunch of coding yourself. Hoping it can help with your coronavirus-related research and teaching. alhill.shinyapps.io/COVID19seir/ (1/7)
The app is based on an SEIR epidemic model, adapted to include the different possible clinical stages/outcomes of COVID19 infection. All the math is there for anyone who’s interested, and the code (R) is all open source on Github so feel free to edit to fit your own needs (2/7)
The model is parameterized w values taken from the (surprisingly vast) literature - thanks to everyone pumping out the pre-prints! If you don’t like the parameters I chose, no problem - you can interactively change them to anything you like and see how it affects outcomes. (3/7)
Having a model that takes into account clinical progression can be useful for a few different things, like understanding the timescale of an outbreak, or estimating the expected # of “unseen” exposed or mild infections for every severe one you do see. (4/7)
The app has the option to introduce an intervention, so you can generate your own “flatten the curve” scenarios. You can also see what happens if an intervention is stopped too soon. (5/7)
Another section lets you compare cases of different severity levels to the healthcare resources needed to care for them, such as hospital beds or mechanical ventilators. You’ll see that really strong interventions are needed to keep cases under these capacity thresholds. (6/7)
This is still a work in progress and there are lots of limitations to this “simple” model. It's an ODE so no stochastic extinction. Pre-symptomatic transmission, asymptomatic infection, and superspreading aren't included yet. Feedback and ideas appreciated! (7/7)


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