The first case of coronavirus disease 2019 (COVID-19) in Algeria was reported on 25 February 2020. Since then, it has progressed rapidly and the number of cases grow exponentially each day. In this article, we utilize SEIR modelling to forecast COVID-19 outbreak in Algeria under two scenarios by using the real-time data from March 01 to April 10, 2020. In the first scenario: no control measures are put into place, we estimate that the basic reproduction number for the epidemic in Algeria is 2.1, the number of new cases in Algeria will peak from around late May to early June and up to 82% of the Algerian population will likely contract the coronavirus. In the second scenario, at a certain date T, drastic control measures are taken, people are being advised to self-isolate or to quarantine and will be able to leave their homes only if necessary. We use SEIR model with fast change between fully protected and risky states. We prove that the final size of the epidemic depends strongly on the cumulative number of cases at the date when we implement intervention and on the fraction of the population in confinement. Our analysis shows that the longer we wait, the worse the situation will be and this very quickly produces.
Moritz U. G. Kraemer; Chia-Hung Yang; Bernardo Gutierrez; Chieh-Hsi Wu; Brennan Klein; David M. Pigott; Louis du Plessis; Nuno R. Faria; Ruoran Li; William P. Hanage; +7 more
Moritz U. G. Kraemer; Chia-Hung Yang; Bernardo Gutierrez; Chieh-Hsi Wu; Brennan Klein; David M. Pigott; Louis du Plessis; Nuno R. Faria; Ruoran Li; William P. Hanage; John S. Brownstein; Maylis Layan; Alessandro Vespignani; Huaiyu Tian; Christopher Dye; Oliver G. Pybus; Samuel V. Scarpino;
Countries: United Kingdom, France, United Kingdom, United Kingdom, United Kingdom, United Kingdom, United Kingdom
Project: NIH | MIDAS Center for Communic... (1U54GM088558-01), NIH | MIDAS Center for Communic... (1U54GM088558-01)
The ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19. One sentence summary: The spread of COVID-19 in China was driven by human mobility early on and mitigated substantially by drastic control measures implemented since the end of January.
ABSTRACTThe novel coronavirus (SARS-CoV-2) causes pandemic of viral pneumonia. The evolution and mutational events of the SARS-CoV-2 genomes are critical for controlling virulence, transmissibility, infectivity, severity of symptoms and mortality associated to this infectious disease. We collected and investigated 309 SARS-CoV-2 genomes from patients infected in France. Detailed genome cartography of all mutational events (SNPs, indels) was reported and correlated to clinical features of patients. A comparative analysis between our 309 SARS-CoV-2 genomes from French patients and the reference Wuhan coronavirus genome revealed 315 substitution mutations and six deletion events: ten were in 5’/3’ UTR, 178 were nonsynonymous, 126 were synonymous and one generated a stop codon. Six different deleted areas were also identified in nine viral variants. In particular, 30 substitution mutations (18 nonsynonymous) and one deletion (Δ21765-21770) concerned the spike S glycoprotein. An average of 7.8 mutational events (+/- 1.7 SD) and a median of 8 (range, 7-9) were reported per viral isolate. Comparative analyses and clustering of specific mutational signatures in 309 genomes disclose several divisions in groups and subgroups combining their geographical and phylogenetic origin. Clinical outcomes of the 309 COVID-19-infected patients were investigated according to the mutational signatures of viral variants. These findings highlight the genome dynamics of the coronavirus 2019-20 and shed light on the mutational landscape and evolution of this virus. Inclusion of the French cohort enabled us to identify 161 novel mutations never reported in SARS-CoV-2 genomes collected worldwide. These results support a global and continuing surveillance of the emerging variants of the coronavirus SARS-CoV-2.
AbstractCOVID-19 SARS-CoV-2 infection exhibits wide inter-individual clinical variability, from silent infection to severe disease and death. The identification of high-risk patients is a continuing challenge in routine care. We aimed to identify factors that influence clinical worsening. We analyzed 52 cell populations, 71 analytes, and RNA-seq gene expression in the blood of severe patients from the French COVID cohort upon hospitalization (n = 61). COVID-19 patients showed severe abnormalities of 27 cell populations relative to healthy donors (HDs). Forty-two cytokines, neutrophil chemo-attractants, and inflammatory components were elevated in COVID-19 patients. Supervised gene expression analyses showed differential expression of genes for neutrophil activation, interferon signaling, T- and B-cell receptors, EIF2 signaling, and ICOS-ICOSL pathways in COVID-19 patients. Unsupervised analysis confirmed the prominent role of neutrophil activation, with a high abundance of CD177, a specific neutrophil activation marker. CD177 was the most highly differentially-expressed gene contributing to the clustering of severe patients and its abundance correlated with CD177 protein serum levels. CD177 levels were higher in COVID-19 patients from both the French and “confirmatory” Swiss cohort (n = 203) than in HDs (P< 0.01) and in ICU than non-ICU patients (P< 0.001), correlating with the time to symptoms onset (P = 0.002). Longitudinal measurements showed sustained levels of serum CD177 to discriminate between patients with the worst prognosis, leading to death, and those who recovered (P = 0.01). These results highlight neutrophil activation as a hallmark of severe disease and CD177 assessment as a reliable prognostic marker for routine care.
AbstractBackgroundCOVID-19 is spreading rapidly in nursing homes (NHs). It is urgent to evaluate the effect of infection prevention and control (IPC) measures to reduce COVID spreading.MethodsWe analysed COVID-19 outbreaks in 12 NH using rRT-PCR for SARS-CoV-2. We estimated secondary attack risks (SARs) and identified cofactors associated with the proportion of infected residents.ResultsThe SAR was below 5%, suggesting a high efficiency of IPC measures. Mask-wearing or establishment of COVID-19 zones for infected residents were associated with lower SAR.ConclusionsWearing masks and isolating potentially infected residents appear to limit SARS-CoV-2 spread in nursing homes.
AbstractThe recent reclassification of theRiboviria, and the introduction of multiple new taxonomic categories including both subfamilies and subgenera for coronaviruses (familyCoronaviridae, subfamilyOrthocoronavirinae) represents a major shift in how official classifications are used to designate specific viral lineages. While the newly defined subgenera provide much-needed standardisation for commonly cited viruses of public health importance, no method has been proposed for the assignment of subgenus based on partial sequence data, or for sequences that are divergent from the designated holotype reference genomes. Here, we describe the genetic variation of a partial region of the coronavirus RNA-dependent RNA polymerase (RdRp), which is one of the most used partial sequence loci for both detection and classification of coronaviruses in molecular epidemiology. We infer Bayesian phylogenies from more than 7000 publicly available coronavirus sequences and examine clade groupings relative to all subgenus holotype sequences. Our phylogenetic analyses are largely coherent with genome-scale analyses based on designated holotype members for each subgenus. Distance measures between sequences form discrete clusters between taxa, offering logical threshold boundaries that can attribute subgenus or indicate sequences that are likely to belong to unclassified subgenera both accurately and robustly. We thus propose that partial RdRp sequence data of coronaviruses is sufficient for the attribution of subgenus-level taxonomic classifications and we supply the R package, “MyCoV”, which provides a method for attributing subgenus and assessing the reliability of the attribution.Importance StatementThe analysis of polymerase chain reaction amplicons derived from biological samples is the most common modern method for detection and classification of infecting viral agents, such as Coronaviruses. Recent updates to the official standard for taxonomic classification of Coronaviruses, however, may leave researchers unsure as to whether the viral sequences they obtain by these methods can be classified into specific viral taxa due to variations in the sequences when compared to type strains. Here, we present a plausible method for defining genetic dissimilarity cut-offs that will allow researchers to state which taxon their virus belongs to and with what level of certainty. To assist in this, we also provide the R package ‘MyCoV’ which classifies user generated sequences.
SARS-CoV-2 outbreak is the first pandemic of the century. SARS-CoV-2 infection is transmitted through droplets; other transmission routes are hypothesized but not confirmed. So far, it is unclear whether and how SARS-CoV-2 can be transmitted from the mother to the fetus. We demonstrate the transplacental transmission of SARS-CoV-2 in a neonate born to a mother infected in the last trimester and presenting with neurological compromise. The transmission is confirmed by comprehensive virological and pathological investigations. In detail, SARS-CoV-2 causes: (1) maternal viremia, (2) placental infection demonstrated by immunohistochemistry and very high viral load; placental inflammation, as shown by histological examination and immunohistochemistry, and (3) neonatal viremia following placental infection. The neonate is studied clinically, through imaging, and followed up. The neonate presented with neurological manifestations, similar to those described in adult patients. Congenital infection of SARS-CoV-2 has been described, but the transmission routes remain unclear. Here, the authors report evidence of transplacental transmission of SARS-CoV-2 in a neonate born to a mother infected in the last trimester and presenting with neurological compromise.
International audience; Estimation of dynamical systems - in particular, identification of their parameters - is fundamental in computational biology, e.g., pharmacology, virology, or epidemiology, to reconcile model runs with available measurements. Unfortunately, the mean and variance priorities of the parameters must be chosen very appropriately to balance our distrust of the measurements when the data are sparse or corrupted by noise. Otherwise, the identification procedure fails. One option is to use repeated measurements collected in configurations with common priorities - for example, with multiple subjects in a clinical trial or clusters in an epidemiological investigation. This shared information is beneficial and is typically modeled in statistics using nonlinear mixed-effects models. In this paper, we present a data assimilation method that is compatible with such a mixed-effects strategy without being compromised by the potential curse of dimensionality. We define population-based estimators through maximum likelihood estimation. We then develop an equivalent robust sequential estimator for large populations based on filtering theory that sequentially integrates data. Finally, we limit the computational complexity by defining a reduced-order version of this population-based Kalman filter that clusters subpopulations with common observational backgrounds. The performance of the resulting algorithm is evaluated against classical pharmacokinetics benchmarks. Finally, the versatility of the proposed method is tested in an epidemiological study using real data on the hospitalisation of COVID-19 patients in the regions and departments of France.
Understanding the pathogenesis of the SARS-CoV-2 infection is key to developing preventive and therapeutic strategies against COVID-19, in the case of severe illness but also when the disease is mild. The use of appropriate experimental animal models remains central in the in vivo exploration of the physiopathology of infection and antiviral strategies. This study describes SARS-CoV-2 intranasal infection in ferrets and hamsters with low doses of low-passage SARS-CoV-2 clinical French isolate UCN19, describing infection levels, excretion, immune responses and pathological patterns in both animal species. Individual infection with 103 p.f.u. SARS-CoV-2 induced a more severe disease in hamsters than in ferrets. Viral RNA was detected in the lungs of hamsters but not of ferrets and in the brain (olfactory bulb and/or medulla oblongata) of both species. Overall, the clinical disease remained mild, with serological responses detected from 7 days and 10 days post-inoculation in hamsters and ferrets respectively. The virus became undetectable and pathology resolved within 14 days. The kinetics and levels of infection can be used in ferrets and hamsters as experimental models for understanding the pathogenicity of SARS-CoV-2, and testing the protective effect of drugs.