We present a timely evaluation of the impact of lockdown on the 2019-nCov epidemic in Hubei province, China. The implementation appears to be effective in reducing about 60% of new infections and deaths MESHD, and its effect also appears to be sustainable even after its removal. Delaying its implementation reduces its effectiveness. However, the direct economic cost of such a lockdown remains to be seen and whether the model is replicable in other Chinese regions remains a matter of further investigation.

The outbreak of the 2019 Novel Coronavirus (2019-nCoV) has rapidly spread from Wuhan, China to multiple countries, causing staggering number of infections and deaths MESHD. A systematic profiling of the immune vulnerability landscape of 2019-nCoV is lacking, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development. In this study, we predicted the potential of all the 2019-nCoV viral proteins to induce class I and II MHC presentation and form linear antibody epitopes. We showed that the enrichment for T cell and B cell epitopes is not uniform on the viral genome, with several focused regions that generate abundant epitopes and may be more targetable. We showed that genetic variations in 2019-nCoV, though fewer for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus, which should be considered in the development of therapies. We create an online database to broadly share our research outcome. Overall, we present an immunological resource for 2019-nCoV that could significantly promote both therapeutic development and mechanistic research.

The coronavirus disease 2019 MESHD bursted out about two months ago in Wuhan has caused the death MESHD of more than a thousand people. China is fighting hard against the epidemics with the helps from all over the world. On the other hand, there appear to be doubts on the reported case number. In this article, we propose a test of the reported case number of coronavirus disease 2019 MESHD in China with Newcomb-Benford law. We find a $p$-value of $92.8\%$ in favour that the cumulative case numbers abide by the Newcomb-Benford law. Even though the reported case number can be lower than the real number of affected people due to various reasons, this test does not seem to indicate the detection of frauds.

Utilization of multiple trajectories of a dynamical system model provides us with several benefits in approximation of time series. For short term predictions a high accuracy can be achieved via switches to new trajectory at any time. Different long term trends (tendency to different stationary points) of the phase portrait characterize various scenarios of the process realization influenced by externalities. The dynamical system's phase portrait analysis helps to see if the equations properly describe the reality. We also extend the dynamical systems approach (discussed in \cite{R5}) to the dynamical systems with external control. We illustrate these ideas with the help of new examples of the rental properties HOMES.mil platform data. We also compare the qualitative properties of HOMES.mil and Wikipedia.org platforms' phase portraits and the corresponding differences of the two platforms' users. In our last example with COVID-19 MESHD data we discuss the high accuracy of the short term prediction of confirmed infection cases, recovery cases and death MESHD cases in various countries.

Background. The epidemic outbreak cased by coronavirus 2019-nCoV is of great interest to researches because of the high rate of spread of the infection and the significant number of fatalities. A detailed scientific analysis of the phenomenon is yet to come, but the public is already interested in the questions of the duration of the epidemic, the expected number of patients and deaths MESHD. For long time predictions, the complicated mathematical models are necessary which need many efforts for unknown parameters identification and calculations. In this article, some preliminary estimates will be presented. Objective. Since the reliable long time data are available only for mainland China, we will try to predict the epidemic characteristics only in this area. We will estimate some of the epidemic characteristics and present the most reliable dependences for victim numbers, infected and removed persons versus time. Methods. In this study we use the known SIR model for the dynamics of an epidemic, the known exact solution of the linear equations and statistical approach developed before for investigation of the children disease, which occurred in Chernivtsi (Ukraine) in 1988-1989. Results. The optimal values of the SIR model parameters were identified with the use of statistical approach. The numbers of infected, susceptible and removed persons versus time were predicted. Conclusions. Simple mathematical model was used to predict the characteristics of the epidemic caused by coronavirus 2019-nCoV in mainland China. The further research should focus on updating the predictions with the use of fresh data and using more complicated mathematical models.

Background: Since the first cluster of cases was identified in Wuhan City, China, in December, 2019, coronavirus disease 2019 MESHD ( COVID-19 MESHD) rapidly spread around the world. Despite the scarcity of publicly available data, scientists around the world have made strides in estimating the magnitude of the epidemic, the basic reproduction number, and transmission patterns. Accumulating evidence suggests that a substantial fraction of the infected individuals with the novel coronavirus show little if any symptoms, which highlights the need to reassess the transmission potential of this emerging disease. In this study, we derive estimates of the transmissibility and virulence of COVID-19 MESHD in Wuhan City, China, by reconstructing the underlying transmission dynamics using multiple data sources. Methods: We employ statistical methods and publicly available epidemiological datasets to jointly derive estimates of transmissibility and severity associated with the novel coronavirus. For this purpose, the daily series of laboratory-confirmed COVID-19 MESHD cases and deaths MESHD in Wuhan City together with epidemiological data of Japanese repatriated from Wuhan City on board government-chartered flights were integrated into our analysis. Results: Our posterior estimates of basic reproduction number (R) in Wuhan City, China in 2019-2020 reached values at 3.49 (95%CrI: 3.39-3.62) with a mean serial interval of 6.0 days, and the enhanced public health intervention after January 23rd in 2020 was associated with a significantly reduced R at 0.84 (95%CrI: 0.81-0.88), with the total number of infections (i.e. cumulative infections) estimated at 1906634 (95%CrI: 1373500- 2651124) in Wuhan City, elevating the overall proportion of infected individuals to 19.1% (95%CrI: 13.5-26.6%). We also estimated the most recent crude infection fatality ratio (IFR) and time-delay adjusted IFR at 0.04% (95% CrI: 0.03%-0.06%) and 0.12% (95%CrI: 0.08-0.17%), respectively, estimates that are several orders of magnitude smaller than the crude CFR estimated at 4.06% Conclusions: We have estimated key epidemiological parameters of the transmissibility and virulence of COVID-19 MESHD in Wuhan, China during January-February, 2020 using an ecological modelling approach. The power of this approach lies in the ability to infer epidemiological parameters with quantified uncertainty from partial observations collected by surveillance systems.

The outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths MESHD. A systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking. In this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes. We created an online database to broadly share the predictions as a resource for the research community. Using this resource, we showed that genetic variations in SARS- CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus. Importantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system. Overall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.

A new mysterious coronavirus outbreak started last month in China. The World Health Organization (WHO) termed the new virus strain 2019-nCoV to be the seventh reported human coronaviruses (HCoV). A seafood market in Wuhan city, central China was the starting point of the emergence with unknown animal causes the first animal to human infection. Until today 904 confirmed deaths MESHD and more than 40000 cases confirmed in China and 28 countries. There is a massive fear of the human to human transmission of 2019-nCoV that reported last week by the Chinese government. The most famous two strains of HCoV are the Severe Acute Respiratory Syndrome coronavirus (SARS CoV) and the Middle East Respiratory Syndrome coronavirus (MERS CoV) MESHD. The former had emerged in China in 2002 while the latter emerged in the Middle East region in 2012 and south Korea in 2015. In this study, the newly emerged 2019-nCoV papain-like protease PROTEIN ( PLpro PROTEIN) is targeted by anti-SARS PLpro PROTEIN drugs and the anti- Hepatitis C Virus MESHD (HCV) Non-structural protein 3 PROTEIN (NS3) serine protease drugs. Sequence analysis, modeling, and docking are used to get a valid model for 2019-nCoV PLpro PROTEIN. The results suggest the effectiveness of the anti-SARS drugs (GRL-0667, GRL-0617, and Mycophenolic acid) and the anti-HCV drugs (Grazoprevir, Telaprevir, and Boceprevir) as potent inhibitors against the newly emerged coronavirus.  

Background: Since December 2019, acute respiratory disease MESHD ( ARD MESHD ARD HGNC) due to 2019 novel coronavirus (2019-nCoV) emerged in Wuhan city and rapidly spread throughout China. We sought to delineate the clinical characteristics of these cases. Methods: We extracted the data on 1,099 patients with laboratory-confirmed 2019-nCoV ARD HGNC ARD MESHD from 552 hospitals in 31 provinces/provincial municipalities through January 29th, 2020. Results: The median age was 47.0 years, and 41.90% were females. Only 1.18% of patients had a direct contact with wildlife, whereas 31.30% had been to Wuhan and 71.80% had contacted with people from Wuhan. Fever MESHD (87.9%) and cough (67.7%) were the most common symptoms. Diarrhea MESHD is uncommon. The median incubation period was 3.0 days (range, 0 to 24.0 days). On admission, ground-glass opacity was the typical radiological finding on chest computed tomography (50.00%). Significantly more severe cases were diagnosed by symptoms plus reverse-transcriptase polymerase-chain-reaction without abnormal radiological findings than non-severe cases (23.87% vs. 5.20%, P<0.001). Lymphopenia MESHD was observed in 82.1% of patients. 55 patients (5.00%) were admitted to intensive care unit and 15 (1.36%) succumbed. Severe pneumonia MESHD was independently associated with either the admission to intensive care unit, mechanical ventilation, or death MESHD in multivariate competing-risk model (sub-distribution hazards ratio, 9.80; 95% confidence interval, 4.06 to 23.67). Conclusions: The 2019-nCoV epidemic spreads rapidly by human-to-human transmission. Normal radiologic findings are present among some patients with 2019-nCoV infection MESHD. The disease severity (including oxygen saturation, respiratory rate, blood leukocyte/lymphocyte count and chest X-ray/CT manifestations) predict poor clinical outcomes.

As of February 20, 2020, the 2019 novel coronavirus (renamed to COVID-19 MESHD) spread to 30 countries with 2130 deaths MESHD and more than 75500 confirmed cases. COVID-19 MESHD is being compared to the infamous SARS coronavirus MESHD, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths MESHD. Though COVID-19 MESHD has a death rate of 2.8% as of 20 February, the 75752 confirmed cases in a few weeks (December 8, 2019 to February 20, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 MESHD genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 MESHD genomes. The proposed method combines supervised machine learning with digital signal processing for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearmans rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp. Our results support a hypothesis of a bat origin and classify COVID-19 MESHD as Sarbecovirus, within Betacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.