Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Virology
Title: COVID-19 outbreak in Algeria: A mathematical model to predict the incidence
Abstract: Abstract Introduction Since December 29, 2019 a pandemic of new novel coronavirus-infected pneumonia named COVID-19 has started from Wuhan, China, has led to 254 996 confirmed cases until midday March 20, 2020. Sporadic cases have been imported worldwide, in Algeria, the first case reported on February 25, 2020 was imported from Italy, and then the epidemic has spread to other parts of the country very quickly with 139 confirmed cases until March 21, 2020. Methods It is crucial to estimate the cases number growth in the early stages of the outbreak, to this end, we have implemented the Alg-COVID-19 Model which allows to predict the incidence and the reproduction number R0 in the coming months in order to help decision makers. The Alg-COVIS-19 Model initial equation 1, estimates the cumulative cases at t prediction time using two parameters: the reproduction number R0 and the serial interval SI. Results We found R0=2.55 based on actual incidence at the first 25 days, using the serial interval SI= 4,4 and the prediction time t=26. The herd immunity HI estimated is HI=61%. Also, The Covid-19 incidence predicted with the Alg-COVID-19 Model fits closely the actual incidence during the first 26 days of the epidemic in Algeria Fig. 1.A. which allows us to use it. According to Alg-COVID-19 Model, the number of cases will exceed 5000 on the 42 th day (April 7 th ) and it will double to 10000 on 46th day of the epidemic (April 11 th ), thus, exponential phase will begin (Table 1; Fig.1.B) and increases continuously until reaching à herd immunity of 61% unless serious preventive measures are considered. Discussion This model is valid only when the majority of the population is vulnerable to COVID-19 infection, however, it can be updated to fit the new parameters values.
Extracted statement — location: Algeria
Research field: Virology
Title: Porcine Circoviruses and Herpesviruses Are Prevalent in an Austrian Game
Abstract: During the annual hunt in a privately owned Austrian game population in fall 2019 and 2020, 64 red deer (Cervus elaphus), 5 fallow deer (Dama dama), 6 mouflon (Ovis gmelini musimon), and 95 wild boars (Sus scrofa) were shot and sampled for PCR testing. Pools of spleen, lung, and tonsillar swabs were screened for specific nucleic acids of porcine circoviruses. Wild ruminants were additionally tested for herpesviruses and pestiviruses, and wild boars were screened for pseudorabies virus (PrV) and porcine lymphotropic herpesviruses (PLHV-1-3). PCV2 was detectable in 5% (3 of 64) of red deer and 75% (71 of 95) of wild boar samples. In addition, 24 wild boar samples (25%) but none of the ruminants tested positive for PCV3 specific nucleic acids. Herpesviruses were detected in 15 (20%) ruminant samples. Sequence analyses showed the closest relationships to fallow deer herpesvirus and elk gammaherpesvirus. In wild boars, PLHV-1 was detectable in 10 (11%), PLHV-2 in 44 (46%), and PLHV-3 in 66 (69%) of animals, including 36 double and 3 triple infections. No pestiviruses were detectable in any ruminant samples, and all wild boar samples were negative in PrV-PCR. Our data demonstrate a high prevalence of PCV2 and PLHVs in an Austrian game population, confirm the presence of PCV3 in Austrian wild boars, and indicate a low risk of spillover of notifiable animal diseases into the domestic animal population.
Extracted statement — has location: Austria
Research field: Virology
Title: Full-genome sequences of the first two SARS-CoV-2 viruses from India
Abstract: Background & objectives: Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally affected 195 countries. In India, suspected cases were screened for SARS-CoV-2 as per the advisory of the Ministry of Health and Family Welfare. The objective of this study was to characterize SARS-CoV-2 sequences from three identified positive cases as on February 29, 2020. Methods: Throat swab/nasal swab specimens for a total of 881 suspected cases were screened by E gene and confirmed by RdRp (1), RdRp (2) and N gene real-time reverse transcription-polymerase chain reactions and next-generation sequencing. Phylogenetic analysis, molecular characterization and prediction of B- and T-cell epitopes for Indian SARS-CoV-2 sequences were undertaken. Results: Three cases with a travel history from Wuhan, China, were confirmed positive for SARS-CoV-2. Almost complete (29,851 nucleotides) genomes of case 1, case 3 and a fragmented genome for case 2 were obtained. The sequences of Indian SARS-CoV-2 though not identical showed high (~99.98%) identity with Wuhan seafood market pneumonia virus (accession number: NC 045512). Phylogenetic analysis showed that the Indian sequences belonged to different clusters. Predicted linear B-cell epitopes were found to be concentrated in the S1 domain of spike protein, and a conformational epitope was identified in the receptor-binding domain. The predicted T-cell epitopes showed broad human leucocyte antigen allele coverage of A and B supertypes predominant in the Indian population. Interpretation & conclusions: The two SARS-CoV-2 sequences obtained from India represent two different introductions into the country. The genetic heterogeneity is as noted globally. The identified B- and T-cell epitopes may be considered suitable for future experiments towards the design of vaccines and diagnostics. Continuous monitoring and analysis of the sequences of new cases from India and the other affected countries would be vital to understand the genetic evolution and rates of substitution of the SARS-CoV-2.
Extracted statement — has location: China
Research field: Virology
Title: Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions
Abstract: Since first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. using a transmission model, we estimate a basic reproductive number of 3.11 (95%CI, 2.39-4.13); 58-76% of transmissions must be prevented to stop increasing; Wuhan case ascertainment of 5.0% (3.6-7.4); 21022 (11090-33490) total infections in Wuhan 1 to 22 January.
Extracted statement — location: China
Research field: Virology
Title: Estimating the effective reproduction number of the 2019-nCoV in China
Abstract: We estimate the effective reproduction number for 2019-nCoV based on the daily reported cases from China CDC. The results indicate that 2019-nCoV has a higher effective reproduction number than SARS with a comparable fatality rate.
Extracted statement — location: China
Research field: Virology
Title: Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak
Abstract: Abstract Backgrounds An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city of China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and countries. We present estimates of the basic reproduction number, R 0 , of 2019-nCoV in the early phase of the outbreak. Methods Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate ( γ ), we estimated R 0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI. Findings The early outbreak data largely follows the exponential growth. We estimated that the mean R 0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R 0 . Conclusion The mean estimate of R 0 for the 2019-nCoV ranges from 2.24 to 3.58, and significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.
Extracted statement — location: China
Research field: Virology
Title: Estimation of the Transmission Risk of 2019-nCov and Its Implication for Public Health Interventions
Abstract: English Abstract: Background: Since the emergence of the first pneumonia cases in Wuhan, China, the novel coronavirus (2019-nCov) infection has been quickly spreading out to other provinces and neighbouring countries. Estimation of the basic reproduction number by means of mathematical modelling can be helpful for determining the potential and severity of an outbreak, and providing critical information for identifying the type of disease interventions and intensity. Methods: A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and the intervention measures. Findings: The estimation results based on likelihood and model analysis reveal that the control reproduction number may be as high as 6.47 (95% CI 5.71-7.23). Sensitivity analyses reveal that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction of Wuhan on 2019-nCov infection in Beijing being almost equivalent to increasing quarantine by 100-thousand baseline value. Interpretation: It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCov infection, and how long should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since January 23rd 2020) with significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in 7 days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction. Mandarin Abstract: 背景：自从中国武汉出现第一例肺炎病例以来，新型冠状病毒（2019-nCov）感染已迅速传播到其他省份和周边国家。通过数学模型估计基本再生数，有助于确定疫情爆发的可能性和严重性，并为确定疾病干预类型和强度提供关键信息。 方法：根据疾病的临床进展，个体的流行病学状况和干预措施，设计确定性的仓室模型。 结果：基于似然函数和模型分析的估计结果表明，控制再生数可能高达6.47（95％CI 5.71-7.23）。敏感性分析显示，密集接触追踪和隔离等干预措施可以有效减少控制再生数和传播风险，武汉封城措施对北京2019-nCov感染的影响几乎等同于增加隔离措施10万的基线值。 解释：必须评估中国当局实施的昂贵，资源密集型措施如何有助于预防和控制2019-nCov感染，以及应维持多长时间。在最严格的措施下，预计疫情将在两周内（自2020年1月23日起）达到峰值，峰值较低。与没有出行限制的情况相比，有了出行限制（即没有输入的潜伏类个体进入北京），北京的7天感染者数量将减少91.14％。
Extracted statement — location: China
Research field: Virology
Title: Early transmission dynamics in wuhan, china, of novel coronavirus-infected pneumonia
Abstract: Abstract Background 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. Methods 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. Results 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). Conclusions 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.)
Extracted statement — location: China
Research field: Virology
Title: A Cybernetics-based Dynamic Infection Model for Analyzing SARS-COV-2 Infection Stability and Predicting Uncontrollable Risks
Abstract: Since December 2019, COVID-19 has raged in Wuhan and subsequently all over China and the world. We propose a Cybernetics-based Dynamic Infection Model (CDIM) to the dynamic infection process with a probability distributed incubation delay and feedback principle. Reproductive trends and the stability of the SARS-COV-2 infection in a city can then be analyzed, and the uncontrollable risks can be forecasted before they really happen. The infection mechanism of a city is depicted using the philosophy of cybernetics and approaches of the control engineering. Distinguished with other epidemiological models, such as SIR, SEIR, etc., that compute the theoretical number of infected people in a closed population, CDIM considers the immigration and emigration population as system inputs, and administrative and medical resources as dynamic control variables. The epidemic regulation can be simulated in the model to support the decision-making for containing the outbreak. City case studies are demonstrated for verification and validation.
Extracted statement — location: China
Research field: Virology
Title: Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions
Abstract: Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71–7.23). Sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.
Extracted statement — location: China
Research field: Virology
Title: Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures
Abstract: By 27 February 2020, the outbreak of coronavirus disease 2019 (COVID‐19) caused 82 623 confirmed cases and 2858 deaths globally, more than severe acute respiratory syndrome (SARS) (8273 cases, 775 deaths) and Middle East respiratory syndrome (MERS) (1139 cases, 431 deaths) caused in 2003 and 2013, respectively. COVID‐19 has spread to 46 countries internationally. Total fatality rate of COVID‐19 is estimated at 3.46% by far based on published data from the Chinese Center for Disease Control and Prevention (China CDC). Average incubation period of COVID‐19 is around 6.4 days, ranges from 0 to 24 days. The basic reproductive number (R0) of COVID‐19 ranges from 2 to 3.5 at the early phase regardless of different prediction models, which is higher than SARS and MERS. A study from China CDC showed majority of patients (80.9%) were considered asymptomatic or mild pneumonia but released large amounts of viruses at the early phase of infection, which posed enormous challenges for containing the spread of COVID‐19. Nosocomial transmission was another severe problem. A total of 3019 health workers were infected by 12 February 2020, which accounted for 3.83% of total number of infections, and extremely burdened the health system, especially in Wuhan. Limited epidemiological and clinical data suggest that the disease spectrum of COVID‐19 may differ from SARS or MERS. We summarize latest literatures on genetic, epidemiological, and clinical features of COVID‐19 in comparison to SARS and MERS and emphasize special measures on diagnosis and potential interventions. This review will improve our understanding of the unique features of COVID‐19 and enhance our control measures in the future.
Extracted statement — location: China
Research field: Virology
Title: Statistics based predictions of coronavirus 2019-nCoV spreading in mainland China
Abstract: 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. 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.
Extracted statement — location: China
Research field: Virology
Title: Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia
Abstract: Abstract Background 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. Methods 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. Results 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). Conclusions 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.)
Extracted statement — location: China
Research field: Virology
Title: Viral load dynamics and disease severity in patients infected with SARS-CoV-2 in Zhejiang province, China, January-March 2020: retrospective cohort study
Abstract: Abstract Objective To evaluate viral loads at different stages of disease progression in patients infected with the 2019 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the first four months of the epidemic in Zhejiang province, China. Design Retrospective cohort study. Setting A designated hospital for patients with covid-19 in Zhejiang province, China. Participants 96 consecutively admitted patients with laboratory confirmed SARS-CoV-2 infection: 22 with mild disease and 74 with severe disease. Data were collected from 19 January 2020 to 20 March 2020. Main outcome measures Ribonucleic acid (RNA) viral load measured in respiratory, stool, serum, and urine samples. Cycle threshold values, a measure of nucleic acid concentration, were plotted onto the standard curve constructed on the basis of the standard product. Epidemiological, clinical, and laboratory characteristics and treatment and outcomes data were obtained through data collection forms from electronic medical records, and the relation between clinical data and disease severity was analysed. Results 3497 respiratory, stool, serum, and urine samples were collected from patients after admission and evaluated for SARS-CoV-2 RNA viral load. Infection was confirmed in all patients by testing sputum and saliva samples. RNA was detected in the stool of 55 (59%) patients and in the serum of 39 (41%) patients. The urine sample from one patient was positive for SARS-CoV-2. The median duration of virus in stool (22 days, interquartile range 17-31 days) was significantly longer than in respiratory (18 days, 13-29 days; P=0.02) and serum samples (16 days, 11-21 days; P<0.001). The median duration of virus in the respiratory samples of patients with severe disease (21 days, 14-30 days) was significantly longer than in patients with mild disease (14 days, 10-21 days; P=0.04). In the mild group, the viral loads peaked in respiratory samples in the second week from disease onset, whereas viral load continued to be high during the third week in the severe group. Virus duration was longer in patients older than 60 years and in male patients. Conclusion The duration of SARS-CoV-2 is significantly longer in stool samples than in respiratory and serum samples, highlighting the need to strengthen the management of stool samples in the prevention and control of the epidemic, and the virus persists longer with higher load and peaks later in the respiratory tissue of patients with severe disease.
Extracted statement — Location: China