instruction:
In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".
question:
Paragraph: Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community. Despite substantial public health improvements in the last century, infectious diseases remain one of the leading causes of both morbidity and mortality 1,2 . When confronted with an infectious disease outbreak, public health officials typically work to control the outbreak by performing assessments, analyzing surveillance data, identifying resources and interacting with subject mater experts 2-4 . Control measures are then implemented based on the cumulative information collected. These approaches rely heavily on good surveillance systems, access to experts, and good intuition about which control measures to use. As such, they are largely subjective, time consuming, and the infrastructure required is often not present in high disease burden areas. Modeling is an attractive supplemental method because of the ability to estimate an outbreak's trajectory and the effects of possible control measures in a timely manner. Compartmental models are historically common; they divide individuals into categories based on their disease status. The most common variant is the SIR model, named after the categories used-"susceptible", "infectious" and "recovered". Models of this nature have small computational requirements, and are thus commonly used as first pass attempts to characterize outbreaks or infections quickly 5 . For example, after the sudden emergence of Severe Acute Respiratory Syndrome (SARS) in the early 2000s, researchers used modeling to characterize the virus' epidemiology. Several used compartmental models with control measures like quarantine or isolation in various settings (e.g., hospitals or cities) to describe effects of possible interventions 6-9 . Similar work exists for essentially all well known infectious diseases. For example, Mandal et al. provide a review of models used for malaria 10 and Bauch et al. explore model use with respect to SARS and other emerging infectious diseases 6 . Methods among these groups are often similar, but tend to focus on specific diseases and locations of interest. In contrast, agent-based models use a bottom-up approach where the agents (these are often people) interact with particular rules to simulate outbreaks 11 . This allows simulations at high resolutions, but requires large amounts of data to parameterize the models, as well as substantial computational power. It is thought that they may reflect real world scenarios more accurately, but the lack of available epidemiological data necessitates assumptions that are difficult or impossible to test 11 . These models further require computational resources inaccessible to an average health department. Both agent-based and compartmental models exhibit additional features that are problematic to wide-spread model adoption. The first is the focus on particular disease-location pairs. This emphasis precludes application to 
 Title: An approach to and web-based tool for infectious disease outbreak intervention analysis OPEN
answer:
False


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Paragraph: Ever since the discovery of SARS-CoV in the year 2003, numerous researchers around the world have been working relentlessly to understand the biology of this virus. As in other coronaviruses, nucleocapsid (N) is one of the most crucial structural components of the SARS-CoV. Hence major attention has been focused on characterization of this protein. Independent studies conducted by several laboratories have elucidated significant insight into the primary function of this protein, which is to encapsidate the viral genome. In addition, many reports also suggest that this protein interferes with different cellular pathways, thus implying it to be a key regulatory component of the virus too. In the first part of this review, we will discuss these different properties of the N-protein in a consolidated manner. Further, this protein has also been proposed to be an efficient diagnostic tool and a candidate vaccine against the SARS-CoV. Hence, towards the end of this article, we will discuss some recent progress regarding the possible clinically relevant use of the N-protein. # 
 Title: The SARS-CoV nucleocapsid protein: A protein with multifarious activities
answer:
True


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Paragraph: A majority of emerging infectious diseases are of zoonotic origin. Metagenomic Next-Generation Sequencing (mNGS) has been employed to identify uncommon and novel infectious etiologies and characterize virus diversity in human, animal, and environmental samples. Here, we systematically reviewed studies that performed viral mNGS in common livestock (cattle, small ruminants, poultry, and pigs). We identified 2481 records and 120 records were ultimately included after a first and second screening. Pigs were the most frequently studied livestock and the virus diversity found in samples from poultry was the highest. Known animal viruses, zoonotic viruses, and novel viruses were reported in available literature, demonstrating the capacity of mNGS to identify both known and novel viruses. However, the coverage of metagenomic studies was patchy, with few data on the virome of small ruminants and respiratory virome of studied livestock. Essential metadata such as age of livestock and farm types were rarely mentioned in available literature, and only 10.8% of the datasets were publicly available. Developing a deeper understanding of livestock virome is crucial for detection of potential zoonotic and animal pathogens and One Health preparedness. Metagenomic studies can provide this background but only when combined with essential metadata and following the "FAIR" (Findable, Accessible, Interoperable, and Reusable) data principles. Viruses 2020, 12, 107 2 of 22 flocks and wild birds, suggesting that pre-existing biosecurity measurements could not keep up with the rate of livestock intensification [16] . In 2007-2010, a large-scale Q fever outbreak was reported in the Netherlands, affecting more than 3500 human cases and resulting in a huge economic loss [17, 18] . A steep increase in the number of goat farms most likely was the driver for the increased prevalence of Coxiella burnetii infections, with animal abortion waves that had gone unnoticed. The policy of voluntary reporting abortion outbreaks to the Animal Health Service hindered the timely detection of the circulation of Q fever, and therefore early interventions. These examples indicate that zoonotic risks in the livestock industry should be carefully managed and adapted to livestock intensification. The One Health approach has been coined for advocating collaboration between multiple stakeholders including veterinarians, clinicians, epidemiologists, virologists, microbiologists, ecologists, and policy makers to prevent and control EIDs through the human-animal-environment interface [19] . Surveillance of livestock and the surrounding environment is a hallmark of early detection but is currently targeted to known risks. Advances in Next-Generation Sequencing (NGS) technologies and rapid development of bioinformatics and computational tools offer new opportunities for EID surveillance in quality and in scale. Particularly, metagenomic NGS (mNGS) allows unbiased detection of all microbes and viruses in a sample, showing potential for timely detection of rare or novel infectious etiologies, as well as for surveillance of foodborne and waterborne viruses [20] [21] [22][23][24][25][26]. However, the use of mNGS as a potential surveillance tool requires a deeper understanding of what is "normal" diversity in humans [27], as well as wildlife [28,29] and farm animals [30]. Characterizing species-specific metagenomes could potentially be used to provide a surveillance baseline for early detection and for tracking of movements of pathogens across different hosts, and has been promoted by projects like the global virome project [31]. However, for such applications, detailed background is needed regarding coverage, representativeness, and biases in the study designs. Here, we conduct a systematic review to identify available literature that performed viral mNGS in common farm animals including cattle, small ruminants (goats and sheep), poultry, and pigs. We reviewed the data and metadata availability and quality of these studies. We further summarized reported viromes of common farm animals in order to translate these efforts as background virus diversity profiles of common farm animals to guide preparedness of diseases at the livestock-human interface. 
 Title: viruses Virus Metagenomics in Farm Animals: A Systematic Review
answer:

True