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".

Paragraph: Norovirus infection constitutes the primary cause of acute viral gastroenteritis. There are currently no vaccines or norovirus-specific antiviral therapeutics available for the management of norovirus infection. Norovirus 3C-like protease is essential for viral replication, consequently, inhibition of this enzyme is a fruitful avenue of investigation that may lead to the emergence of anti-norovirus therapeutics. We describe herein the optimization of dipeptidyl inhibitors of norovirus 3C-like protease using iterative SAR, X-ray crystallographic, and enzyme and cell-based studies. We also demonstrate herein in vivo efficacy of an inhibitor using the murine model of norovirus infection. . Accession Codes. The coordinates and structure factors for norovirus 3CL protease in complex with inhibitors have been deposited to the Protein Databank with the accession codes NVpro:17 (4XBB), NVPro:44-h (4XBC) and NVPro:44-o (4XBD). We initially focused on the design of peptidyl transition state inhibitors of NV 3CLpro that incorporate in their structure a recognition element (a peptidyl fragment) that is congruent with the known substrate specificity of the enzyme (vide supra) and a warhead (aldehyde or α-ketoamide), latent warhead (bisulfite adduct) or transition state mimic (α- Kankanamalage et al. The synthesized compounds were evaluated for their inhibitory activity against NV 3CLpro, as well as their anti-norovirus activity in a cell-based replicon system, as previously Kankanamalage et al. In order to evaluate the specific interactions between the enzyme and the αhydroxyphosphonate inhibitors, inhibitor 17 was incubated with NV 3CLpro in Tris buffer, pH 8.0. Surprisingly, the enzyme-inhibitor complex formed corresponded to the complex formed between aldehyde 16 and 3CL protease. The formation of this complex indicates initial non-enzymatic (buffer)-or enzyme-catalyzed conversion of α-hydroxyphosphonate 17 to the corresponding aldehyde 16, followed by the formation of a tetrahedral adduct with the active site cysteine (Cys139). Studies aimed at ascertaining the validity of this hypothesis are in progress. The ensemble of hydrogen bonding and hydrophobic interactions Kankanamalage et al.  
 Title: Structure-Guided Design and Optimization of Dipeptidyl Inhibitors of Norovirus 3CL Protease. Structure-Activity Relationships and Biochemical, X-ray Crystallographic, Cell- Based, and In Vivo Studies
False

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
False

Paragraph: Background: Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) are two coronaviruses with demonstrated potential to generate significant nosocomial outbreaks. In particular, MERS continues to pose a significant threat in the Middle East since 2012. Currently, no licensed vaccine or drug treatment is available to treat patients infected with either coronavirus. However, there are some MERS vaccines in the preclinical stage of development. We sought to evaluate the potential impact of targeted vaccination strategies for mitigating SARS and MERS outbreaks in healthcare settings using simple mathematical models and detailed historic transmission trees describing the progression of past nosocomial outbreaks of SARS and MERS. Results: Our findings suggest that vaccination strategies targeting patients and healthcare workers, which have been disproportionately affected during past outbreaks, and assuming two vaccination coverage levels at 50 and 75% have the potential to avert nearly 50% or more of MERS or SARS cases. Conclusion: Our modeling results informed by historic outbreak data for SARS and MERS suggest that vaccination strategies targeting patients could be an effective measure to mitigate and prevent outbreaks in the healthcare setting. 
 Title: Evaluating the potential impact of targeted vaccination strategies against severe acute respiratory syndrome coronavirus (SARS- CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) outbreaks in the healthcare setting

True