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".

Ex Input:
Paragraph: Background: Reconstructing individual transmission events in an infectious disease outbreak can provide valuable information and help inform infection control policy. Recent years have seen considerable progress in the development of methodologies for reconstructing transmission chains using both epidemiological and genetic data. However, only a few of these methods have been implemented in software packages, and with little consideration for customisability and interoperability. Users are therefore limited to a small number of alternatives, incompatible tools with fixed functionality, or forced to develop their own algorithms at considerable personal effort. Results: Here we present outbreaker2, a flexible framework for outbreak reconstruction. This R package re-implements and extends the original model introduced with outbreaker, but most importantly also provides a modular platform allowing users to specify custom models within an optimised inferential framework. As a proof of concept, we implement the within-host evolutionary model introduced with TransPhylo, which is very distinct from the original genetic model in outbreaker, and demonstrate how even complex model results can be successfully included with minimal effort. Conclusions: outbreaker2 provides a valuable starting point for future outbreak reconstruction tools, and represents a unifying platform that promotes customisability and interoperability. Implemented in the R software, outbreaker2 joins a growing body of tools for outbreak analysis. 
 Title: outbreaker2: a modular platform for outbreak reconstruction

Ex Output:
True


Ex Input:
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

Ex Output:
False


Ex Input:
Paragraph: During the last decade, the application of both qualitative and quantitative nucleic acid detection techniques has had a major impact on diagnostics in clinical virology. Both signal and target amplification-based systems are currently used routinely in most if not all virology laboratories. However, commercial assays are only available for a very limited number of targets, and this has resulted in the development and introduction of assays developed in-house for most viral targets. With improved and automated nucleic acid sample isolation techniques, as well as real-time detection methods, a new generation of assays for most clinically important viruses is being developed. These technological improvements also make it possible to generate results with a very short turnaround time. As an example of a more individual-patient disease-management concept, we have introduced in our clinical setting the quantitative detection of Epstein-Barr virus (EBV) in T-cell-depleted allogeneic stem cell transplant patients. This has enabled us to develop models for pre-emptive anti-B-cell immunotherapy for EBV reactivation, and for reducing not only the incidence of EBV lymphoproliferative disease (EBV-LPD), but the virus-related mortality. It is now also feasible to introduce molecular testing for those viruses that can easily be detected using classical virological methods, such as culture techniques or antigen detection. Prospective studies are needed to evaluate the clinical importance of the additional positive samples detected. It should, however, be clear that a complete exchange of technology is unlikely to occur, and that complementary methods should stay operational, making possible the discovery of new viruses. Furthermore, the ability to characterise viruses more easily by sequencing opens new possibilities for epidemiological studies. There is also an urgent need, with regard to molecular diagnostic methods, for the introduction and use of standardised materials and participation in international quality control programmes. Finally, with the introduction of a universal internal control throughout the whole procedure, the accuracy of the results generated is warranted. 
 Title: REVIEW Molecular and diagnostic clinical virology in real time

Ex Output:

True