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".

Q: 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

A: False
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Q: Paragraph: Cryptococcus gattii is a pathogenic environmental yeast that is considered to be emerging in different areas of the world including the Mediterranean Basin. Exposure to infection might be more likely in animals than in human beings, given their closer relationship with the natural habitat of the yeast, vegetation and soil. Thus, animals, and especially pets, can act as indicators of the presence of this yeast in a determined area. Domestic ferrets (Mustela putorius furo) have become common pets in the past 10-20 years. Their natural behavior of sniffing around and going inside narrow spaces makes them prone to contact with decaying organic matter and soil, the substrate for Cryptococcus species. This study describes two cases of cryptococcosis in ferrets in the Iberian Peninsula and Balearic Islands and documents a relationship of ferret cryptococcosis with environmental isolates in the same locations. Here, we emphasize the importance of how an adequate identification and environmental search of the yeast leads to a better understanding of the epidemiology of cryptococcosis and suggests ferrets may act as sentinels for this fungal disease. 
 Title: Ferrets as Sentinels of the Presence of Pathogenic Cryptococcus Species in the Mediterranean Environment

A: True
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Q: Paragraph: duces lethal encephalitis, with high macrophage and neutrophil, but minimal T-cell, infiltration into the brain when compared to the neuroattenuated strain RA59. To determine if chemokine expression corresponds with the cellular infiltrate, chemokine protein and RNA levels from the brains of infected mice were quantified. RJHM-infected mice had lower T-cell (CXCL9, CXCL10), but higher macrophage-attracting (CCL2), chemokine proteins compared to RA59. RJHM also induced significantly higher CXCL2 (a neutrophil chemoattractant) mRNA compared to RA59. The neurovirulent spike gene chimera SJHM/RA59 induces high levels of T cells and macrophages in the brain compared to the attenuated SA59/RJHM chimera. Accordingly, SJHM/RA59 induced higher levels of CXCL9, CXCL10, and CCL2 protein compared to SA59/RJHM. Chemokine mRNA patterns were in general agreement. Thus, chemokine patterns correspond with the cellular infiltrate, and the spike protein influences levels of macrophage, but not T-cell, chemokines. Journal of NeuroVirology (2008) 14, 5-16. 
 Title: Chemokine expression during mouse hepatitis virus-induced encephalitis: Contributions of the spike and background genes

A:
True
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