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: 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: The nasal epithelium from a young girl was examined by electron microscopy and found to be infected by coronavirus. Virions are seen within and outside the ciliated cells, but not outside or within the goblet cells or other cells of the nasal mucosa. Some virions are located near the microvilli, others in pockets in the apical cell membrane. The cytoplasm contains many small vesicles with a single virion, large apical vesicles containing hundreds of virions, and lysosome-like cytosomes with a moderate number of virions. Some viruslike particles devoid of an electron-dense interior are seen both in the cytosomes and extracellularly. Virus budding was observed in the Golgi apparatus but nowhere else in the cell. The ciliated cells seem not to be destroyed by the viruses, although in many cases the cilia are withdrawn into the cell body. The loss of cilia is likely to cause rhinorrhoea. 
 Title: Ultrastructure of human nasal epithelium during an episode of coronavirus infection

Ex Output:
True


Ex Input:
Paragraph: The role of rhinovirus asymptomatic infections in the transmission among close contacts subjects is unknown. We tested health care workers, a pair of one child and a family member and immunocompromised patients (n =191). HRV were detected on 22.9% symptomatic and 3.6% asymptomatic cases suggesting lower transmission among contacts. Rhinovirus infections are among the most frequent causes of the common colds (18). It is the most common etiology of viral respiratory infections among diverse populations, including adults, children and more recent studies have linked HRVs to more severe lower respiratory illnesses in otherwise healthy young children (11-12), immunocompromised (4,6), and elderly patients. (5,13, 23). In Brazil, there are few HRV studies, Arruda et.al. (1991), realized the first Brazilian study detecting a 45.5% rate on symptomatic children in Fortaleza -CE. Another study realized in Salvador -BA also showed a high prevalence (48.5%) (Souza ,2003). The only study 
 Title: HUMAN RHINOVIRUS INFECTIONS IN SYMPTOMATIC AND ASYMPTOMATIC SUBJECTS

Ex Output:

False