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 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
[EX A]: False

[EX Q]: Paragraph: The replicase of mouse hepatitis virus strain JHM (MHV-JHM) is encoded by two overlapping open reading frames, ORF1a and ORF1b, which are translated to produce a 750-kDa precursor polyprotein. The polyprotein is proposed to be processed by viral proteinases to generate the functional replicase complex. To date, only the MHV-JHM amino-terminal proteins p28 and p72, which is processed to p65, have been identified. To further elucidate the biogenesis of the MHV-JHM replicase, we cloned and expressed five regions of ORF1a in bacteria and prepared rabbit antisera to each region. Using the immune sera to immunoprecipitate radiolabeled proteins from MHV-JHM infected cells, we determined that the MHV-JHM ORF1a is initially processed to generate p28, p72, p250, and p150. Pulse±chase analysis revealed that these intermediates are further processed to generate p65, p210, p40, p27, the MHV 3C-like proteinase, and p15. A putative replicase complex consisting of p250, p210, p40, p150, and a large protein (Ͼ300 kDa) coprecipitate from infected cells disrupted with NP-40, indicating that these proteins are closely associated even after initial proteolytic processing. Immunofluorescence studies revealed punctate labeling of ORF1a proteins in the perinuclear region of infected cells, consistent with a membrane-association of the replicase complex. Furthermore, in vitro transcription/translation studies of the MHV-JHM 3Cpro and flanking hydrophobic domains confirm that 3C protease activity is significantly enhanced in the presence of canine microsomal membranes. Overall, our results demonstrate that the MHV-JHM ORF1a polyprotein: (1) is processed into more than 10 protein intermediates and products, (2) requires membranes for efficient biogenesis, and (3) is detected in discrete membranous regions in the cytoplasm of infected cells. 
 Title: Processing of the Coronavirus MHV-JHM Polymerase Polyprotein: Identification of Precursors and Proteolytic Products Spanning 400 Kilodaltons of ORF1a
[EX A]: False

[EX Q]: Paragraph: To investigate the long-term effects of mild H1N1 influenza infection on the pulmonary function of a cohort of patients. Forty-eight patients, all diagnosed with influenza A virus subtype H1N1 in 2009, were retrospectively included in this study. Each patient in the study was monitored for 11-13 months by standard pulmonary function examination. The examination included monitoring respiratory tract infection symptoms (cough, expectoration or gasping) and vital signs. Long-term changes in symptoms and changes in vital signs were correlated back to and compared with the severity of the initial H1N1 influenza infection. 
 Title: Pulmonary Function and Clinical Manifestations of Patients Infected with Mild Influenza A Virus Subtype H1N1: A One-Year Follow-Up
[EX A]:
True