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: Background: Cotton blue disease (CBD), an important global cotton crop pathology responsible for major economic losses, is prevalent in the major cotton-producing states of Brazil. Typical CBD symptoms include stunting due to internodal shortening, leaf rolling, intense green foliage, and yellowing veins. Atypical CBD symptoms, including reddish and withered leaves, were also observed in Brazilian cotton fields in 2007. Recently, a Polerovirus named Cotton leafroll dwarf virus (CLRDV) was shown to be associated with CBD. Results: To understand the distribution and genetic diversity of CLRDV in Brazil, we analyzed 23 CBD-symptomatic plants from susceptible cotton varieties originating from five of the six most important cotton-growing states, from 2004-2007. Here, we report on CLRDV diversity in plants with typical or atypical CBD symptoms by comparing viral coat protein, RNA polymerase (RdRp), and intergenic region genomic sequences. The virus had a widespread distribution with a low genetic diversity; however, three divergent isolates were associated with atypical CBD symptoms. These divergent isolates had a CLRDV-related coat protein but a distinct RdRp sequence, and probably arose from recombination events. Based on the taxonomic rules for the family Luteoviridae, we propose that these three isolates represent isolates of a new species in the genus Polerovirus. 
 Title: Virology Journal Widespread distribution and a new recombinant species of Brazilian virus associated with cotton blue disease

Ex Output:
True


Ex Input:
Paragraph: Background: Influenza-like illness (ILI) may be caused by a variety of pathogens. Clinical observations are of little help to recognise myxovirus infection and implement appropriate prevention measures. The limited use of molecular tools underestimates the role of other common pathogens. Objectives: During the early weeks of the 2009-2010 flu pandemic, a clinical and virological survey was conducted in adult and paediatric patients with ILI referred to two French University hospitals in Paris and Tours. Aims were to investigate the different pathogens involved in ILI and describe the associated symptoms. Methods: H1N1v pandemic influenza diagnosis was performed with real time RT-PCR assay. Other viral aetiologies were investigated by the molecular multiplex assay RespiFinder19H. Clinical data were collected prospectively by physicians using a standard questionnaire. Results: From week 35 to 44, endonasal swabs were collected in 413 patients. Overall, 68 samples (16.5%) were positive for H1N1v. In 13 of them, other respiratory pathogens were also detected. Among H1N1v negative samples, 213 (61.9%) were positive for various respiratory agents, 190 in single infections and 23 in mixed infections. The most prevalent viruses in H1N1v negative single infections were rhinovirus (62.6%), followed by parainfluenza viruses (24.2%) and adenovirus (5.3%). 70.6% of H1N1v cases were identified in patients under 40 years and none after 65 years. There was no difference between clinical symptoms observed in patients infected with H1N1v or with other pathogens. Our results highlight the high frequency of non-influenza viruses involved in ILI during the pre-epidemic period of a flu alert and the lack of specific clinical signs associated with influenza infections. Rapid diagnostic screening of a large panel of respiratory pathogens may be critical to define and survey the epidemic situation and to provide critical information for patient management. 
 Title: High Burden of Non-Influenza Viruses in Influenza-Like Illness in the Early Weeks of H1N1v Epidemic in France

Ex Output:

True