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".

Example Input: Paragraph: Background: The detrimental role of viruses has been well described in CF, although the pattern of virus infections has not been investigated in a longitudinal study. The primary aim was to determine the feasibility of fortnightly parent collected swabs in young children with CF. Methods: Children under three years with CF were recruited. Nasal swabs were collected by parents every fortnight and during periods of symptoms over 12 months. Nasal swabs were posted and virus detected using real-time PCR. Results: Only 27% of the patients completed the study to 10 months, although 98% of the swabs returned were adequate for analysis. Mould was observed growing on 23% of the returned swabs. There was no evidence to demonstrate relationships with symptoms and viruses, prolonged symptoms, prolonged shedding or patterns of virus infections. Conclusions: This study highlights the need to further investigate the role of viruses in children with CF using a robust method of frequent collection in children for a longitudinal study, with appropriate storage and shipping techniques to avoid mould growth or other potential contaminants. 
 Title: Original Article
Example Output: False

Example 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
Example Output: False

Example Input: Paragraph: Note: this report is a pre-print and has not yet been peer-reviewed. As the number of novel coronavirus cases grows both inside and outside of China, public health authorities require evidence on the effectiveness of control measures such as thermal screening of arrivals at airports. We evaluated the effectiveness of exit and entry screening for 2019-nCoV infection. In our baseline scenario, we estimated that 46.5% (95%CI: 35.9 to 57.7) of infected travellers would not be detected, depending on the incubation period, sensitivity of exit and entry screening, and the proportion of cases which are asymptomatic. Airport screening is unlikely to detect a sufficient proportion of 2019-nCoV infected travellers to avoid entry of infected travellers. We developed an online tool so that results can be updated as new information becomes available. As of 29 Jan 2019, 5,997 confirmed and 9,239 suspected cases of 2019-nCoV have been reported from China with 132 deaths confirmed so far 1 . There were cases in at least 15 other countries, identified due to symptoms plus recent travel history to Hubei province, China, which strongly suggests that the reported cases constitute only a small fraction of the actual number of infected individuals in China 2 . While the most affected region, Hubei province, has now ceased air travel and closed major public transport routes 3 it is unlikely that these measures will fully contain the outbreak. Despite limited evidence for its effectiveness 4,5 , airport screening is frequently implemented to limit the probability of infected cases entering other regions or countries. Here we use the available evidence to evaluate the effectiveness of exit and entry screening for detecting travellers with 2019-nCoV. We also provide an online app so that results can be updated as new information becomes available. . CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the (which was not peer-reviewed) The copyright holder for this preprint . 
 Title: Effectiveness of airport screening at detecting travellers infected with 2019-nCoV
Example Output:
True