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: Background. Animal models suggest that influenza infection favors nasopharyngeal acquisition of pneumococci. We assessed this relationship with influenza and other respiratory viruses in young children. Methods. A case-control study was nested within a prospective cohort study of acute respiratory illness (ARI) in Andean children <3 years of age (RESPIRA-PERU study). Weekly household visits were made to identify ARI and obtain nasal swabs for viral detection using real-time reverse-transcription polymerase chain reaction. Monthly nasopharyngeal (NP) samples were obtained to assess pneumococcal colonization. We determined whether specific respiratory viral ARI episodes occurring within the interval between NP samples increased the risk of NP acquisition of new pneumococcal serotypes. Results. A total of 729 children contributed 2128 episodes of observation, including 681 pneumococcal acquisition episodes (new serotype, not detected in prior sample), 1029 nonacquisition episodes (no colonization or persistent colonization with the same serotype as the prior sample), and 418 indeterminate episodes. The risk of pneumococcal acquisition increased following influenza-ARI (adjusted odds ratio [AOR], 2.19; 95% confidence interval [CI], 1.02-4.69) and parainfluenza-ARI (AOR, 1.86; 95% CI, 1.15-3.01), when compared with episodes without ARI. Other viral infections (respiratory syncytial virus, human metapneumovirus, human rhinovirus, and adenovirus) were not associated with acquisition. Conclusions. Influenza and parainfluenza ARIs appeared to facilitate pneumococcal acquisition among young children. As acquisition increases the risk of pneumococcal diseases, these observations are pivotal in our attempts to prevent pneumococcal disease. 
 Title: The Role of Influenza and Parainfluenza Infections in Nasopharyngeal Pneumococcal Acquisition Among Young Children
[EX A]: 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: Background: Given that many infectious diseases spread rapidly, across borders and species, there is a growing worldwide need to increase the number of public health professionals skilled in controlling infectious epidemics. Needed also are more public health professionals skilled in non-communicable disease surveillance and interventions. As a result, we surveyed all 57 field epidemiology training programmes (FETPs) that are members of the Training Program in Epidemiology and Public Health Interventions Network (TEPHINET), to evaluate the progress of the FETPs, the only global applied epidemiology network, toward increasing public health capacity globally. Methods: Data on the FETP programmes and the training they provide were abstracted from TEPHINET membership surveys and verified with FETP directors for all FETPs that were members of TEPHINET in 2012. Data on abstracts submitted to the recent TEPHINET Global Scientific Conference, on recent accomplishments by each FETP, and on quality improvement were also compiled to provide a worldwide view of the public health human resource capacity produced by these programmes. Results: A total of 6980 public health professionals worldwide have graduated from an FETP or from the Center for Disease Control and Prevention's Epidemiology Intelligence Service (EIS). FETP residents and graduates participate in key public health prevention, control, and response activities. Each FETP has adapted its curriculum and objectives over time to align with its country's public health priorities. FETPs are well integrated into their national public health infrastructures, and they have many partners at the national, regional and global levels. Conclusion: FETPs are a competent and diverse source of highly skilled public health professionals who contribute significantly to public health's global human resource needs. This finding is evidenced by 1) the training curricula that were adapted over time to meet public health's human resource needs, 2) the FETPs' continued support from internal and external partners, 3) the increasing number of FETP residents and graduates and their increasing contribution to effective public health work, and 4) the increased quality improvement initiatives facilitated through the FETPs membership in one global network, TEPHINET. 
 Title: An evaluation of the global network of field epidemiology and laboratory training programmes: a resource for improving public health capacity and increasing the number of public health professionals worldwide
[EX A]:
True