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".

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

A: False
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Q: Paragraph: This article examines the history of dog mess in Paris, from its 'discovery' in the late 1920s to the management regime of the early twenty-first century. Mayor Jacques Chirac's anti-excrement campaigns in the 1980s are a particular focus. Situating the meaning and management of dog mess within histories of public hygiene and disgust, and mobilising insights from work on public hygiene, biopolitics and governmentality, this article shows how Chirac's attempt to produce self-regulating and responsible dog owners through education failed to persuade them to overcome their disgust at their pets' excrement. Fining alongside education proved a more effective management strategy. The history of dog mess in Paris highlights the biopolitical problems raised by animal excrement decades after the apogee of the public hygiene movement, and shows how human-animal partnerships expose the limits of governmentality approaches to public hygiene and neo-liberal urban governance. 
 Title: Combating Canine 'Visiting Cards': Public Hygiene and the Management of Dog Mess in Paris since the 1920s

A: True
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Q: Paragraph: Hospitals worldwide are facing an increasing incidence of hard-to-treat infections. Limiting infections and providing patients with optimal drug regimens require timely strain identification as well as virulence and drug-resistance profiling. Additionally, prophylactic interventions based on the identification of environmental sources of recurrent infections (e.g., contaminated sinks) and reconstruction of transmission chains (i.e., who infected whom) could help to reduce the incidence of nosocomial infections. WGS could hold the key to solving these issues. However, uptake in the clinic has been slow. Some major scientific and logistical challenges need to be solved before WGS fulfils its potential in clinical microbial diagnostics. In this review we identify major bottlenecks that need to be resolved for WGS to routinely inform clinical intervention and discuss possible solutions. Thanks to progress in high-throughput sequencing technologies over the last two decades, generating microbial genomes is now considered neither particularly challenging nor expensive. As a result, whole-genome sequencing (WGS) (see Glossary) has been championed as the obvious and inevitable future of diagnostics in multiple reviews and opinion pieces dating back to 2010 [1] [2] [3] [4] . Despite enthusiasm in the community, WGS diagnostics has not yet been widely adopted in clinical microbiology, which may seem at odds with the current suite of applications for which WGS has huge potential, and which are already widely used in the academic literature. Common applications of WGS in diagnostic microbiology include isolate characterization, antimicrobial resistance (AMR) profiling, and establishing the sources of recurrent infections and between-patient transmissions. All of these have obvious clinical relevance and provide case studies where WGS could, in principle, provide additional information and even replace the knowledge obtained through standard clinical microbiology techniques. This review reiterates the potential of WGS for clinical microbiology, but also its current limitations, and suggests possible solutions to some of the main bottlenecks to routine implementation. In particular, we argue that applying existing WGS pipelines developed for fundamental research is unlikely to produce the fast and robust tools required, and that new dedicated approaches are needed for WGS in the clinic. At the most basic level, WGS can be used to characterize a clinical isolate, informing on the likely species and/or subtype and allowing phylogenetic placement of a given sequence relative to an existing set of isolates. WGS-based strain identification gives a far superior resolution Highlights In principle, WGS can provide highly relevant information for clinical microbiology in near-real-time, from phenotype testing to tracking outbreaks. However, despite this promise, the uptake of WGS in the clinic has been limited to date, and future implementation is likely to be a slow process. The increasing information provided by WGS can cause conflict with traditional microbiological concepts and typing schemes. Decreasing raw sequencing costs have not translated into decreasing total costs for bacterial genomes, which have stabilised. Existing research pipelines are not suitable for the clinic, and bespoke clinical pipelines should be developed. 
 Title: From Theory to Practice: Translating Whole-Genome Sequencing (WGS) into the Clinic

A:
True
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