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".

Paragraph: People in the Middle East have been affected by the Middle East Respiratory Syndrome CoronaVirus (MERS Co-V) since 2012. New cases are continuously reported especially in the Kingdom of Saudi Arabia, and the risk of exposure remains an issue. Data visualization plays a vital role in effective analysis of the data. In this paper, we introduce an interactive visualization application for MERS data collected from the Control and Command Centre, Ministry of Health website of Saudi Arabia. The data corresponding to the period from January 1, 2019 to February 28, 2019 was used in the present work. The attributes considered include gender, age, date of reporting, city, region, camel contact, description and status of the patient. The visualization tool has been developed using Shiny framework of R programming language. The application presents information in the form of interactive plots, maps and tables. The salient feature of the tool is that users can view and download data corresponding to the period of their choice. This tool can help decision makers in the detailed analysis of data and hence devise measures to prevent the spread of the disease. 
 Title: Shiny Framework Based Visualization and Analytics Tool for Middle East Respiratory Syndrome
False

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
False

Paragraph: We studied the genetic diversity, geographical structure, infection prevalence, and age-associated prevalence among camels at the largest entry port of camels from Africa into the Arabian Peninsula. Methods In this prospective genomic study, we took nasal samples from camels imported from Sudan and Djibouti into the Port of Jeddah in Jeddah, Saudi Arabia, over an almost 2-year period and local Arabian camels over 2 months in the year after surveillance of the port. We determined the prevalence of MERS-CoV infection, age-associated patterns of infection, and undertook phylogeographical and migration analyses to determine intercountry virus transmission after local lineage establishment. We compared all virological characteristics between the local and imported cohorts. We compared major gene deletions between African and Arabian strains of the virus. Reproductive numbers were inferred with Bayesian birth death skyline analyses. Between Aug 10, 2016, and May 3, 2018, we collected samples from 1196 imported camels, of which 868 originated from Sudan and 328 from Djibouti, and between May 1, and June 25, 2018, we collected samples from 472 local camels, of which 189 were from Riyadh and 283 were from Jeddah, Saudi Arabia. Virus prevalence was higher in local camels than in imported camels (224 [47·5%] of 472 vs 157 [13·1%] of 1196; p<0·0001). Infection prevalence peaked among camels older than 1 year and aged up to 2 years in both groups, with 255 (66·9%) of 381 positive cases in this age group. Although the overall geographical distribution of the virus corresponded with the phylogenetic tree topology, some virus exchange was observed between countries corresponding with trade routes in the region. East and west African strains of the virus appear to be geographically separated, with an origin of west African strains in east Africa. African strains of the virus were not re-sampled in Saudi Arabia despite sampling approximately 1 year after importation from Africa. All local Arabian samples contained strains of the virus that belong to a novel recombinant clade (NRC) first detected in 2014 in Saudi Arabia. Reproduction number estimates informed by the sequences suggest sustained endemicity of NRC, with a mean R e of 1·16. Interpretation Despite frequent imports of MERS-CoV with camels from Africa, African lineages of MERS-CoV do not establish themselves in Saudi Arabia. Arabian strains of the virus should be tested for changes in virulence and transmissibility. 
 Title: Enzootic patterns of Middle East respiratory syndrome coronavirus in imported African and local Arabian dromedary camels: a prospective genomic study

True