TASK DEFINITION: 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".
PROBLEM: 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

SOLUTION: False

PROBLEM: Paragraph: During the replication cycle of Sindbis virus minus-strand synthesis stops normally at the time that plus-strand synthesis reaches a maximum rate. We have isolated and characterized revertants of ts24, a member of the A complementation group of Sindbis HR mutants, that we had demonstrated previously to have a temperature-sensitive defect in the regulation of minus-strand synthesis. These revertants of t&4 replicated efficiently at 40' but nevertheless retained the temperature sensitive defect in the regulation of minus-strand synthesis: they continued to synthesize minus strands late in the replication cycle at 40" but not at 30" and in the presence or absence of protein synthesis. Although failure to regulate the synthesis of minus strands resulted in continuous minus-strand synthesis and in the accumulation of minus strands, the rate of plus-strand synthesis was not increased concertedly. Minus strands synthesized after the rate of plus-strand synthesis had become constant were demonstrated to be utilized as templates for 26 S mRNA synthesis. Thus, the change from an increasing to a constant rate of plus-strand synthesis during the alphavirus replication cycle cannot be governed solely by the number of minus strands that accumulate in infected cells. We present a model for the preferential utilization of minus strands as a mechanism for the cessation of minus-strand synthesis that occurs normally during alphavirus replication. 
 Title: The Effect of Loss of Regulation of Minus-Strand RNA Synthesis on Sindbis Virus Replication

SOLUTION: True

PROBLEM: Paragraph: Background: During the 2014 West African Ebola Virus Disease (EVD) outbreak, the U.S. Centers for Disease Control and Prevention recommended that all emergency department (ED) patients undergo travel screening for risk factors of importing EVD. Objectives: We sought to determine the overall adherence rate to the recommended travel screening protocol and to identify factors associated with nonadherence to the protocol. Methods: We conducted a multicenter, retrospective analysis of adherence to the travel screening program in an academic hospital and three affiliated community hospitals. A regression model identified patient and hospital factors associated with nonadherence. Results: Of the 147,062 patients included for analysis, 93.7% (n = 137,834) had travel screenings completed. We identified several characteristics of patients that were most likely to be missed by the screening protocol-patients with low English proficiency, patients who arrive via ambulance or helicopter, and patients with more severe illness or injury based on initial triage acuity. Conclusions: These findings should be used to improve adherence to the travel screening protocol for future emerging infectious disease threats. Ó 
 Title: Original Contributions ADHERENCE TO UNIVERSAL TRAVEL SCREENING IN THE EMERGENCY DEPARTMENT DURING EPIDEMIC EBOLA VIRUS DISEASE

SOLUTION:
False