instruction:
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".
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Paragraph: Most models of the COVID-19 pandemic in the United States do not consider geographic variation, and their relevance to public policies is not straightforward. We developed a mathematical model that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when the disease is already widespread will make little difference. Meanwhile, increased testing capacity (facilitating early identification of infected people and quick isolation) and strict social-distancing and self-quarantine rules are effective in abating the outbreak. The modeling has also produced state-specific information. For example, for New York and Michigan, isolation of persons exposed to the virus needs to be imposed within 2 days to prevent a broad outbreak, whereas for other states this period can be 3.6 days. This model could be used to determine resources needed before safely lifting state policies on social distancing.
Title: Mitigating COVID-19 outbreak via high testing capacity and strong transmission-intervention in the United States
answer:
True

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Paragraph: Rationale: Ex vivo, bronchial epithelial cells from people with asthma are more susceptible to rhinovirus infection caused by deficient induction of the antiviral protein, IFN-b. Exogenous IFN-b restores antiviral activity. Objectives: To compare the efficacy and safety of inhaled IFN-b with placebo administered to people with asthma after onset of cold symptoms to prevent or attenuate asthma symptoms caused by respiratory viruses. Methods: A total of 147 people with asthma on inhaled corticosteroids (British Thoracic Society Steps 2-5), with a history of virus-associated exacerbations, were randomized to 14-day treatment with inhaled IFN-b (n = 72) or placebo (n = 75) within 24 hours of developing cold symptoms and were assessed clinically, with relevant samples collected to assess virus infection and antiviral responses. Measurements and Main Results: A total of 91% of randomized patients developed a defined cold. In this modified intention-to-treat population, asthma symptoms did not get clinically significantly worse (mean change in six-item Asthma Control Questionnaire ,0.5) and IFN-b treatment had no significant effect on this primary endpoint, although it enhanced morning peak expiratory flow recovery (P = 0.033), reduced the need for additional treatment, and boosted innate immunity as assessed by blood and sputum biomarkers. In an exploratory analysis of the subset of more difficult-to-treat, Step 4-5 people with asthma (n = 27 IFN-b; n = 31 placebo), Asthma Control Questionnaire-6 increased significantly on placebo; this was prevented by IFN-b (P = 0.004). Conclusions: Although the trial did not meet its primary endpoint, it suggests that inhaled IFN-b is a potential treatment for virus-induced deteriorations of asthma in difficult-to-treat people with asthma and supports the need for further, adequately powered, trials in this population. Clinical trial registered with www.clinicaltrials.gov (NCT 01126177).
Title: The Effect of Inhaled IFN-b on Worsening of Asthma Symptoms Caused by Viral Infections A Randomized Trial
answer:
True

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Paragraph: 1. Predicting infectious disease dynamics is a central challenge in disease ecology. Models 36 that can assess which individuals are most at risk of being exposed to a pathogen not only 37 provide valuable insights into disease transmission and dynamics but can also guide 38 management interventions. Constructing such models for wild animal populations, 39 however, is particularly challenging; often only serological data is available on a subset 40 of individuals and non-linear relationships between variables are common. 41 42 2. Here we take advantage of the latest advances in statistical machine learning to construct 43 pathogen-risk models that automatically incorporate complex non-linear relationships 44 with minimal statistical assumptions from ecological data with missing values. Our 45 approach compares multiple machine learning algorithms in a unified environment to 46 find the model with the best predictive performance and uses game theory to better 47 interpret results. We apply this framework on two major pathogens that infect African 48 lions: canine distemper virus (CDV) and feline parvovirus. 49 50 3. Our modelling approach provided enhanced predictive performance compared to more 51 traditional approaches, as well as new insights into disease risks in a wild population. We 52 were able to efficiently capture and visualise strong non-linear patterns, as well as model 53 complex interactions between variables in shaping exposure risk from CDV and feline 54 parvovirus. For example, we found that lions were more likely to be exposed to CDV at a 55 young age but only in low rainfall years. 56 57 . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/569012 doi: bioRxiv preprint 3 4. When combined with our data calibration approach, our framework helped us to answer 58 questions about risk of pathogen exposure which are difficult to address with previous 59 methods. Our framework not only has the potential to aid in predicting disease risk in 60 animal populations, but also can be used to build robust predictive models suitable for 61 other ecological applications such as modelling species distribution or diversity patterns.
Title: How to make more from exposure data? An integrated machine Author Contributions Data Accessibility Statement Running Title Machine learning and pathogen exposure risk
answer:
True