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".
question:
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 CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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


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Paragraph: The avian influenza virus H5N1 and the 2009 swine flu H1N1 are potentially serious pandemic threats to human health, and air travel readily facilitates the spread of infectious diseases. However, past studies have not yet incorporated the effects of air travel on the transmission of influenza in the construction of mathematical epidemic models. Therefore, this paper focused on the human-to-human transmission of influenza, and investigated the effects of air travel activities on an influenza pandemic in a small-world network. These activities of air travel include passengers' consolidation, conveyance and distribution in airports and flights. Dynamic transmission models were developed to assess the expected burdens of the pandemic, with and without control measures. This study also investigated how the small-world properties of an air transportation network facilitate the spread of influenza around the globe. The results show that, as soon as the influenza is spread to the top 50 global airports, the transmission is greatly accelerated. Under the constraint of limited resources, a strategy that first applies control measures to the top 50 airports after day 13 and then soon afterwards to all other airports may result in remarkable containment effectiveness. As the infectiousness of the disease increases, it will expand the scale of the pandemic, and move the start time of the pandemic ahead. 
 Title: Transmission and control of an emerging influenza pandemic in a small-world airline network
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False


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Paragraph: Background. Respiratory syncytial virus (RSV) has been implicated in the etiology of adult communityacquired pneumonia (CAP). We investigated RSV infection in Chilean adults with CAP using direct viral detection, real-time reverse-transcription polymerase chain reaction (rtRT-PCR), and serology (microneutralization assay). Methods. RSV, other respiratory viruses, and bacteria were studied by conventional and molecular techniques in adults aged $18 years presenting with CAP to the healthcare facilities in Santiago, Chile from February 2005 through December 2007. Results. All 356 adults with CAP enrolled had an acute blood sample collected at enrollment, and 184 had a convalescent blood sample. RSV was detected in 48 cases (13.4%). Immunofluorescence assay and viral isolation each detected only 1 infection (0.2%), whereas rtRT-PCR was positive in 32 (8.9%) cases and serology was positive in 20 (10.8%) cases. CAP clinical characteristics were similar in RSV-infected and non-RSV-infected cases. RSV-specific geometric mean serum-neutralizing antibody titer (GMST) was significantly lower at admission in the 48 RSV-infected cases compared with 308 non-RSV-infected adults (GMST in log 2 : RSV/A 8.1 vs 8.9, and RSV/B 9.3 vs 10.4; P , .02). Conclusions. RSV infection is frequent in Chilean adults with CAP. Microneutralization assay was as sensitive as rtRT-PCR in detecting RSV infection and is a good adjunct assay for diagnostic research. High RSV-specific serum-neutralizing antibody levels were associated with protection against common and severe infection. The development of a vaccine could prevent RSV-related CAP in adults. 
 Title: Role of Neutralizing Antibodies in Adults With Community-Acquired Pneumonia by Respiratory Syncytial Virus
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True