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 CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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: Dengue virus (DENV) emerged from the sylvatic environment and colonized urban settings, being sustained in a human-Aedes-human transmission chain, mainly by the bites of females of the anthropophilic species Aedes aegypti. Herein, we sought evidence for fine-tuning in viral codon usage, possibly due to viral adaptation to human transmission. We compared the codon adaptation of DENV serotype 2 (DENV-2) genotypes from urban and sylvatic habitats and tried to correlate the findings with key evolutionary determinants. We found that DENV-2 codons of urban and sylvatic genotypes had a higher CAI to humans than to Ae. aegypti. Remarkably, we found no significant differences in codon adaptation to human between urban American/Asian and sylvatic DENV-2 genotypes. Moreover, CAI values were significantly different, when comparing all genotypes to Ae. aegypti codon preferences, with lower values for sylvatic than urban genotypes. In summary, our findings suggest the presence of a molecular signature among the genotypes that circulate in sylvatic and urban environments, and may help explain the trafficking of DENV-2 strains to an urban cycle. 
 Title: Codon adaptation biases among sylvatic and urban genotypes of Dengue virus type 2
answer:
False


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Paragraph: The aim of this study was to explore the role of the Diffusion of Innovations framework in adopting an infection prevention and control program (IPCP) in a low and middle income (LMI) country, the Republic of Kiribati. Methods: Case-study methodology was used to examine and contextualise the analysis of the Republic of Kiribati's adoption of the IPCP from 2003 to 2010. Data were collected from multiple sources including semi-structured interviews, IPCP documentation, program evaluation and a healthcare worker survey. Data were subjected to thematic analysis and descriptive statistics where relevant to the study design. Results: It was found that the self-initiated progression of activities and stimuli has resulted in the successful adoption of a comprehensive IPCP. The process followed the staged model of the classic Diffusion of Innovations process in organisations described by Everett Rogers. Conclusion: This case study provides an illustration of how a comprehensive IPCP can be adopted in a LMI country setting with little involvement from external agencies. It identifies key stimuli, opportunities and activities which could be similarly adopted and implemented by other LMI countries. 
 Title: Success in the South Pacific: a case study of successful diffusion of an infection prevention and control program
answer:

False