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".

[Q]: Paragraph: M.N.); halromaihi@moph.gov.qa (H.A.-R.); malthani@moph.gov.qa (M.A.T.); dralmarri@moph.gov.qa (S.A.-M.); malhajri1@moph.gov.qa (M.A.) Abstract: MERS-CoV (Middle East respiratory syndrome corona virus) antibodies were detected in camels since 1983, but the first human case was only detected in 2012. This study sought to identify and quantify possible drivers for the MERS-CoV emergence and spillover to humans. A list of potential human, animal and environmental drivers for disease emergence were identified from literature. Trends in possible drivers were analyzed from national and international databases, and through structured interviews with experts in Qatar. The discovery and exploitation of oil and gas led to a 5-fold increase in Qatar GDP coupled with a 7-fold population growth in the past 30 years. The lifestyle gradually transformed from Bedouin life to urban sedentary life, along with a sharp increase in obesity and other comorbidities. Owing to substantial governmental support, camel husbandry and competitions flourished, exacerbating the already rapidly occurring desertification that forced banning of free grazing in 2005. Consequently, camels were housed in compact barns alongside their workers. The transition in husbandry leading to high density camel farming along with increased exposure to humans, combined with the increase of camel movement for the racing and breeding industry, have led to a convergence of factors driving spillover of MERS-CoV from camels to humans. 13 of 15 to provide "hard evidence" of causality. Nevertheless, this is the first comprehensive quantitative overview of possible drivers of MERS-CoV in Qatar. Several key changes were shown to involve camels, humans, the economy, and the environment in Qatar during the last 30 years. Our study indicates that the rapid increase in camel ownership, leading to the presence of camels from different origins in a high-density environment mixed together with human and other animal species may have offered the right circumstances for the virus to spread from camels to humans. The other key changes that were described collectively contributed to this situation. Further understanding of the drivers that led to the emergence of MERS-CoV can serve as input for MERS-CoV surveillance and control measures to prevent further spread of MERS-CoV and reduce transmission from camels to humans. 
 Title: viruses Drivers of MERS-CoV Emergence in Qatar
[A]: True


[Q]: 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
[A]: True


[Q]: Paragraph: Background: The laboratory has always played a very critical role in diagnosis of the diseases. The success of any disease programme is based on a functional laboratory network. Health laboratory services are an integral component of the health system. Efficiency and effectiveness of both clinical and public health functions including surveillance, diagnosis, prevention, treatment, research and health promotion are influenced by reliable laboratory services. The establishment of the African Regional polio laboratory for the Polio Eradication Initiative (PEI) has contributed in supporting countries in their efforts to strengthen laboratory capacity. On the eve of the closing of the program, we have shown through this article, examples of this contribution in two countries of the African region: Côte d'Ivoire and the Democratic Republic of Congo. Methods: Descriptive studies were carried out in Côte d'Ivoire (RCI) and Democratic Republic of Congo (DRC) from October to December 2014. Questionnaires and self-administered and in-depth interviews and group discussions as well as records and observation were used to collect information during laboratory visits and assessments. Results: The PEI financial support allows to maintain the majority of the 14 (DRC) and 12 (RCI) staff involved in the polio laboratory as full or in part time members. Through laboratory technical staff training supported by the PEI, skills and knowledge were gained to reinforce laboratories capacity and performance in quality laboratory functioning, processes and techniques such as cell culture. In the same way, infrastructure was improved and equipment provided. General laboratory quality standards, including the entire laboratory key elements was improved through the PEI accreditation process. Conclusion: The Polio Eradication Initiative (PEI) is a good example of contribution in strengthening public health laboratories systems in the African region. It has established strong Polio Laboratory network that contributed to the strengthening of capacities and its expansion to surveillance of other viral priority diseases such as measles, yellow fever, Influenza, MERS-CoV and Ebola. This could serve as lesson and good example of laboratory based surveillance to improving diseases prevention, detection and control in our middle and low income countries as WHO and partners are heading to polio eradication in the world. 
 Title: c World Health Organization Intercountry Support Office, Ouagadougou, Burkina Faso d Expanded Program on Immunization, WHO Regional Office for Africa, Inter-Country Support Team
[A]:
True