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".

Ex Input:
Paragraph: Background: Host genetic factors may play a role in susceptibility and resistance to SARS associated coronavirus (SARS-CoV) infection. The study was carried out to investigate the association between the genetic polymorphisms of 2',5'-oligoadenylate synthetase 1 (OAS1) gene as well as myxovirus resistance 1 (MxA) gene and susceptibility to SARS in Chinese Han population. A hospital-based case-control study was conducted. A collective of 66 SARS cases and 64 close contact uninfected controls were enrolled in this study. End point real time polymerase chain reaction (PCR) and PCR-based Restriction Fragment Length Polymorphism (RFLP) analysis were used to detect the single nucleic polymorphisms (SNPs) in OAS1 and MxA genes. Information on other factors associated with SARS infection was collected using a pre-tested questionnaire. Univariate and multivariate logistic analyses were conducted. Results: One polymorphism in the 3'-untranslated region (3'-UTR) of the OAS1 gene was associated with SARS infection. Compared to AA genotype, AG and GG genotypes were found associated with a protective effect on SARS infection with ORs (95% CI) of 0.42 (0.20~0.89) and 0.30 (0.09~0.97), respectively. Also, a GT genotype at position 88 in the MxA gene promoter was associated with increased susceptibility to SARS infection compared to a GG genotype (OR = 3.06, 95% CI: 1.25~7.50). The associations of AG genotype in OAS1 and GT genotype in MxA remained significant in multivariate analyses after adjusting for SARS protective measures (OR = 0.38, 95% CI: 0.14~0.98 and OR = 3.22, 95% CI: 1.13~9.18, respectively). Conclusion: SNPs in the OAS1 3'-UTR and MxA promoter region appear associated with host susceptibility to SARS in Chinese Han population.
Title: BMC Infectious Diseases Association of SARS susceptibility with single nucleic acid polymorphisms of OAS1 and MxA genes: a case-control study

Ex Output:
True

Ex Input:
Paragraph: As infectious disease outbreaks emerge, public health agencies often enact vaccination and social distancing measures to slow transmission. Their success depends on not only strategies and resources, but also public adherence. Individual willingness to take precautions may be influenced by global factors, such as news media, or local factors, such as infected family members or friends. Here, we compare three modes of epidemiological decisionmaking in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns. Individuals decide whether to adopt a recommended intervention based on overall disease prevalence, the proportion of social contacts infected, or the number of social contacts infected. While all strategies can substantially mitigate transmission, vaccinating (or self isolating) based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources. Unlike the other strategies, it has a substantial herd effect, providing indirect protection to a large fraction of the population. misinformation from the "anti-vaxxer" movement [8, [11] [12] [13] . Recently, there have been calls for a special government commission on vaccine safety, despite overwhelming scientific consensus that vaccines are both safe and effective [12, [14] [15] [16] . As outbreaks unfold, people can take a variety precautionary measures to avoid infection, including immunization and social distancing [1, [17] [18] [19] . They often judge personal risk based on their impressions of overall disease prevalence and severity [2, [20] [21] [22] [23] . When infection risk appears low, small risks of adverse effects from the vaccine can seem relatively important and cause vaccine coverage to drop below levels required to control transmission. A variety of other factors can influence the perceived utility of disease prevention, including epidemiological news and rumors, costs of vaccination and other control measures, trust in health professionals, government agencies, media and non-official information sources, as well as societal pressure to ensure the health of one's children [24] [25] [26] [27] [28] [29] . Studies have shown that media reports about outbreaks that specify numbers of cases, hospitalizations or deaths, can influence avoidance behavior and contact patterns at both individual and community levels. In some cases, oversimplified or erroneous media reports regarding flawed vaccines can trigger panic and increases in vaccine hesitancy [30] [31] [32] [33] . For both seasonal and pandemic influenza, such interactions between vaccination decision-making and transmission dynamics can profoundly shape the course of epidemics [20, [34] [35] [36] . Individual intervention decisions can have far-reaching effects. For example, vaccination protects not only the immunized individual, but also social contacts who might have been infected by the individual. Social distancing decisions may break chains of transmission by protecting the decision-maker and more generally disrupting social dynamics. Following [37, 38] , we refer to this indirect protection as a herd effect. Although previously equated to the reduction in incidence in the unimmunized population [38], we quantify the herd effect of an intervention effort by estimating the number of infections averted per vaccine administered (or per individual social distancing action). The general phenomenon in which individual intervention actions reduce the risk of infection to others has also been called vaccination efficiency, vaccination effectiveness, herd immunity, and indirect protection [37] [38] [39] [40] . The magnitude of the herd effect will critically depend on contact patterns [41, 42]. Measures taken by gregarious individuals may have greater immediate benefits than those taken by solitary individuals, with downstream epidemiological consequences modulated by the full social network [43, 44] . Contact patterns may also influence the decision-making process itself, by constraining epidemiological perspectives. When gauging infection risk, individuals may consider global information (e.g., from news media) or local first-hand encounters with disease (e.g., infected acquaintances, friends or family members) [30, 31] . While traditional compartmental models assume homogeneity in both epidemiological risks and intervention benefits, network-based models provide a tractable framework for studying the complex interplay between contact networks, intervention decision making and disease transmission [34, [45] [46] [47] [48] [49] [50] [51] . Here, we investigate the epidemiological impacts of different decision paradigms using a network-based SIR epidemic model, in which individuals also make vaccination or social distancing choices based on their perceived epidemiological risks. Depending on the decision model, they estimate either overall disease prevalence, their number of infected social contacts, or their fraction of infected social contacts. When the perceived threat is sufficiently high, they take a measure that immediately affords full protection for the duration of the epidemic. We compare the efficacy of these three different paradigms across a range of diseases in a realistically heterogeneous network, and show that the most naive model-simply counting one's infected contacts-affords the most epidemiological protection using the least amount of resources (e.g., vaccinations or economic costs associated with social distancing). Local risk perception enhances epidemic control PLOS ONE | https://doi.
Title: Local risk perception enhances epidemic control

Ex Output:
True

Ex Input:
Paragraph: WHO has listed several priority diseases with epidemic potential for which there are no, or insufficient, medical countermeasures. In response, the Bill & Melinda Gates Foundation (with support from PricewaterhouseCoopers) coordinated subject matter experts to create a preparedness plan for Disease X. Disease X is caused by Pathogen X, an infectious agent that is not currently known to cause human disease, but an aetiologic agent of a future outbreak with epidemic or pandemic potential. We have identified crucial areas for acceleration in medical countermeasure product development and international coordination. We have also reviewed novel platforms and process improvements related to manufacturing, which could revolutionise the response to the next pandemic. Finally, we created several coordination and engagement guides. These guides range from the rational design of an intervention target product profile, to the key facets of vaccine and therapeutic development, to accelerated manufacturing and regulatory mechanisms. In this Personal View, we provide a high-level summary of the outcomes of the medical countermeasure development workstream, intended for a broad audience including academia, medical countermeasure developers, and multilateral coordinating bodies. We hope that they might find this piece useful in prioritising strategic investments and efforts to accelerate medical countermeasure development. We observed that in-depth analyses of clinical trial design, chemistry, manufacturing and control activities, and accelerated regulatory pathways are necessary for shortening the timelines for the product development of medical countermeasures. We intend to cover these topics in future publications.
Title: Personal View Disease X: accelerating the development of medical countermeasures for the next pandemic

Ex Output:
False