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".

Example Input: Paragraph: Background: The burden of Hepatitis C virus (HCV) has become more and more considerable in China. A macroscopic spatial analysis of HCV infection that can provide scientific information for further intervention and disease control is lacking. Methods: All geo-referenced HCV cases that had been recorded by the China Information System for Disease Control and Prevention (CISDCP) during 2005-2011 were included in the study. In order to learn about the changes of demographic characteristics and geographic distribution, trend test and spatial analysis were conducted to reflect the changing pattern of HCV infection. Results: Over 770,000 identified HCV infection cases had specific geographic information during the study period (2005)(2006)(2007)(2008)(2009)(2010)(2011). Ratios of gender (Male/Female, Z-value = 218.53, P,0.001), age group (#30 years old/$31 years old, Z-value = 2 51.03, P,0.001) and diagnosis type (Clinical diagnosis/Laboratory diagnosis, Z-value = 2130.47, P,0.001) declined. HCV infection was not distributed randomly. Provinces Henan, Guangdong, Guangxi, Xinjiang, and Jilin reported more than 40,000 HCV infections during 2005 to 2011, accounting for 43.91% of all cases. The strength of cluster of disease was increasing in China during the study period. Overall, 11 provinces had once been detected as hotspots during 7 years, most of which were located in the central or border parts of China. Tibet, Qinghai, Jiangxi were the regions that had coldspots. The number of clustering of HCV infection among older adults increased in recent years. Specific interventions and prevention programs targeting at main HCV epidemic areas are urgently in need in mainland China. 
 Title: Spatial Analysis on Hepatitis C Virus Infection in Mainland China: From 2005 to 2011
Example Output: False

Example 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
Example Output: True

Example Input: Paragraph: The current emergence of the novel coronavirus pandemic caused by SARS-CoV-2 demands the development of new therapeutic strategies to prevent rapid progress of mortalities. The coronavirus spike (S) protein, which facilitates viral attachment, entry and membrane fusion is heavily glycosylated and plays a critical role in the elicitation of the host immune response. The spike protein is comprised of two protein subunits (S1 and S2), which together possess 22 potential N-glycosylation sites. Herein, we report the glycosylation mapping on spike protein subunits S1 and S2 expressed on human cells through high resolution mass spectrometry. We have characterized the quantitative N-glycosylation profile on spike protein and interestingly, observed unexpected O-glycosylation modifications on the receptor binding domain (RBD) of spike protein subunit S1. Even though O-glycosylation has been predicted on the spike protein of SARS-CoV-2, this is the first report of experimental data for both the site of O-glycosylation and identity of the O-glycans attached on the subunit S1. Our data on the N-and O-glycosylation is strengthened by extensive manual interpretation of each glycopeptide spectra in addition to using bioinformatics tools to confirm the complexity of glycosylation in the spike protein. The elucidation of the glycan repertoire on the spike protein provides insights into the viral binding studies and more importantly, propels research towards the development of a suitable vaccine candidate. author/funder. All rights reserved. No reuse allowed without permission. 
 Title: Deducing the N-and O-glycosylation profile of the spike protein of novel coronavirus SARS-CoV-2
Example Output:
True