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".
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Question: Paragraph: Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic. Pandemic growth rates from sequences E. de Silva et al. 1799 Pandemic growth rates from sequences E. de Silva et al. 1807  
 Title: Inferring pandemic growth rates from sequence data

Answer: True


Question: Paragraph: Fusion of MHV-3-immune splenocytes from MHV-3-resistant A/J murine strain, with NS myeloma cells produced several hybridomas. Among eight hybridoma clones, the 1E7A4H1 clone secreted kappa IgG2a apparently directed against the nucleoprotein of the MHV-3 virion. The monoclonal antibody was able to neutralize tile in vitro cytopathic effect of MHV-3 on cultured L2 cells, and was detected by indirect immunofluorescence on MHV-3-infected cultured Y AC cells. In addition, it conferred a significant protection against MHV-3-induced acute disease, if injected intraperitoneally to C57BL/6 mice before inoculation with MHV-3. , Mouse hepatitis viruses (MHVs), members of the coronavirus family, produce various diseases i n t h e mouse and other animal species [16] . They exhibit different tissue tropisms and patterns of infection [1]. The susceptibility of the animals to the virus-induced diseases appears to be genetically controlled at different levels [16] . Serological neutralization patterns of several of the MHVs are very complex [11, 15] and the three structural proteins demonstrated variable degrees of conservation [4, 13] . Coronaviruses are frequently endemic in laboratory mouse colonies [8] . Neutralizing monoclonal antibodies have been recently prepared against the J H M (MHV-4) [2, 4, 17] and MHV-2 [9] strains. In this brief note, we report on a monoclonaI antibody, apparently specific for the nueleo-* Present address: Instituto Butantan, Laboratoria Especial de Immunologia Viral, S~o Paulo, Brazil. 
 Title: Archives of Virology Protection from mouse hepatitis virus type 3-induced acute disease by an anti-nucleoprotein monoclonal antibody Brief Report

Answer: True


Question: Paragraph: During the prevention of coronavirus disease 2019 , epidemiological data is essential for controlling the source of infection, cutting off the route of transmission, and protecting vulnerable populations. Following Law of the People's Republic of China on Prevention and Treatment of Infectious Diseases and other related regulations, medical institutions have been authorized to collect the detailed information of patients, while it is still a formidable task in megacities because of the significant patient mobility and the existing information sharing barrier. As a smart city which strengthens precise epidemic prevention and control, Shanghai has established a multi-department platform named "one-net management" on dynamic information monitoring. By sharing epidemiological data with medical institutions under a safe environment, we believe that the ability to prevent and control epidemics among medical institutions will be effectively and comprehensively improved. 
 Title: Feature Demand Analysis and Management Suggestion: Sharing Epidemiological Data Among Medical Institutions in Megacities for Epidemic Prevention and Control

Answer:
False