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 Q]: 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
[EX A]: True

[EX Q]: Paragraph: Background: Wild birds are an important but to some extent under-studied reservoir for emerging pathogens. We used unbiased sequencing methods for virus discovery in shorebird samples from the Delaware Bay, USA; an important feeding ground for thousands of migratory birds. Findings: Analysis of shorebird fecal samples indicated the presence of a novel astrovirus and coronavirus. A sanderling sample yielded sequences with distant homology to avian nephritis virus 1, an astrovirus associated with acute nephritis in poultry. A ruddy turnstone sample yielded sequences with homology to deltacoronaviruses. Conclusions: Our findings highlight shorebirds as a virus reservoir and the need to closely monitor wild bird populations for the emergence of novel virus variants. 
 Title: Novel Coronavirus and Astrovirus in Delaware Bay Shorebirds
[EX A]: False

[EX Q]: Paragraph: The clinical consequences of co-infection with two or more respiratory viruses are poorly understood. We sought to determine if co-infection with pandemic 2009-2010 influenza A H1N1 (pH1N1) and another respiratory virus was associated with worse clinical outcomes. Methods: A retrospective cohort study was performed of all hospitalized patients with a positive respiratory viral panel (RVP) for two or more viruses within 72 hours of admission at our institution from October 2009 to December 2009. We compared patients infected with one respiratory virus to those with respiratory viral co-infection. We identified 617 inpatients with a positive RVP sample with a single virus and 49 inpatients with a positive RVP sample for two viruses (i.e. co-infection). Co-infected patients were significantly younger, more often had fever/chills, tachypnea, and they more often demonstrated interstitial opacities suggestive of viral pneumonia on the presenting chest radiograph (OR 7.5, 95% CI 3.4-16.5). The likelihood of death, length of stay, and requirement for intensive care unit level of care were similar in both groups, but patients with any respiratory virus co-infection were more likely to experience complications, particularly treatment for a secondary bacterial pneumonia (OR 6.8, 95% CI 3.3-14.2). Patients co-infected with pH1N1 and another respiratory virus were more likely to present with chest radiograph changes suggestive of a viral pneumonia, compared to mono-infection with pH1N1 (OR 16.9, 95% CI 4.5-62.7). By logistic regression using monoinfection with non-PH1N1 viruses as the reference group, co-infection with pH1N1 was the strongest independent predictor of treatment for a secondary bacterial pneumonia (OR 17.8, 95% CI 6.7-47.1). Patients with viral co-infection, particularly with pH1N1, were more likely to have chest radiograph features compatible with a viral pneumonia and complications during their hospital course, particularly treatment for secondary bacterial pneumonia. Despite this, co-infection was not associated with ICU admission. 
 Title: Clinical Characteristics and Outcomes in Hospitalized Patients with Respiratory Viral Co-Infection during the 2009 H1N1 Influenza Pandemic
[EX A]:
False