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".

Paragraph: In this paper, we propose and discuss a stochastic SIRS epidemic model with non-monotone incidence rate under regime-switching. First of all, we show that there is a unique positive solution, which is a prerequisite for analyzing the long-term behavior of the stochastic model. Then, a threshold dynamic determined by the basic reproduction number R s 0 is established: the disease can be eradicated almost surely if R s 0 < 1 and under mild extra conditions, whereas if R s 0 > 1 , the densities of the distributions of the solution can converge in L 1 to an invariant density by using the Markov semigroups theory. Finally, based on realistic parameters obtained from previous literatures, numerical simulations have been performed to verify our analytical results. 
 Title: A stochastic SIRS epidemic model with non-monotone incidence rate under regime-switching
True

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
True

Paragraph: Background: Taiwan has been considered free from canine parvovirus type 2c (CPV-2c) based on the last report of canine parvovirus type 2 (CPV-2) surveillance. However, since January 2015, the first report of CPV-2c in a puppy has occurred in Taiwan. There is currently limited information about the CPV-2c variant in Taiwan. In the present study, we characterized the previously unidentified CPV-2c variant and investigated the distribution of CPV-2 variants in Taiwan. Methods: During January 2014 to April 2016, fecal or rectal swab samples from 99 dogs with suspected CPV-2 infection in Taiwan were collected. Eighty-eight were identified as being either CPV-2a, −2b or -2c variants positive by real-time PCR and sequence analysis. Results: Sequence analysis of the 88 isolates confirmed CPV-2c as the dominant variant (54.6 %), followed by CPV-2b (26.1 %) and . Phylogenetic analysis demonstrated that the recent CPV-2c variants are similar to the Chinese CPV-2c strain but can be considered as novel Asian CPV-2c isolates. Conclusion: The present study provides evidence for the existence of a novel CPV-2c variant in Taiwan. 
 Title: Identification of a novel canine parvovirus type 2c in Taiwan

True