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: Estimating the case-fatality risk (CFR)-the probability that a person dies from an infection given that they are a case-is a high priority in epidemiologic investigation of newly emerging infectious diseases and sometimes in new outbreaks of known infectious diseases. The data available to estimate the overall CFR are often gathered for other purposes (e.g., surveillance) in challenging circumstances. We describe two forms of bias that may affect the estimation of the overall CFR-preferential ascertainment of severe cases and bias from reporting delays-and review solutions that have been proposed and implemented in past epidemics. Also of interest is the estimation of the causal impact of specific interventions (e.g., hospitalization, or hospitalization at a particular hospital) on survival, which can be estimated as a relative CFR for two or more groups. When observational data are used for this purpose, three more sources of bias may arise: confounding, survivorship bias, and selection due to preferential inclusion in surveillance datasets of those who are hospitalized and/or die. We illustrate these biases and caution against causal interpretation of differential CFR among those receiving different interventions in observational datasets. Again, we discuss ways to reduce these biases, particularly by estimating outcomes in smaller but more systematically defined cohorts ascertained before the onset of symptoms, such as those identified by forward contact tracing. Finally, we discuss the circumstances in which these biases may affect non-causal interpretation of risk factors for death among cases. PLOS Neglected Tropical Diseases | 
 Title: Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks

Ex Output:
False


Ex Input:
Paragraph: A novel porcine deltacoronavirus (PdCV) was first discovered in Ohio and Indiana in February 2014, rapidly spread to other states in the United States and Canada, and caused significant economic loss in the swine industry. The origin and virulence of this novel porcine coronavirus are not known. Here, we characterized U.S. PdCV isolates and determined their virulence in gnotobiotic and conventional piglets. Genome analyses revealed that U.S. PdCV isolates possess unique genetic characteristics and share a close relationship with Hong Kong and South Korean PdCV strains and coronaviruses (CoVs) of Asian leopard cats and Chinese ferret-badgers. The PdCV-positive intestinal content (Ohio CVM1) and the cell culture-adapted PdCV Michigan (MI) strain were orally inoculated into gnotobiotic and/or conventional piglets. Within 1 to 3 days postinfection, profuse watery diarrhea, vomiting, and dehydration were observed. Clinical signs were associated with epithelial necrosis in the gastric pits and small intestine, the latter resulting in severe villous atrophy. Mild interstitial pneumonia was identified in the lungs of PdCVinfected piglets. High levels of viral RNA (8 to 11 log RNA copies/g) were detected in intestinal tissues/luminal contents and feces of infected piglets, whereas moderate RNA levels (2 to 5 log RNA copies/g) were detected in blood, lung, liver, and kidney, indicating multisystemic dissemination of the virus. Polyclonal immune serum against PdCV but not immune serum against porcine epidemic diarrhea virus (PEDV) reacted with PdCV-infected small-intestinal epithelial cells, indicating that PdCV is antigenically distinct from PEDV. Collectively, we demonstrate for the first time that PdCV caused severe gastrointestinal diseases in swine. Indiana and subsequently spread rapidly across the United States and Canada, causing significant economic losses. Here, we report the detailed genetic characterization, phylogeny, and virulence of emergent PdCV strains in the United States. We found that PdCV caused severe diarrhea, vomiting, and dehydration in gnotobiotic and conventional piglets, signs that were clinically indistinguishable from those caused by PEDV and TGEV. In addition to extensive intestinal lesions, PdCV caused significant lesions in the stomach and mild pulmonary lesions that have not been reported for TGEV and PEDV. The finding that PdCV is a significant enteric disease of swine highlights the need to develop effective measures to control this disease. . 2015. Origin, evolution, and virulence of porcine deltacoronaviruses in the United States. mBio 6(2): e00064-15. 
 Title: Origin, Evolution, and Virulence of Porcine Deltacoronaviruses in the United States

Ex Output:
False


Ex Input:
Paragraph: understanding the determinants of virus transmission is a fundamental step for effective design of screening and intervention strategies to control viral epidemics. Phylogenetic analysis can be a valid approach for the identification of transmission chains, and very-large data sets can be analysed through parallel computation. Here we propose and validate a new methodology for the partition of large-scale phylogenies and the inference of transmission clusters. This approach, on the basis of a depth-first search algorithm, conjugates the evaluation of node reliability, tree topology and patristic distance analysis. The method has been applied to identify transmission clusters of a phylogeny of 11,541 human immunodeficiency virus-1 subtype B pol gene sequences from a large Italian cohort. molecular transmission chains were characterized by means of different clinical/demographic factors, such as the interaction between male homosexuals and male heterosexuals. our method takes an advantage of a flexible notion of transmission cluster and can become a general framework to analyse other epidemics. ,15 on behalf of the ARCA collaborative group ARTICLE nATuRE CommunICATIons | 
 Title: A novel methodology for large-scale phylogeny partition on behalf of the ARCA collaborative group

Ex Output:

True