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: Background: Reconstructing individual transmission events in an infectious disease outbreak can provide valuable information and help inform infection control policy. Recent years have seen considerable progress in the development of methodologies for reconstructing transmission chains using both epidemiological and genetic data. However, only a few of these methods have been implemented in software packages, and with little consideration for customisability and interoperability. Users are therefore limited to a small number of alternatives, incompatible tools with fixed functionality, or forced to develop their own algorithms at considerable personal effort. Results: Here we present outbreaker2, a flexible framework for outbreak reconstruction. This R package re-implements and extends the original model introduced with outbreaker, but most importantly also provides a modular platform allowing users to specify custom models within an optimised inferential framework. As a proof of concept, we implement the within-host evolutionary model introduced with TransPhylo, which is very distinct from the original genetic model in outbreaker, and demonstrate how even complex model results can be successfully included with minimal effort. Conclusions: outbreaker2 provides a valuable starting point for future outbreak reconstruction tools, and represents a unifying platform that promotes customisability and interoperability. Implemented in the R software, outbreaker2 joins a growing body of tools for outbreak analysis. 
 Title: outbreaker2: a modular platform for outbreak reconstruction
[EX A]: True

[EX Q]: Paragraph: Coronaviruses have large positive-strand RNA genomes that are 5′ capped and 3′ polyadenylated. The 5′-terminal two-thirds of the genome contain two open reading frames (ORFs), 1a and 1b, that together make up the viral replicase gene and encode two large polyproteins that are processed by viral proteases into 15-16 nonstructural proteins, most of them being involved in viral RNA synthesis. ORFs located in the 3′-terminal one-third of the genome encode structural and accessory proteins and are expressed from a set of 5′ leader-containing subgenomic mRNAs that are synthesized by a process called discontinuous transcription. Coronavirus protein synthesis not only involves cap-dependent translation mechanisms but also employs regulatory mechanisms, such as ribosomal frameshifting. Coronavirus replication is known to affect cellular translation, involving activation of stress-induced signaling pathways, and employing viral proteins that affect cellular mRNA translation and RNA stability. This chapter describes our current understanding of the mechanisms involved in coronavirus mRNA translation and changes in host mRNA translation observed in coronavirus-infected cells. 
 Title: Viral and Cellular mRNA Translation in Coronavirus-Infected Cells
[EX A]: False

[EX Q]: Paragraph: We review behavioural change models (BCMs) for infectious disease transmission in humans. Following the Cochrane collaboration guidelines and the PRISMA statement, our systematic search and selection yielded 178 papers covering the period 2010-2015. We observe an increasing trend in published BCMs, frequently coupled to (re)emergence events, and propose a categorization by distinguishing how information translates into preventive actions. Behaviour is usually captured by introducing information as a dynamic parameter (76/178) or by introducing an economic objective function, either with (26/178) or without (37/178) imitation. Approaches using information thresholds (29/178) and exogenous behaviour formation (16/178) are also popular. We further classify according to disease, prevention measure, transmission model (with 81/178 population, 6/178 metapopulation and 91/178 individuallevel models) and the way prevention impacts transmission. We highlight the minority (15%) of studies that use any real-life data for parametrization or validation and note that BCMs increasingly use social media data and generally incorporate multiple sources of information (16/178), multiple types of information (17/178) or both (9/178). We conclude that individual-level models are increasingly used and useful to model behaviour changes. Despite recent advancements, we remain concerned that most models are purely theoretical and lack representative data and a validation process. 
 Title: Behavioural change models for infectious disease transmission: a systematic review (2010-2015)
[EX A]:
False