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: Background. Experimental inoculation is an important tool for common cold and asthma research. Producing rhinovirus (RV) inocula from nasal secretions has required prolonged observation of the virus donor to exclude extraneous pathogens. We produced a RV-A16 inoculum using reverse genetics and determined the dose necessary to cause moderate colds in seronegative volunteers. Methods. The consensus sequence of RV-A16 from a previous inoculum was cloned, and inoculum virus was produced using reverse genetics techniques. After safety testing, volunteers were inoculated with either RV-A16 (n = 26) or placebo (n = 10), Jackson cold scores were recorded, and nasal secretions were tested for shedding of RV-A16 ribonucleic acid. Results. The reverse genetics process produced infectious virus that was neutralized by specific antisera and had a mutation rate similar to conventional virus growth techniques. The 1000 median tissue culture infectious dose (TCID 50 ) dose produced moderate colds in most individuals with effects similar to that of a previously tested conventional RV-A16 inoculum. Conclusions. Reverse genetics techniques produced a RV-A16 inoculum that can cause clinical colds in seronegative volunteers, and they also serve as a stable source of virus for laboratory use. The recombinant production procedures eliminate the need to derive seed virus from nasal secretions, thus precluding introduction of extraneous pathogens through this route. 
 Title: Development of a Rhinovirus Inoculum Using a Reverse Genetics Approach
[EX A]: False

[EX Q]: Paragraph: Mathematical models when applied to infectious disease data can provide extremely useful insights into the possible future impacts of potential emerging epidemics and how they might be best controlled or mitigated. Modelling, which is like any other hypothesis-driven approach, aims to develop a better understanding of biological phenomena. However, diseases processes generally, and particularly those related to transmission, will in many cases be imperfectly understood or too complex to systematically describe, so models will necessarily be simplifications of the overall system. It is essential, therefore, that models are designed carefully and used appropriately. Key to this is identifying what specific questions a model might be expected to answer and what data is available to inform the model. A particular type of model might be fine for one particular situation but highly inappropriate for another. It is also important to appreciate and communicate what simplifications and assumptions have had to be made and how this might affect the robustness of the modelling results. It is also particularly important to understand that models frequently make what can be hidden assumptions about underlying processes because of the way they have been constructed and these assumptions also need to be carefully considered and made explicit, particularly for non-expert audiences. This chapter, therefore, provides a brief introduction to some of these aspects of epidemic modelling for those that might be less familiar with them. 
 Title: Chapter 23 Modelling Emerging Viral Epidemics for Public Health Protection
[EX A]:
False