In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

[Q]: Four Massachusetts-type (Mass-type) strains of infectious bronchitis coronavirus (IBV) were compared genetically with the pathogenic M41 and H120 vaccine strains using the complete genomic sequences. The results revealed that strains ck/CH/LNM/091017 and ck/CH/LDL/101212 were closely related to the H120 vaccine, which suggests that they might represent re-isolations of vaccine strains or variants of vaccine strains that have resulted from the accumulated point mutations after several passages in chickens. In contrast, strains ck/CH/LHLJ/07VII and ck/CH/LHLJ/100902 had a close genetic relationship with the pathogenic M41 strain. In addition, molecular markers have been identified that distinguish between field and vaccine (or vaccine-like) Mass-type viruses, which may be able to differentiate between field and vaccine strains for diagnostic purposes. Phylogenetic analysis, and pairwise comparison of full-length genomes and the nine genes, identified the occurrence of recombination events in the genome of strain CK/VH/LHLJ/07VII, which suggests that this virus originated from recombination events between M41-and H120-like strains at the switch site located at the 3 0 end of the nucleocapsid (N) genes. To our knowledge, this is the first time that evidence for the evolution and natural recombination under field conditions between Mass-type pathogenic and vaccinal IBV strains has been documented. These findings provide insights into the emergence and evolution of the Mass-type IB coronaviruses and may help to explain the emergence of Mass-type IBV in chicken flocks all over the world.
[A]: Comparative analysis of four Massachusetts type infectious bronchitis coronavirus genomes reveals a novel Massachusetts type strain and evidence of natural recombination in the genome


[Q]: Background: Influenza-like illness (ILI) may be caused by a variety of pathogens. Clinical observations are of little help to recognise myxovirus infection and implement appropriate prevention measures. The limited use of molecular tools underestimates the role of other common pathogens. Objectives: During the early weeks of the 2009-2010 flu pandemic, a clinical and virological survey was conducted in adult and paediatric patients with ILI referred to two French University hospitals in Paris and Tours. Aims were to investigate the different pathogens involved in ILI and describe the associated symptoms. Methods: H1N1v pandemic influenza diagnosis was performed with real time RT-PCR assay. Other viral aetiologies were investigated by the molecular multiplex assay RespiFinder19H. Clinical data were collected prospectively by physicians using a standard questionnaire. Results: From week 35 to 44, endonasal swabs were collected in 413 patients. Overall, 68 samples (16.5%) were positive for H1N1v. In 13 of them, other respiratory pathogens were also detected. Among H1N1v negative samples, 213 (61.9%) were positive for various respiratory agents, 190 in single infections and 23 in mixed infections. The most prevalent viruses in H1N1v negative single infections were rhinovirus (62.6%), followed by parainfluenza viruses (24.2%) and adenovirus (5.3%). 70.6% of H1N1v cases were identified in patients under 40 years and none after 65 years. There was no difference between clinical symptoms observed in patients infected with H1N1v or with other pathogens. Our results highlight the high frequency of non-influenza viruses involved in ILI during the pre-epidemic period of a flu alert and the lack of specific clinical signs associated with influenza infections. Rapid diagnostic screening of a large panel of respiratory pathogens may be critical to define and survey the epidemic situation and to provide critical information for patient management.
[A]: High Burden of Non-Influenza Viruses in Influenza-Like Illness in the Early Weeks of H1N1v Epidemic in France


[Q]: Individual-based epidemiology models are used to study the effects of public health interventions and changes in individual behavior to the dynamics of infectious diseases [1] . A detailed description of the individual-based model used in simulating the outbreaks in this study is presented in [2] . The individual-based model is made up of a dynamic social contact network and a disease transmission model. The social contact network which is a synthetic representation of a particular geographical region is generated using United States Census data. The structure of the underlying network is important since interactions between agents influences how disease propagates during epidemic simulations. In this initial study, neither changes in individual behavior nor intervention strategies are explored. However, in later studies to better understand the implications of specific intervention strategies, we would forecast disease dynamics given that effective measures have been introduced to control the spread of an outbreak. The construction of the dynamic social contact network requires two steps. In step 1 a synthetic population is created. Individuals in the synthetic population are tagged to realistic demographics while preserving the confidentiality of the original data sets. Each individual is assigned to a household using a decision tree based on demographic information (such as number of people in a household, number of children etc.) from data provided in SF3 and PUMA (Public Use Microdata Area) census files. Households are placed in realistic geographical regions. The synthetic population is statistically identical to the census aggregated at the block level. More information can be found in [3], [4] and [5]. Step 2 assigns a daily activity list with specified durations (start and end times) to each household based on several thousand responses to an activity or time-use survey. The activity templates which vary by region given factors such as the geographical location and age composition of the population, are created based on the National
[A]:
A Simulation Optimization Approach to Epidemic Forecasting Supporting Information Individual-based Model