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.

[EX Q]: An RNA-based, non-cytopathic replicon vector system, based on the flavivirus Kunjin, has shown considerable promise as a new vaccine delivery system. Here we describe the testing in mice of four different SIVmac239 gag vaccines delivered by Kunjin replicon virus-like-particles. The four vaccines encoded the wild type gag gene, an RNA-optimised gag gene, a codon-optimised gag gene and a modified gag-pol gene construct. The vaccines behaved quite differently for induction of effector memory and central memory responses, for mediation of protection, and with respect to insert stability, with the SIV gag-pol vaccine providing the optimal performance. These results illustrate that for an RNA-based vector the RNA sequence of the antigen can have profound and unforeseen consequences on vaccine behaviour.
[EX A]: Kunjin replicon-based simian immunodeficiency virus gag vaccines

[EX Q]: a b s t r a c t H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.
[EX A]: Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

[EX Q]: The global burden of morbidity and mortality associated with communicable diseases is nearly three times that of cancer and 70% higher than that of cardiovascular disease [1] . Much of the infectious disease burden is caused by diseases that have a long history in human populations, such as measles, influenza, and pertussis. In recent years, additional disease burden has emerged from outbreaks of HIV, Chikungunya, Zika virus, Ebola virus, Middle East respiratory syndrome, coronavirus, Nipah virus, and Lassa fever, underscoring the continued vulnerability of human populations to novel and re-emerging threats. Mathematical models of infectious disease can be used to understand and predict epidemiological trajectories of outbreaks; they can then be applied to optimize strategies for mitigating disease transmission. These models incorporate pertinent environmental factors, such as seasonal fluctuations, that affect survival or activity of pathogens and their vectors. They can also address complexities of host behavioral change, including local and global human societal changes, health policy, clinical trial design, and resource allocation, which have been increasingly guided by modeling studies. The epidemiology of infectious diseases and their control is complex. While models can be structured to represent the intricacies of a disease system, the quality of the information they provide depends on balancing existing data, methodological rigor, and simplifying assumptions. Here we describe factors influencing disease transmission, including both mechanisms of microbial infection and host behavior. We then discuss how model structure and parameterization are optimally used for addressing research and policy questions in the context of available information. The spread and control of infectious diseases are related to biological factors that impact the magnitude, timing, and route of transmission. 14 228 Transmission could result from direct contact between hosts, could require host-environment interaction, or could be mediated by a vector-often a phlebotomine insect-that transmits a pathogen between different hosts. Transmission can also vary across strata of the host population, as subgroups determined by behavioral, genetic, demographic, environmental, or other characteristics may result in differential exposure or susceptibility to specific diseases. Much of the art and science of modeling rides on critically evaluating which factors are essential to include in order to generate quantitative understanding of a focal research question. Beyond accounting for the change in number of infected individuals over time, it can be important to also incorporate the course of infection during which microbial load, symptom severity, and contact behavior may all vary. Infection progression, or "age of infection," is associated with temporal changes in infectiousness. Microbiological studies that measure the change in pathogen load over the course of an infection are integral to parameterization of dynamic models of disease, particularly evaluating interventions as the timing of intervention during the infection period could impact predicted effectiveness. The behaviors of human hosts, reservoir hosts, and vectors are fundamental to risks of both infection and transmission to others. For nonhuman hosts and vectors, the geographical distribution of habitat, feeding preferences, and proximity to humans affect the rate of transmission. For human hosts, contact patterns, belief systems, attitudes about public health recommendations, and propensity to seek care impact the real-world effectiveness of infection control measures, including quarantining, school closures, handwashing, and vaccination. To some extent, behavioral effects are implicit in the measures of transmission that are used in and estimated by models. However, in many cases it can be more appropriate to treat the transmission rate as a dynamic variable that evolves in concert with behaviors associated with the epidemic.
[EX A]:
Modeling Approaches Toward Understanding Infectious Disease Transmission