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.

Example Input: Acute respiratory tract infections (ARI) are a major burden in pediatric long-term care. We analyzed the financial impact of ARI in 2012-2013. Costs associated with ARI during the respiratory viral season were ten times greater than during the non-respiratory viral season, $31 224 and $3242 per 1000 patient-days, respectively (P < 0Á001). ARI are burdensome for pediatric long-term care facilities not only because of the associated morbidity and mortality, but also due to the great financial costs of prevention. Please cite this paper as: Murray et al. (2016) Direct costs of acute respiratory infections in a pediatric long-term care facility. Influenza and Other Respiratory Viruses 10(1), 34-36.
Example Output: Direct costs of acute respiratory infections in a pediatric long-term care facility

Example Input: 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.
Example Output: Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Example Input: The emergence of highly pathogenic human coronaviruses (hCoVs) in the last two decades has illuminated their potential to cause high morbidity and mortality in human populations and disrupt global economies. Global pandemic concerns stem from their high mortality rates, capacity for human-to-human spread by respiratory transmission, and complete lack of approved therapeutic countermeasures. Limiting disease may require the development of virus-directed and host-directed therapeutic strategies due to the acute etiology of hCoV infections. Therefore, understanding how hCoV-host interactions cause pathogenic outcomes relies upon mammalian models that closely recapitulate the pathogenesis of hCoVs in humans. Pragmatism has largely been the driving force underpinning mice as highly effective mammalian models for elucidating hCoV-host interactions that govern pathogenesis. Notably, tractable mouse genetics combined with hCoV reverse genetic systems has afforded the concomitant manipulation of virus and host genetics to evaluate virus-host interaction networks in disease. In addition to assessing etiologies of known hCoVs, mouse models have clinically predictive value as tools to appraise potential disease phenotypes associated with pre-emergent CoVs. Knowledge of CoV pathogenic potential before it crosses the species barrier into the human population provides a highly desirable preclinical platform for addressing global pathogen preparedness, an overarching directive of the World Health Organization. Although we recognize that results obtained in robust mouse models require evaluation in non-human primates, we focus this review on the current state of hCoV mouse models, their use as tractable complex genetic organisms for untangling complex hCoV-host interactions, and as pathogenesis models for preclinical evaluation of novel therapeutic interventions.
Example Output:
Modeling pathogenesis of emergent and pre-emergent human coronaviruses in mice