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: We summarize herein the results of various virologic studies of acute otitis media (AOM) conducted at our site over a ten -year period. Among 566 children with AOM, respiratory syncytial virus (RSV) was the most common virus identified in either middle ear fluid or nasal wash; it was found in 16% of all children and 38% of virus-positive children. Seventy-one percent of the children with RSV were one year of age or older, which was significantly older than all other viruses combined (P = 0.045). RSV infection was associated with the common bacterial pathogens causing AOM. Past efforts to develop vaccines for RSV have emphasized prevention of lower respiratory tract infection in infants, which is a more serious problem but less common than AOM. Our results suggest that RSV vaccines that work only against infection in older children may have value in preventing AOM, the most common pediatric disease.
Example Output: ROLE OF RESPIRATORY SYNCYTIAL VIRUS IN ACUTE OTITIS MEDIA: IMPLICATIONS FOR VACCINE DEVELOPMENT

Example Input: Contact networks are convenient models to investigate epidemics, with nodes and links representing potential hosts and infection pathways, respectively. The outcomes of outbreak simulations on networks are driven both by the underlying epidemic model, and by the networks' structural properties, so that the same pathogen can generate different epidemic dynamics on different networks. Here we ask whether there are general properties that make a contact network intrinsically vulnerable to epidemics (that is, regardless of specific epidemiological parameters). By conducting simulations on a large set of modelled networks, we show that, when a broad range of network topologies is taken into account, the effect of specific network properties on outbreak magnitude is stronger than that of fundamental pathogen features such as transmission rate, infection duration, and immunization ability. Then, by focusing on a large set of real world networks of the same type (potential contacts between field voles, Microtus agrestis), we showed how network structure can be used to accurately assess the relative, intrinsic vulnerability of networks towards a specific pathogen, even when those have limited topological variability. These results have profound implications for how we prevent disease outbreaks; in many real world situations, the topology of host contact networks can be described and used to infer intrinsic vulnerability. Such an approach can increase preparedness and inform preventive measures against emerging diseases for which limited epidemiological information is available, enabling the identification of priority targets before an epidemic event.
Example Output: The intrinsic vulnerability of networks to epidemics

Example Input: Infectious disease has witnessed the emergence of mathematical modeling a tool of synthesizing data of growing complexity now available to clinicians and basic scientists alike. The purpose of this review is to introduce mathematical tools commonly used to model infectious disease. We will illustrate the use of equation-based, agent-based or statistical modeling approaches to a variety of examples pertaining to acute inflammation, bacterial dynamics, viral dynamics, and signaling pathways, focusing of host-pathogen interactions rather than population models. We will discuss the strengths and weaknesses of these approaches and offer future perspectives for this rapidly evolving field.
Example Output:
DISEASE In silico modeling in infectious disease