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]: Background: Infection control professionals (ICP) who have experienced disaster response have not been assessed in terms of the lessons they have learned, gaps they perceive in disaster preparedness, and their perceived priorities for future emergency response training. Methods: Focus groups were conducted at the APIC 2006 Conference to evaluate ICPs' perceived needs related to disaster planning topics, products they feel are needed for education and reference materials, and lessons learned from past disasters. Results: ICPs' role in disaster preparedness and response is essential, even in noninfectious disease emergencies. Infection control issues in shelters, such as overcrowding, foodborne illness, lack of restroom facilities, inadequate environmental cleaning procedures and products, difficulty assessing disease outbreaks in shelters, inability to isolate potentially contagious patients, and too few hand hygiene supplies can contribute to secondary disease transmission. Other important topics on which ICPs need to be trained include surge capacity, employee health and safety, incident command system, educating responders and the public on disaster preparedness, addressing changing standards/recommendations, and partnering with public health. ICPs need quick reference materials, such as checklists, templates, tool kits, and algorithms to better equip them for disaster response. Conclusion: Infection control must continue to partner with public health and other responding agencies to address gaps in disaster planning. (Am J Infect Control 2007;35:374-81.) 
[EX A]: Disaster preparedness lessons learned and future directions for education: Results from focus groups conducted at the 2006 APIC Conference

[EX Q]: Objective: Health literacy has become a global issue, and it is important that patients and individuals are able to use information technology to access health information and educational services. The research objective is to develop a Saudi e-health literacy scale (SeHL) for measuring e-health literacy among Saudis suffering from non-communicable diseases (NCD). Methods: Overall, 14 relevant papers in related interdisciplinary fields were reviewed to select the most useful literacy dimensions. From these articles, we extracted the most common dimensions used to measure e-health literacy across the disciplines. Multiple workshops with multidisciplinary team members reviewed and evaluated items for SeHL. Results: Four key aspects of e-health literacy-use of technology/media, information-seeking, usefulness and confidence-were identified and integrated as e-health literacy dimensions. These will be used to measure e-health literacy among Saudi patients with NCDs. A translation from Arabic to English was performed in order to ensure that translation process was accurate. A SeHL scale was developed to measure e-health literacy among Saudi patients. By understanding e-health literacy levels, we will be able to create a patient-education system to be used by patients in Saudi Arabia. Conclusions: As information technology is increasingly used by people of all ages all over the world, e-health literacy has been identified as a key factor in determining health outcomes. To date, no comprehensive scale exists to assess e-health literacy levels among speakers of Arabic, particularly among people with NCD such as diabetes, cardiovascular diseases and hypertension.
[EX A]: Saudi Arabia, 5 Prince Sultan Military Medical City, AlSulimaniah, Riyadh 11159, Saudi Arabia, 6 King Faisal Specialist and Research Center

[EX Q]: 1. Predicting infectious disease dynamics is a central challenge in disease ecology. Models 36 that can assess which individuals are most at risk of being exposed to a pathogen not only 37 provide valuable insights into disease transmission and dynamics but can also guide 38 management interventions. Constructing such models for wild animal populations, 39 however, is particularly challenging; often only serological data is available on a subset 40 of individuals and non-linear relationships between variables are common. 41 42 2. Here we take advantage of the latest advances in statistical machine learning to construct 43 pathogen-risk models that automatically incorporate complex non-linear relationships 44 with minimal statistical assumptions from ecological data with missing values. Our 45 approach compares multiple machine learning algorithms in a unified environment to 46 find the model with the best predictive performance and uses game theory to better 47 interpret results. We apply this framework on two major pathogens that infect African 48 lions: canine distemper virus (CDV) and feline parvovirus. 49 50 3. Our modelling approach provided enhanced predictive performance compared to more 51 traditional approaches, as well as new insights into disease risks in a wild population. We 52 were able to efficiently capture and visualise strong non-linear patterns, as well as model 53 complex interactions between variables in shaping exposure risk from CDV and feline 54 parvovirus. For example, we found that lions were more likely to be exposed to CDV at a 55 young age but only in low rainfall years. 56 57 . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/569012 doi: bioRxiv preprint 3 4. When combined with our data calibration approach, our framework helped us to answer 58 questions about risk of pathogen exposure which are difficult to address with previous 59 methods. Our framework not only has the potential to aid in predicting disease risk in 60 animal populations, but also can be used to build robust predictive models suitable for 61 other ecological applications such as modelling species distribution or diversity patterns.
[EX A]:
How to make more from exposure data? An integrated machine Author Contributions Data Accessibility Statement Running Title Machine learning and pathogen exposure risk