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]: After decades of low personal risk for contracting lethal diseases, physicians are suddenly facing the possibility of a substantial increase in occupational risk during an influenza pandemic. If they are not confronted before the onset of an influenza pandemic, feelings of unease and fear or ignorance about physicians' professional obligations could profoundly hinder individual physicians in fulfilling their professional duties. Such feelings could therefore undermine institutional and societal preparations. In their review published in Critical Care, Anantham and coworkers outline the ethical framework that forms the basis of the professional obligations of physicians who respond to health care emergencies, such as an influenza pandemic.
[EX A]: Pandemic influenza: are we prepared to face our obligations?

[EX Q]: Objectives: We aimed to develop a prospective prediction tool on CrimeaneCongo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner. Methods: We used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases. Results: We predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration). Conclusions: Main driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases. Ç. Ak, Clin Microbiol Infect 2020;26:123.e1e123.e7
[EX A]: A prospective prediction tool for understanding CrimeaneCongo haemorrhagic fever dynamics in Turkey

[EX Q]: Data collected during outbreaks are essential to better understand infectious disease transmission and design effective control strategies. But analysis of such data is challenging owing to the dependency between observations that is typically observed in an outbreak and to missing data. In this paper, we discuss strategies to tackle some of the ongoing challenges in the analysis of outbreak data. We present a relatively generic statistical model for the estimation of transmission risk factors, and discuss algorithms to estimate its parameters for different levels of missing data. We look at the problem of computational times for relatively large datasets and show how they can be reduced by appropriate use of discretization, sufficient statistics and some simple assumptions on the natural history of the disease. We also discuss approaches to integrate parametric model fitting and tree reconstruction methods in coherent statistical analyses. The methods are tested on both real and simulated datasets of large outbreaks in structured populations.
[EX A]:
Methods to infer transmission risk factors in complex outbreak data