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: Whether information sources influence health protective behaviours during influenza pandemics or other emerging infectious disease epidemics is uncertain. Methodology: Data from cross-sectional telephone interviews of 1,001 Hong Kong adults in June, 2009 were tested against theory and data-derived hypothesized associations between trust in (formal/informal) information, understanding, selfefficacy, perceived susceptibility and worry, and hand hygiene and social distancing using Structural Equation Modelling with multigroup comparisons. Principal Findings: Trust in formal (government/media) information about influenza was associated with greater reported understanding of A/H1N1 cause (b = 0.36) and A/H1N1 prevention self-efficacy (b = 0.25), which in turn were associated with more hand hygiene (b = 0.19 and b = 0.23, respectively). Trust in informal (interpersonal) information was negatively associated with perceived personal A/H1N1 susceptibility (b = 20.21), which was negatively associated with perceived selfefficacy (b = 20.42) but positively associated with influenza worry (b = 0.44). Trust in informal information was positively associated with influenza worry (b = 0.16) which was in turn associated with greater social distancing (b = 0.36). Multigroup comparisons showed gender differences regarding paths from trust in formal information to understanding of A/H1N1 cause, trust in informal information to understanding of A/H1N1 cause, and understanding of A/H1N1 cause to perceived self-efficacy. Conclusions/Significance: Trust in government/media information was more strongly associated with greater self-efficacy and handwashing, whereas trust in informal information was strongly associated with perceived health threat and avoidance behaviour. Risk communication should consider the effect of gender differences.
[EX A]: Situational Awareness and Health Protective Responses to Pandemic Influenza A (H1N1) in Hong Kong: A Cross- Sectional Study

[EX Q]: Foot-and-mouth disease GIS OpenFOAM Livestock infectious diseases, such as foot-and-mouth disease (FMD), cause substantial economic damage to livestock farms and their related industries. Among various causes of disease spread, airborne dispersion has previously been considered to be an important factor that could not be controlled by preventive measures to stop the spread of disease that focus on direct and indirect contact. Forecasting and predicting airborne virus spread are important to make time for developing strategies and to minimise the damage of the disease. To predict the airborne spread of the disease a modelling approach is important since field experiments using sensors are ineffective because of the rarefied concentrations of virus in the air. The simulation of airborne spread during past outbreaks required improvement both for farmers and for policy decision makers. In this study a free license computational fluid dynamics (CFD) code was used to simulate airborne virus spread. Forecasting data from the Korea Meteorological Administration (KMA) was directly connected in the developed model for real-time forecasting for 48 h in three-hourly intervals. To reduce computation time, scalar transport for airborne virus spread was simulated based on a database for the CFD computed airflow in the investigated area using representative wind conditions. The simulation results, and the weather data were then used to make a database for a web-based forecasting system that could be accessible to users. ScienceDirect jo urnal homepage: www .e lsev ie r.com/ locate/issn/153 75110 b i o s y s t e m s e n g i n e e r i n g 1 2 9 ( 2 0 1 5 ) 1 6 9 e1 8 4 http://dx.
[EX A]: Web-based forecasting system for the airborne spread of livestock infectious disease using computational fluid dynamics

[EX Q]: Background: The Middle East respiratory syndrome (MERS) coronavirus has caused recurrent outbreaks in the Arabian Peninsula since 2012. Although MERS has low overall human-to-human transmission potential, there is occasional amplification in the healthcare setting, a pattern reminiscent of the dynamics of the severe acute respiratory syndrome (SARS) outbreaks in 2003. Here we provide a head-to-head comparison of exposure patterns and transmission dynamics of large hospital clusters of MERS and SARS, including the most recent South Korean outbreak of MERS in 2015. Methods: To assess the unexpected nature of the recent South Korean nosocomial outbreak of MERS and estimate the probability of future large hospital clusters, we compared exposure and transmission patterns for previously reported hospital clusters of MERS and SARS, based on individual-level data and transmission tree information. We carried out simulations of nosocomial outbreaks of MERS and SARS using branching process models rooted in transmission tree data, and inferred the probability and characteristics of large outbreaks. Results: A significant fraction of MERS cases were linked to the healthcare setting, ranging from 43.5 % for the nosocomial outbreak in Jeddah, Saudi Arabia, in 2014 to 100 % for both the outbreak in Al-Hasa, Saudi Arabia, in 2013 and the outbreak in South Korea in 2015. Both MERS and SARS nosocomial outbreaks are characterized by early nosocomial super-spreading events, with the reproduction number dropping below 1 within three to five disease generations. There was a systematic difference in the exposure patterns of MERS and SARS: a majority of MERS cases occurred among patients who sought care in the same facilities as the index case, whereas there was a greater concentration of SARS cases among healthcare workers throughout the outbreak. Exposure patterns differed slightly by disease generation, however, especially for SARS. Moreover, the distributions of secondary cases per single primary case varied highly across individual hospital outbreaks (Kruskal-Wallis test; P < 0.0001), with significantly higher transmission heterogeneity in the distribution of secondary cases for MERS than SARS. Simulations indicate a 2-fold higher probability of occurrence of large outbreaks (>100 cases) for SARS than MERS (2 % versus 1 %); however, owing to higher transmission heterogeneity, the largest outbreaks of MERS are characterized by sharper incidence peaks. The probability of occurrence of MERS outbreaks larger than the South Korean cluster (n = 186) is of the order of 1 %. Our study suggests that the South Korean outbreak followed a similar progression to previously described hospital clusters involving coronaviruses, with early super-spreading events generating a disproportionately large number of secondary infections, and the transmission potential diminishing greatly in subsequent generations. Differences in relative exposure patterns and transmission heterogeneity of MERS and SARS could point to changes in hospital practices since 2003 or differences in transmission mechanisms of these coronaviruses.
[EX A]:
Transmission characteristics of MERS and SARS in the healthcare setting: a comparative study