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.

[Q]: lord mayor and aldermen of the city of London put into place a set of orders "concerning the infection of the plague," which was then sweeping through the population. He intended that these actions would be "very expedient for preventing and avoiding of infection of sickness" (1). At that time, London faced a public health crisis, with an inadequate scientific base in that the role of rats and their fleas in disease transmission was unknown. Nonetheless, this crisis was faced with good intentions by the top medical and political figures of the community. Daniel Defoe made an observation that could apply to many public health interventions then and today, "This shutting up of houses was at first counted a very cruel and unchristian method… but it was a public good that justified a private mischief" (1). Then, just as today, a complex relationship existed between the science of public health and the practice of public health and politics. We address the relationship between science, public health, and politics, with a particular emphasis on infectious diseases. Science, public health, and politics are not only compatible, but all three are necessary to improve the public's health. The progress of each area of public health is related to the strength of the other areas. The effect of politics in public health becomes dangerous when policy is dictated by ideology. Policy is also threatened when it is solely determined by science, devoid of considerations of social condition, culture, economics, and public will. When using the word "politics," we refer not simply to partisan politics but to the broader set of policies and sys-tems. Although ideology is used in many different ways, in this case, it refers to individual systems of belief that may color a person's attitudes and actions and that are not necessarily based on scientific evidence (2).
[A]: Plagues, Public Health, and Politics 1


[Q]: Background: Prevention of infection with airborne pathogens and exposure to airborne particulates and aerosols (environmental pollutants and allergens) can be facilitated through use of disposable face masks. The effectiveness of such masks for excluding pathogens and pollutants is dependent on the intrinsic ability of the masks to resist penetration by airborne contaminants. This study evaluated the relative contributions of a mask, valve, and Micro Ventilator on aerosol filtration efficiency of a new N95 respiratory face mask. Methods: The test mask was challenged, using standardized methods, with influenza A and rhinovirus type 14, bacteriophage ΦΧ174, Staphylococcus aureus (S. aureus), and model pollutants. The statistical significance of results obtained for different challenge microbial agents and for different mask configurations (masks with operational or nonoperational ventilation fans and masks with sealed Smart Valves) was assessed. Results: The results demonstrate >99.7% efficiency of each test mask configuration for exclusion of influenza A virus, rhinovirus 14, and S. aureus and >99.3% efficiency for paraffin oil and sodium chloride (surrogates for PM 2.5 ). Statistically significant differences in effectiveness of the different mask configurations were not identified. The efficiencies of the masks for excluding smaller-size (i.e., rhinovirus and bacteriophage ΦΧ174) vs. larger-size microbial agents (influenza virus, S. aureus) were not significantly different. Conclusions: The masks, with or without features intended for enhancing comfort, provide protection against both small-and large-size pathogens. Importantly, the mask appears to be highly efficient for filtration of pathogens, including influenza and rhinoviruses, as well as the fine particulates (PM 2.5 ) present in aerosols that represent a greater challenge for many types of dental and surgical masks. This renders this individual-use N95 respiratory mask an improvement over the former types of masks for protection against a variety of environmental contaminants including PM 2.5 and pathogens such as influenza and rhinoviruses.
[A]: Assessment of a respiratory face mask for capturing air pollutants and pathogens including human influenza and rhinoviruses


[Q]: Data used for modelling the household transmission of infectious diseases, such as influenza, have inherent multilevel structures and correlated property, which make the widely used conventional infectious disease transmission models (including the Greenwood model and the Reed-Frost model) not directly applicable within the context of a household (due to the crowded domestic condition or socioeconomic status of the household). Thus, at the household level, the effects resulting from individual-level factors, such as vaccination, may be confounded or modified in some way. We proposed the Bayesian hierarchical randomeffects (random intercepts and random slopes) model under the context of generalised linear model to capture heterogeneity and variation on the individual, generation, and household levels. It was applied to empirical surveillance data on the influenza epidemic in Taiwan. The parameters of interest were estimated by using the Markov chain Monte Carlo method in conjunction with the Bayesian directed acyclic graphical models. Comparisons between models were made using the deviance information criterion. Based on the result of the random-slope Bayesian hierarchical method under the context of the Reed-Frost transmission model, the regression coefficient regarding the protective effect of vaccination varied statistically significantly from household to household. The result of such a heterogeneity was robust to the use of different prior distributions (including non-informative, sceptical, and enthusiastic ones). By integrating out the uncertainty of the parameters of the posterior distribution, the predictive distribution was computed to forecast the number of influenza cases allowing for random-household effect.
[A]:
Analysis of household data on influenza epidemic with Bayesian hierarchical model