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 Input:
Background: With increasing international travel and historically high numbers of residents visiting friends and relatives overseas, travel-associated illnesses are frequent in Marseille, France. We report the changing epidemiology of travel-related illnesses over a 12-year period. Methods: A single site GeoSentinel surveillance analysis was undertaken for 3460 ill returned travellers presenting to two public hospitals in Marseille, France from March 2003 to October 2015, with travel-related illnesses. Demographic characteristics, travel history, presenting symptoms and information on pre-travel consultations were collected. Results: There was a predominance of travel to sub-Saharan Africa, in particular to Comoros archipelago. Tourism was the main reason for travel (1591/3460, 46%), followed by visiting friends or relatives (VFR) (895/3460, 26%), with a mean duration of 29 days; 35% (1212/3460) of travellers reported a pre-travel health consultation. The most common syndromic diagnoses were febrile systemic illness (1343, 39%), dermatologic (716, 21%), gastrointestinal (340, 10%) and respiratory/ear-nose-throat (331, ENT) (10%). Hospitalization rates were highest amongst travellers from sub-Saharan Africa (858/ 1632, 53%), and VFR (573/ 895, 64%, P < 0.001). Frequent diagnoses included malaria (797, 23%), dengue (96, 2.77%) and chikungunya (75, 2.17%), reflecting global trends. Comparison of two periods (2003-10 to 2011-15) demonstrated an increase in chikungunya and decrease in malaria and influenza-like illness. We report an increase in ill travellers from the Caribbean, Middle East and South-East Asia. Conclusion: Surveillance of travellers provides relevant sentinel information on the changing epidemiology of infectious diseases across the globe, most notably for malaria, dengue and chikungunya. We demonstrate the use of travel surveillance in improving pre-travel consultation needs and to address autochthonous vector-borne viral risks.

Ex Output:
Surveillance of travel-associated diseases at two referral centres in Marseille, France: a 12-year survey


Ex Input:
Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online.

Ex Output:
Bayesian mixture analysis for metagenomic community profiling


Ex Input:
The semi-Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end-point of the support of the censoring time is strictly less than the right end-point of the support of the semi-Markov kernel, the transition probability of the semi-Markov process is nonidentifiable, and the estimators proposed in the literature are inconsistent in general. We derive the set of all attainable values for the transition probability based on the censored data, and we propose a nonparametric inference procedure for the transition probability using this set. Second, the conventional approach to constructing confidence bands is not applicable for the semi-Markov kernel and the sojourn time distribution. We propose new perturbation resampling methods to construct these confidence bands. Different weights and transformations are explored in the construction. We use simulation to examine our proposals and illustrate them with hospitalization data from a recent cancer survivor study. Multi-state stochastic processes provide a convenient framework for the analysis of event history data (Andersen et al., 1993; Commenges, 1999; Andersen and Keiding, 2002) . The semi-Markov model is a generalization of the classical homogeneous Markov model. It is preferred in many practical applications because it accommodates the dependence of transitions between states on the state durations (Andersen, Esbjerg, and Sorensen, 2000; Kang and Lagakos, 2007) . Suppose {S(t):t ≥ 0} is a finite-state stochastic process with state space ℰ = {1,…, r}. Denote its sequence of consecutive states by {J m :m = 0, 1, 2,…}, and let the corresponding transition times be {T m :m = 0, 1,…}. Without loss of generality, we assume T 0 = 0. Let X m = T m − T m−1 be the sojourn time of the mth transition. Then {S(t):t ≥ 0} is a homogeneous

Ex Output:

Estimation with Right-Censored Observations Under A Semi- Markov Model