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.

Input: Consider Input: Recent studies suggest an association between the Interferon Inducible Transmembrane 3 (IFITM3) rs12252 variant and the course of influenza infection. However, it is not clear whether the reported association relates to influenza infection severity. The aim of this study was to estimate the hospitalization risk associated with this variant in Influenza Like Illness (ILI) patients during the H1N1 pandemic influenza. A case-control genetic association study was performed, using nasopharyngeal/oropharyngeal swabs collected during the H1N1 pandemic influenza. Laboratory diagnosis of influenza infection was performed by RT-PCR, the IFITM3 rs12252 was genotyped by RFLP and tested for association with hospitalization. Conditional logistic regression was performed to calculate the confounder-adjusted odds ratio of hospitalization associated with IFITM3 rs12252. We selected 312 ILI cases and 624 matched non-hospitalized controls. Within ILI Influenza A(H1N1)pdm09 positive patients, no statistical significant association was found between the variant and the hospitalization risk (Adjusted OR: 0.73 (95%CI: 0.33-1.50)). Regarding ILI Influenza A(H1N1)pdm09 negative patients, CT/CC genotype carriers had a higher risk of being hospitalized than patients with TT genotype (Adjusted OR: 2.54 (95%CI: 1.54-4.19)). The IFITM3 rs12252 variant was associated with respiratory infection hospitalization but not specifically in patients infected with Influenza A(H1N1)pdm09.

Output: Hospitalization Risk Due to Respiratory Illness Associated with Genetic Variation at IFITM3 in Patients with Influenza A(H1N1)pdm09 Infection: A Case-Control Study


Input: Consider 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.

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


Input: Consider Input: Background. The negative binomial distribution is used commonly throughout biology as a model for overdispersed count data, with attention focused on the negative binomial dispersion parameter, k. A substantial literature exists on the estimation of k, but most attention has focused on datasets that are not highly overdispersed (i.e., those with k$1), and the accuracy of confidence intervals estimated for k is typically not explored. Methodology. This article presents a simulation study exploring the bias, precision, and confidence interval coverage of maximum-likelihood estimates of k from highly overdispersed distributions. In addition to exploring small-sample bias on negative binomial estimates, the study addresses estimation from datasets influenced by two types of event under-counting, and from disease transmission data subject to selection bias for successful outbreaks. Conclusions. Results show that maximum likelihood estimates of k can be biased upward by small sample size or under-reporting of zero-class events, but are not biased downward by any of the factors considered. Confidence intervals estimated from the asymptotic sampling variance tend to exhibit coverage below the nominal level, with overestimates of k comprising the great majority of coverage errors. Estimation from outbreak datasets does not increase the bias of k estimates, but can add significant upward bias to estimates of the mean. Because k varies inversely with the degree of overdispersion, these findings show that overestimation of the degree of overdispersion is very rare for these datasets.


Output: Maximum Likelihood Estimation of the Negative Binomial Dispersion Parameter for Highly Overdispersed Data, with Applications to Infectious Diseases