Disease surveillance using a hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.</p> <p>Methods</p> <p>A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.</p> <p>Results</p> <p>Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.</p> <p>Conclusion</p> <p>Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.</p

The Lactobacillus flora in vagina and rectum of fertile and postmenopausal healthy Swedish women

<p>Abstract</p> <p>Background</p> <p><it>Lactobacillus </it>species are the most often found inhabitants of vaginal ecosystem of fertile women. In postmenopausal women with low oestrogen levels, <it>Lactobacillus </it>flora is diminishing or absent. However, no studies have been performed to investigate the correlation between oestrogen levels and the lactobacilli in the gut. The aim of the present study was to investigate the relation in healthy women between vaginal and rectal microbial flora as well as possible variations with hormone levels.</p> <p>Methods</p> <p>Vaginal and rectal smears were taken from 20 healthy fertile women, average 40 years (range 28-49 years), in two different phases of the menstrual cycle, and from 20 postmenopausal women, average 60 years (range 52-85 years). Serum sex hormone levels were analyzed. Bacteria from the smears isolated on Rogosa Agar were grouped by Randomly Amplified Polymorphic DNA and identified by multiplex PCR and partial 16S rRNA gene sequencing.</p> <p>Results</p> <p><it>Lactobacillus crispatus </it>was more often found in the vaginal flora of fertile women than in that of postmenopausal (p = 0.036). Fifteen of 20 fertile women had lactobacilli in their rectal smears compared to 10 postmenopausal women (p = 0.071). There was no correlation between the number of bacteria in vagina and rectum, or between the number of bacteria and hormonal levels. Neither could any association between the presence of rectal lactobacilli and hormonal levels be found.</p> <p>Conclusion</p> <p><it>Lactobacillus crispatus </it>was more prevalent in the vaginal flora of fertile women, whereas the <it>Lactobacillus </it>flora of rectum did not correlate to the vaginal flora nor to hormonal levels.</p

Measurement of the tt¯ production cross section, the top quark mass, and the strong coupling constant using dilepton events in pp collisions at √s = 13 TeV

A measurement of the top quark–antiquark pair production cross section σtt¯ in proton–proton collisions at a centre-of-mass energy of 13TeV is presented. The data correspond to an integrated luminosity of 35.9fb−1, recorded by the CMS experiment at the CERN LHC in 2016. Dilepton events (e ± μ ∓, μ+μ−, e+e−) are selected and the cross section is measured from a likelihood fit. For a top quark mass parameter in the simulation of mMCt=172.5GeV the fit yields a measured cross section σtt¯=803±2(stat)±25(syst)±20(lumi)pb, in agreement with the expectation from the standard model calculation at next-to-next-to-leading order. A simultaneous fit of the cross section and the top quark mass parameter in the POWHEG simulation is performed. The measured value of mMCt=172.33±0.14(stat)+0.66−0.72(syst)GeV is in good agreement with previous measurements. The resulting cross section is used, together with the theoretical prediction, to determine the top quark mass and to extract a value of the strong coupling constant with different sets of parton distribution functions