2,289 research outputs found
A resampling-based test to detect person-to-person transmission of infectious disease
Early detection of person-to-person transmission of emerging infectious
diseases such as avian influenza is crucial for containing pandemics. We
developed a simple permutation test and its refined version for this purpose. A
simulation study shows that the refined permutation test is as powerful as or
outcompetes the conventional test built on asymptotic theory, especially when
the sample size is small. In addition, our resampling methods can be applied to
a broad range of problems where an asymptotic test is not available or fails.
We also found that decent statistical power could be attained with just a small
number of cases, if the disease is moderately transmissible between humans.Comment: Published at http://dx.doi.org/10.1214/07-AOAS105 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees
Recent work has attempted to use whole-genome sequence data from pathogens to
reconstruct the transmission trees linking infectors and infectees in
outbreaks. However, transmission trees from one outbreak do not generalize to
future outbreaks. Reconstruction of transmission trees is most useful to public
health if it leads to generalizable scientific insights about disease
transmission. In a survival analysis framework, estimation of transmission
parameters is based on sums or averages over the possible transmission trees. A
phylogeny can increase the precision of these estimates by providing partial
information about who infected whom. The leaves of the phylogeny represent
sampled pathogens, which have known hosts. The interior nodes represent common
ancestors of sampled pathogens, which have unknown hosts. Starting from
assumptions about disease biology and epidemiologic study design, we prove that
there is a one-to-one correspondence between the possible assignments of
interior node hosts and the transmission trees simultaneously consistent with
the phylogeny and the epidemiologic data on person, place, and time. We develop
algorithms to enumerate these transmission trees and show these can be used to
calculate likelihoods that incorporate both epidemiologic data and a phylogeny.
A simulation study confirms that this leads to more efficient estimates of
hazard ratios for infectiousness and baseline hazards of infectious contact,
and we use these methods to analyze data from a foot-and-mouth disease virus
outbreak in the United Kingdom in 2001. These results demonstrate the
importance of data on individuals who escape infection, which is often
overlooked. The combination of survival analysis and algorithms linking
phylogenies to transmission trees is a rigorous but flexible statistical
foundation for molecular infectious disease epidemiology.Comment: 28 pages, 11 figures, 3 table
Estimating within-household contact networks from egocentric data
Acute respiratory diseases are transmitted over networks of social contacts.
Large-scale simulation models are used to predict epidemic dynamics and
evaluate the impact of various interventions, but the contact behavior in these
models is based on simplistic and strong assumptions which are not informed by
survey data. These assumptions are also used for estimating transmission
measures such as the basic reproductive number and secondary attack rates.
Development of methodology to infer contact networks from survey data could
improve these models and estimation methods. We contribute to this area by
developing a model of within-household social contacts and using it to analyze
the Belgian POLYMOD data set, which contains detailed diaries of social
contacts in a 24-hour period. We model dependency in contact behavior through a
latent variable indicating which household members are at home. We estimate
age-specific probabilities of being at home and age-specific probabilities of
contact conditional on two members being at home. Our results differ from the
standard random mixing assumption. In addition, we find that the probability
that all members contact each other on a given day is fairly low: 0.49 for
households with two 0--5 year olds and two 19--35 year olds, and 0.36 for
households with two 12--18 year olds and two 36+ year olds. We find higher
contact rates in households with 2--3 members, helping explain the higher
influenza secondary attack rates found in households of this size.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS474 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Estimating within-school contact networks to understand influenza transmission
Many epidemic models approximate social contact behavior by assuming random
mixing within mixing groups (e.g., homes, schools and workplaces). The effect
of more realistic social network structure on estimates of epidemic parameters
is an open area of exploration. We develop a detailed statistical model to
estimate the social contact network within a high school using friendship
network data and a survey of contact behavior. Our contact network model
includes classroom structure, longer durations of contacts to friends than
nonfriends and more frequent contacts with friends, based on reports in the
contact survey. We performed simulation studies to explore which network
structures are relevant to influenza transmission. These studies yield two key
findings. First, we found that the friendship network structure important to
the transmission process can be adequately represented by a dyad-independent
exponential random graph model (ERGM). This means that individual-level sampled
data is sufficient to characterize the entire friendship network. Second, we
found that contact behavior was adequately represented by a static rather than
dynamic contact network.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS505 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Predictive Modeling of Cholera Outbreaks in Bangladesh
Despite seasonal cholera outbreaks in Bangladesh, little is known about the
relationship between environmental conditions and cholera cases. We seek to
develop a predictive model for cholera outbreaks in Bangladesh based on
environmental predictors. To do this, we estimate the contribution of
environmental variables, such as water depth and water temperature, to cholera
outbreaks in the context of a disease transmission model. We implement a method
which simultaneously accounts for disease dynamics and environmental variables
in a Susceptible-Infected-Recovered-Susceptible (SIRS) model. The entire system
is treated as a continuous-time hidden Markov model, where the hidden Markov
states are the numbers of people who are susceptible, infected, or recovered at
each time point, and the observed states are the numbers of cholera cases
reported. We use a Bayesian framework to fit this hidden SIRS model,
implementing particle Markov chain Monte Carlo methods to sample from the
posterior distribution of the environmental and transmission parameters given
the observed data. We test this method using both simulation and data from
Mathbaria, Bangladesh. Parameter estimates are used to make short-term
predictions that capture the formation and decline of epidemic peaks. We
demonstrate that our model can successfully predict an increase in the number
of infected individuals in the population weeks before the observed number of
cholera cases increases, which could allow for early notification of an
epidemic and timely allocation of resources.Comment: 43 pages, including appendices, 5 figures, 1 table in the main tex
Embedded motivational interviewing combined with a smartphone app to increase physical activity in people with sub-acute low back pain: study protocol of a cluster randomised control trial
Background: Motivational Interviewing is an evidence-based, client-centred counselling technique that has been used effectively to increase physical activity, including for people with low back pain. One barrier to implementing Motivational Interviewing in health care settings more broadly is the extra treatment time with therapists. The aim of this paper is to describe the design of a cluster randomised controlled trial evaluating the effect of an intervention that pairs Motivational Interviewing embedded into usual physiotherapy care with a specifically designed app to increase physical activity in people with sub-acute low back pain. Methods: The study is a cluster randomised controlled in which patients aged over 18 years who have sub-acute low back pain (3–12 weeks duration) are recruited from four public hospital outpatient clinics. Based on the recruitment site, participants either receive usual physiotherapy care or the Motivational Interviewing intervention over 6 consecutive weekly outpatient sessions with a specifically designed app designed to facilitate participant-led physical activity behaviour change in between sessions. Outcome measures assessed at baseline and 7 weeks are: physical activity as measured by accelerometer (primary outcome), and pain-related activity restriction and pain self-efficacy (secondary outcomes). Postintervention interviews with physiotherapists and participants will be conducted as part of a process evaluation. Discussion: This intervention, which comprises trained physiotherapists conducting conversations about increasing physical activity with their patients in a manner consistent with Motivational Interviewing as part of usual care combined with a specifically designed app, has potential to facilitate behaviour change with minimal extra therapist time
Final Report: The Markets and Marketing Issues of the Kona Coffee Industry
This publication looks some of the problems with markets and marketing of Kona coffee and provides some recommendations for improvement
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