6,743 research outputs found
Bayesian prediction of an epidemic curve
AbstractAn epidemic curve is a graph in which the number of new cases of an outbreak disease is plotted against time. Epidemic curves are ordinarily constructed after the disease outbreak is over. However, a good estimate of the epidemic curve early in an outbreak would be invaluable to health care officials. Currently, techniques for predicting the severity of an outbreak are very limited. As far as predicting the number of future cases, ordinarily epidemiologists simply make an educated guess as to how many people might become affected. We develop a model for estimating an epidemic curve early in an outbreak, and we show results of experiments testing its accuracy
Estimation of COVID-19 spread curves integrating global data and borrowing information
Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to
global health. The rapid spread of the virus has created pandemic, and
countries all over the world are struggling with a surge in COVID-19 infected
cases. There are no drugs or other therapeutics approved by the US Food and
Drug Administration to prevent or treat COVID-19: information on the disease is
very limited and scattered even if it exists. This motivates the use of data
integration, combining data from diverse sources and eliciting useful
information with a unified view of them. In this paper, we propose a Bayesian
hierarchical model that integrates global data for real-time prediction of
infection trajectory for multiple countries. Because the proposed model takes
advantage of borrowing information across multiple countries, it outperforms an
existing individual country-based model. As fully Bayesian way has been
adopted, the model provides a powerful predictive tool endowed with uncertainty
quantification. Additionally, a joint variable selection technique has been
integrated into the proposed modeling scheme, which aimed to identify possible
country-level risk factors for severe disease due to COVID-19
Probabilistic projections of HIV prevalence using Bayesian melding
The Joint United Nations Programme on HIV/AIDS (UNAIDS) has developed the
Estimation and Projection Package (EPP) for making national estimates and
short-term projections of HIV prevalence based on observed prevalence trends at
antenatal clinics. Assessing the uncertainty about its estimates and
projections is important for informed policy decision making, and we propose
the use of Bayesian melding for this purpose. Prevalence data and other
information about the EPP model's input parameters are used to derive a
probabilistic HIV prevalence projection, namely a probability distribution over
a set of future prevalence trajectories. We relate antenatal clinic prevalence
to population prevalence and account for variability between clinics using a
random effects model. Predictive intervals for clinic prevalence are derived
for checking the model. We discuss predictions given by the EPP model and the
results of the Bayesian melding procedure for Uganda, where prevalence peaked
at around 28% in 1990; the 95% prediction interval for 2010 ranges from 2% to
7%.Comment: Published at http://dx.doi.org/10.1214/07-AOAS111 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Predicting epidemic evolution on contact networks from partial observations
The massive employment of computational models in network epidemiology calls
for the development of improved inference methods for epidemic forecast. For
simple compartment models, such as the Susceptible-Infected-Recovered model,
Belief Propagation was proved to be a reliable and efficient method to identify
the origin of an observed epidemics. Here we show that the same method can be
applied to predict the future evolution of an epidemic outbreak from partial
observations at the early stage of the dynamics. The results obtained using
Belief Propagation are compared with Monte Carlo direct sampling in the case of
SIR model on random (regular and power-law) graphs for different observation
methods and on an example of real-world contact network. Belief Propagation
gives in general a better prediction that direct sampling, although the quality
of the prediction depends on the quantity under study (e.g. marginals of
individual states, epidemic size, extinction-time distribution) and on the
actual number of observed nodes that are infected before the observation time
Bayesian data assimilation provides rapid decision support for vector-borne diseases
Predicting the spread of vector-borne diseases in response to incursions
requires knowledge of both host and vector demographics in advance of an
outbreak. Whereas host population data is typically available, for novel
disease introductions there is a high chance of the pathogen utilising a vector
for which data is unavailable. This presents a barrier to estimating the
parameters of dynamical models representing host-vector-pathogen interaction,
and hence limits their ability to provide quantitative risk forecasts. The
Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this
problem: even though the vector has received extensive laboratory study, a high
degree of uncertainty persists over its national demographic distribution.
Addressing this, we develop a Bayesian data assimilation approach whereby
indirect observations of vector activity inform a seasonal spatio-temporal risk
surface within a stochastic epidemic model. We provide quantitative predictions
for the future spread of the epidemic, quantifying uncertainty in the model
parameters, case infection times, and the disease status of undetected
infections. Importantly, we demonstrate how our model learns sequentially as
the epidemic unfolds, and provides evidence for changing epidemic dynamics
through time. Our approach therefore provides a significant advance in rapid
decision support for novel vector-borne disease outbreaks
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