90 research outputs found
On the predictability of infectious disease outbreaks
Infectious disease outbreaks recapitulate biology: they emerge from the
multi-level interaction of hosts, pathogens, and their shared environment. As a
result, predicting when, where, and how far diseases will spread requires a
complex systems approach to modeling. Recent studies have demonstrated that
predicting different components of outbreaks--e.g., the expected number of
cases, pace and tempo of cases needing treatment, demand for prophylactic
equipment, importation probability etc.--is feasible. Therefore, advancing both
the science and practice of disease forecasting now requires testing for the
presence of fundamental limits to outbreak prediction. To investigate the
question of outbreak prediction, we study the information theoretic limits to
forecasting across a broad set of infectious diseases using permutation entropy
as a model independent measure of predictability. Studying the predictability
of a diverse collection of historical outbreaks--including, chlamydia, dengue,
gonorrhea, hepatitis A, influenza, measles, mumps, polio, and whooping
cough--we identify a fundamental entropy barrier for infectious disease time
series forecasting. However, we find that for most diseases this barrier to
prediction is often well beyond the time scale of single outbreaks. We also
find that the forecast horizon varies by disease and demonstrate that both
shifting model structures and social network heterogeneity are the most likely
mechanisms for the observed differences across contagions. Our results
highlight the importance of moving beyond time series forecasting, by embracing
dynamic modeling approaches, and suggest challenges for performing model
selection across long time series. We further anticipate that our findings will
contribute to the rapidly growing field of epidemiological forecasting and may
relate more broadly to the predictability of complex adaptive systems
Epidemiological consequences of an ineffective Bordetella pertussis vaccine
The recent increase in Bordetella pertussis incidence (whooping cough)
presents a challenge to global health. Recent studies have called into question
the effectiveness of acellular B. pertussis vaccination in reducing
transmission. Here we examine the epidemiological consequences of an
ineffective B. pertussis vaccine. Using a dynamic transmission model, we find
that: 1) an ineffective vaccine can account for the observed increase in B.
pertussis incidence; 2) asymptomatic infections can bias surveillance and upset
situational awareness of B. pertussis; and 3) vaccinating individuals in close
contact with infants too young to receive vaccine (so called "cocooning"
unvaccinated children) may be ineffective. Our results have important
implications for B. pertussis vaccination policy and paint a complicated
picture for achieving herd immunity and possible B. pertussis eradication.Comment: 7 pages, 3 figures, with supplemen
A message-passing approach for recurrent-state epidemic models on networks
Epidemic processes are common out-of-equilibrium phenomena of broad
interdisciplinary interest. Recently, dynamic message-passing (DMP) has been
proposed as an efficient algorithm for simulating epidemic models on networks,
and in particular for estimating the probability that a given node will become
infectious at a particular time. To date, DMP has been applied exclusively to
models with one-way state changes, as opposed to models like SIS
(susceptible-infectious-susceptible) and SIRS
(susceptible-infectious-recovered-susceptible) where nodes can return to
previously inhabited states. Because many real-world epidemics can exhibit such
recurrent dynamics, we propose a DMP algorithm for complex, recurrent epidemic
models on networks. Our approach takes correlations between neighboring nodes
into account while preventing causal signals from backtracking to their
immediate source, and thus avoids "echo chamber effects" where a pair of
adjacent nodes each amplify the probability that the other is infectious. We
demonstrate that this approach well approximates results obtained from Monte
Carlo simulation and that its accuracy is often superior to the pair
approximation (which also takes second-order correlations into account).
Moreover, our approach is more computationally efficient than the pair
approximation, especially for complex epidemic models: the number of variables
in our DMP approach grows as where is the number of edges and is
the number of states, as opposed to for the pair approximation. We
suspect that the resulting reduction in computational effort, as well as the
conceptual simplicity of DMP, will make it a useful tool in epidemic modeling,
especially for inference tasks where there is a large parameter space to
explore.Comment: 12 pages, 8 figure
multiDimBio: An R Package for the Design, Analysis, and Visualization of Systems Biology Experiments
The past decade has witnessed a dramatic increase in the size and scope of
biological and behavioral experiments. These experiments are providing an
unprecedented level of detail and depth of data. However, this increase in data
presents substantial statistical and graphical hurdles to overcome, namely how
to distinguish signal from noise and how to visualize multidimensional results.
Here we present a series of tools designed to support a research project from
inception to publication. We provide implementation of dimension reduction
techniques and visualizations that function well with the types of data often
seen in animal behavior studies. This package is designed to be used with
experimental data but can also be used for experimental design and sample
justification. The goal for this project is to create a package that will
evolve over time, thereby remaining relevant and reflective of current methods
and techniques
Prudent behaviour accelerates disease transmission
Infectious diseases often spread faster near their peak than would be
predicted given early data on transmission. Despite the commonality of this
phenomena, there are no known general mechanisms able to cause an exponentially
spreading dis- ease to begin spreading faster. Indeed most features of real
world social networks, e.g. clustering1,2 and community structure3, and of
human behaviour, e.g. social distancing4 and increased hygiene5, will slow
disease spread. Here, we consider a model where individuals with essential
societal roles-e.g. teachers, first responders, health-care workers, etc.- who
fall ill are replaced with healthy individuals. We refer to this process as
relational exchange. Relational exchange is also a behavioural process, but one
whose effect on disease transmission is less obvious. By incorporating this
behaviour into a dynamic network model, we demonstrate that replacing
individuals can accelerate disease transmission. Furthermore, we find that the
effects of this process are trivial when considering a standard mass-action
model, but dramatic when considering network structure. This result highlights
another critical shortcoming in mass-action models, namely their inability to
account for behavioural processes. Lastly, using empirical data, we find that
this mechanism parsimoniously explains observed patterns across more than
seventeen years of influenza and dengue virus data. We anticipate that our
findings will advance the emerging field of disease forecasting and will better
inform public health decision making during outbreaks
Estimation with Binned Data
Variables such as household income are sometimes binned, so that we only know
how many households fall in each of several bins such as 10,000-15,000, or $200,000+. We provide a SAS macro that estimates the mean
and variance of binned data by fitting the extended generalized gamma (EGG)
distribution, the power normal (PN) distribution, and a new distribution that
we call the power logistic (PL). The macro also implements a "best-of-breed"
estimator that chooses from among the EGG, PN, and PL estimates on the basis of
likelihood and finite variance. We test the macro by estimating the mean family
and household incomes of approximately 13,000 US school districts between 1970
and 2009. The estimates have negligible bias (0-2%) and a root mean squared
error of just 3-6%. The estimates compare favorably with estimates obtained by
fitting the Dagum, generalized beta (GB2), or logspline distributions.Comment: 16 pages + 2 tables + 4 figure
The Interhospital Transfer Network for Very Low Birth Weight Infants in the United States
Very low birth weight (VLBW) infants require specialized care in neonatal
intensive care units. In the United States (U.S.), such infants frequently are
transferred between hospitals. Although these neonatal transfer networks are
important, both economically and for infant morbidity and mortality, the
national-level pattern of neonatal transfers is largely unknown. Using data
from Vermont Oxford Network on 44,753 births, 2,122 hospitals, and 9,722
inter-hospital infant transfers from 2015, we performed the largest analysis to
date on the inter-hospital transfer network for VLBW infants in the U.S. We
find that transfers are organized around regional communities, but that despite
being largely within state boundaries, most communities often contain at least
two hospitals in different states. To classify the structural variation in
transfer pattern amongst these communities, we applied a spectral measure for
regionalization and found an association between a community's degree of
regionalization and their infant transfer rate, which was not utilized in
detecting communities. We also demonstrate that the established measures of
network centrality and hierarchy, e.g., the community-wide entropy in PageRank
or betweenness centrality and number of distinct `layers' within a community,
correlate weakly with our regionalization index and were not significantly
associated with metrics on infant transfer rate. Our results suggest that the
regionalization index captures novel information about the structural
properties of VLBW infant transfer networks, have the practical implication of
characterizing neonatal care in the U.S., and may apply more broadly to the
role of centralizing forces in organizing complex adaptive systems
Interacting contagions are indistinguishable from social reinforcement
From fake news to innovative technologies, many contagions spread via a
process of social reinforcement, where multiple exposures are distinct from
prolonged exposure to a single source. Contrarily, biological agents such as
Ebola or measles are typically thought to spread as simple contagions. Here, we
demonstrate that interacting simple contagions are indistinguishable from
complex contagions. In the social context, our results highlight the challenge
of identifying and quantifying mechanisms, such as social reinforcement, in a
world where an innumerable amount of ideas, memes and behaviors interact. In
the biological context, this parallel allows the use of complex contagions to
effectively quantify the non-trivial interactions of infectious diseases.Comment: Supplementary Material containing details of our simulation and
inference procedures is available as an ancillary fil
Robust estimation of inequality from binned incomes
Researchers must often estimate income inequality using data that give only
the number of cases (e.g., families or households) whose incomes fall in "bins"
such as 10,000-14,999,..., $200,000+. We find that popular methods
for estimating inequality from binned incomes are not robust in small samples,
where popular methods can produce infinite, undefined, or arbitrarily large
estimates. To solve these and other problems, we develop two improved
estimators: the robust Pareto midpoint estimator (RPME) and the multimodel
generalized beta estimator (MGBE). In a broad evaluation using US national,
state, and county data from 1970 to 2009, we find that both estimators produce
very good estimates of the mean and Gini, but less accurate estimates of the
Theil and mean log deviation. Neither estimator is uniformly more accurate, but
the RPME is much faster, which may be a consideration when many estimates must
be obtained from many datasets. We have made the methods available as the rpme
and mgbe commands for Stata and the binequality package for R.Comment: 39 pages, 7 tables, 7 figure
Socioeconomic bias in influenza surveillance
Individuals in low socioeconomic brackets are considered at-risk for
developing influenza-related complications and often exhibit higher than
average influenza-related hospitalization rates. This disparity has been
attributed to various factors, including restricted access to preventative and
therapeutic health care, limited sick leave, and household structure. Adequate
influenza surveillance in these at-risk populations is a critical precursor to
accurate risk assessments and effective intervention. However, the United
States of America's primary national influenza surveillance system (ILINet)
monitors outpatient healthcare providers, which may be largely inaccessible to
lower socioeconomic populations. Recent initiatives to incorporate
internet-source and hospital electronic medical records data into surveillance
systems seek to improve the timeliness, coverage, and accuracy of outbreak
detection and situational awareness. Here, we use a flexible statistical
framework for integrating multiple surveillance data sources to evaluate the
adequacy of traditional (ILINet) and next generation (BioSense 2.0 and Google
Flu Trends) data for situational awareness of influenza across poverty levels.
We find that zip codes in the highest poverty quartile are a critical
blind-spot for ILINet that the integration of next generation data fails to
ameliorate
- …