6,644 research outputs found
Optimal treatment allocations in space and time for on-line control of an emerging infectious disease
A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America
Invited review: Epidemics on social networks
Since its first formulations almost a century ago, mathematical models for
disease spreading contributed to understand, evaluate and control the epidemic
processes.They promoted a dramatic change in how epidemiologists thought of the
propagation of infectious diseases.In the last decade, when the traditional
epidemiological models seemed to be exhausted, new types of models were
developed.These new models incorporated concepts from graph theory to describe
and model the underlying social structure.Many of these works merely produced a
more detailed extension of the previous results, but some others triggered a
completely new paradigm in the mathematical study of epidemic processes. In
this review, we will introduce the basic concepts of epidemiology, epidemic
modeling and networks, to finally provide a brief description of the most
relevant results in the field.Comment: 17 pages, 13 figure
Enhancing Bayesian risk prediction for epidemics using contact tracing
Contact tracing data collected from disease outbreaks has received relatively
little attention in the epidemic modelling literature because it is thought to
be unreliable: infection sources might be wrongly attributed, or data might be
missing due to resource contraints in the questionnaire exercise. Nevertheless,
these data might provide a rich source of information on disease transmission
rate. This paper presents novel methodology for combining contact tracing data
with rate-based contact network data to improve posterior precision, and
therefore predictive accuracy. We present an advancement in Bayesian inference
for epidemics that assimilates these data, and is robust to partial contact
tracing. Using a simulation study based on the British poultry industry, we
show how the presence of contact tracing data improves posterior predictive
accuracy, and can directly inform a more effective control strategy.Comment: 40 pages, 9 figures. Submitted to Biostatistic
Epidemics on Networks: Reducing Disease Transmission Using Health Emergency Declarations and Peer Communication
Understanding individual decisions in a world where communications and
information move instantly via cell phones and the internet, contributes to the
development and implementation of policies aimed at stopping or ameliorating
the spread of diseases. In this manuscript, the role of official social network
perturbations generated by public health officials to slow down or stop a
disease outbreak are studied over distinct classes of static social networks.
The dynamics are stochastic in nature with individuals (nodes) being assigned
fixed levels of education or wealth. Nodes may change their epidemiological
status from susceptible, to infected and to recovered. Most importantly, it is
assumed that when the prevalence reaches a pre-determined threshold level, P*,
information, called awareness in our framework, starts to spread, a process
triggered by public health authorities. Information is assumed to spread over
the same static network and whether or not one becomes a temporary informer, is
a function of his/her level of education or wealth and epidemiological status.
Stochastic simulations show that threshold selection P* and the value of the
average basic reproduction number impact the final epidemic size
differentially. For the Erdos-Renyi and Small-world networks, an optimal choice
for P* that minimize the final epidemic size can be identified under some
conditions while for Scale-free networks this is not case
Endemicity and prevalence of multipartite viruses under heterogeneous between-host transmission
Multipartite viruses replicate through a puzzling evolutionary strategy.
Their genome is segmented into two or more parts, and encapsidated in separate
particles that appear to propagate independently. Completing the replication
cycle, however, requires the full genome, so that a systemic infection of a
host requires the concurrent presence of several particles. This represents an
apparent evolutionary drawback of multipartitism, while its advantages remain
unclear. A transition from monopartite to multipartite viral forms has been
described in vitro under conditions of high multiplicity of infection,
suggesting that cooperation between defective mutants is a plausible
evolutionary pathway towards multipartitism. However, it is unknown how the
putative advantages that multipartitism might enjoy at the microscopic level
affect its epidemiology, or if an explicit advantange is needed to explain its
ecological persistence. To disentangle which mechanisms might contribute to the
rise and fixation of multipartitism, we investigate the interaction between
viral spreading dynamics and host population structure. We set up a
compartmental model of the spread of a virus in its different forms and explore
its epidemiology using both analytical and numerical techniques. We uncover
that the impact of host contact structure on spreading dynamics entails a rich
phenomenology of ecological relationships that includes cooperation,
competition, and commensality. We find that multipartitism might rise to
fixation even in the absence of explicit microscopic advantages. Multipartitism
allows the virus to colonize environments that could not be invaded by the
monopartite form, facilitated by homogeneous contacts among hosts. We
conjecture that these features might have led to an increase in the diversity
and prevalence of multipartite viral forms concomitantly with the expansion of
agricultural practices.Comment: 27 pages, 4 figures, 1 tabl
Characterising Livestock System Zoonoses Hotspots
A systematic review of the published literature was undertaken, to explore the ability of different types of model to help identify the relative importance of different drivers leading to the development of zoonoses hotspots. We estimated that out of 373 papers we included in our review, 108 papers touched upon the objective of 'Assessment of interventions and intervention policies', 75 addressed the objective of 'Analysis of economic aspects of disease outbreaks and interventions', 67 the objective of 'Prediction of future outbreaks', but only 37 broadly addressed the objective of 'Sensitivity analysis to identify criteria leading to enhanced risk'. Most models of zoonotic diseases are currently capturing outbreaks over relatively short time and largely ignoring socio-economic drivers leading to pathogen emergence, spill-over and spread. In order to study long-term changes we need to understand how socio-economic and climatic changes affect structure of livestock production and how these in turn affect disease emergence and spread. Models capable of describing this processes do not appear to exist, although some progress has been made in linking social and economical aspects of livestock production and in linking economics to disease dynamics. Henceforth we conclude that a new modelling framework is required that expands and formalises the 'one world, one health' strategy, enabling its deployment in the re-thinking of prevention and control strategies. Although modelling can only provide means to identify risks associated with socio-economic changes, it can never be a substitute for data collection. Finally, we note that uncertainty analysis and uncertainty communication form a key element of modelling process and yet are rarely addressed
The role of caretakers in disease dynamics
One of the key challenges in modeling the dynamics of contagion phenomena is
to understand how the structure of social interactions shapes the time course
of a disease. Complex network theory has provided significant advances in this
context. However, awareness of an epidemic in a population typically yields
behavioral changes that correspond to changes in the network structure on which
the disease evolves. This feedback mechanism has not been investigated in
depth. For example, one would intuitively expect susceptible individuals to
avoid other infecteds. However, doctors treating patients or parents tending
sick children may also increase the amount of contact made with an infecteds,
in an effort to speed up recovery but also exposing themselves to higher risks
of infection. We study the role of these caretaker links in an adaptive network
models where individuals react to a disease by increasing or decreasing the
amount of contact they make with infected individuals. We find that pure
avoidance, with only few caretaker links, is the best strategy for curtailing
an SIS disease in networks that possess a large topological variability. In
more homogeneous networks, disease prevalence is decreased for low
concentrations of caretakers whereas a high prevalence emerges if caretaker
concentration passes a well defined critical value.Comment: 8 pages, 9 figure
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