144 research outputs found
Mass campaigns with antimalarial drugs: a modelling comparison of artemether-lumefantrine and DHA-piperaquine with and without primaquine as tools for malaria control and elimination
Antimalarial drugs are a powerful tool for malaria control and elimination.
Artemisinin-based combination therapies (ACTs) can reduce transmission when
widely distributed in a campaign setting. Modelling mass antimalarial campaigns
can elucidate how to most effectively deploy drug-based interventions and
quantitatively compare the effects of cure, prophylaxis, and
transmission-blocking in suppressing parasite prevalence. A previously
established agent-based model that includes innate and adaptive immunity was
used to simulate malaria infections and transmission. Pharmacokinetics of
artemether, lumefantrine, dihydroartemisinin, piperaquine, and primaquine were
modelled with a double-exponential distribution-elimination model including
weight-dependent parameters and age-dependent dosing. Drug killing of asexual
parasites and gametocytes was calibrated to clinical data. Mass distribution of
ACTs and primaquine was simulated with seasonal mosquito dynamics at a range of
transmission intensities. A single mass campaign with antimalarial drugs is
insufficient to permanently reduce malaria prevalence when transmission is
high. Current diagnostics are insufficiently sensitive to accurately identify
asymptomatic infections, and mass-screen-and-treat campaigns are much less
efficacious than mass drug administrations. Improving campaign coverage leads
to decreased prevalence one month after the end of the campaign, while
increasing compliance lengthens the duration of protection against reinfection.
Use of a long-lasting prophylactic as part of a mass drug administration
regimen confers the most benefit under conditions of high transmission and
moderately high coverage. Addition of primaquine can reduce prevalence but
exerts its largest effect when coupled with a long-lasting prophylactic.Comment: 14 pages, 5 figure
A malaria transmission-directed model of mosquito life cycle and ecology
<p>Abstract</p> <p>Background</p> <p>Malaria is a major public health issue in much of the world, and the mosquito vectors which drive transmission are key targets for interventions. Mathematical models for planning malaria eradication benefit from detailed representations of local mosquito populations, their natural dynamics and their response to campaign pressures.</p> <p>Methods</p> <p>A new model is presented for mosquito population dynamics, effects of weather, and impacts of multiple simultaneous interventions. This model is then embedded in a large-scale individual-based simulation and results for local elimination of malaria are discussed. Mosquito population behaviours, such as anthropophily and indoor feeding, are included to study their effect upon the efficacy of vector control-based elimination campaigns.</p> <p>Results</p> <p>Results for vector control tools, such as bed nets, indoor spraying, larval control and space spraying, both alone and in combination, are displayed for a single-location simulation with vector species and seasonality characteristic of central Tanzania, varying baseline transmission intensity and vector bionomics. The sensitivities to habitat type, anthropophily, indoor feeding, and baseline transmission intensity are explored.</p> <p>Conclusions</p> <p>The ability to model a spectrum of local vector species with different ecologies and behaviours allows local customization of packages of interventions and exploration of the effect of proposed new tools.</p
Fractional diffusion emulates a human mobility network during a simulated disease outbreak
From footpaths to flight routes, human mobility networks facilitate the
spread of communicable diseases. Control and elimination efforts depend on
characterizing these networks in terms of connections and flux rates of
individuals between contact nodes. In some cases, transport can be
parameterized with gravity-type models or approximated by a diffusive random
walk. As a alternative, we have isolated intranational commercial air traffic
as a case study for the utility of non-diffusive, heavy-tailed transport
models. We implemented new stochastic simulations of a prototypical
influenza-like infection, focusing on the dense, highly-connected United States
air travel network. We show that mobility on this network can be described
mainly by a power law, in agreement with previous studies. Remarkably, we find
that the global evolution of an outbreak on this network is accurately
reproduced by a two-parameter space-fractional diffusion equation, such that
those parameters are determined by the air travel network.Comment: 26 pages, 4 figure
Optimality and robustness of a biophysical decision-making model under norepinephrine modulation
The locus coeruleus (LC) can exhibit tonic or phasic activity and release norepinephrine (NE) throughout the cortex, modulating cellular excitability and synaptic efficacy and thus influencing behavioral performance. We study the effects of LC-NE modulation on decision making in two-alternative forced-choice tasks by changing conductances in a biophysical neural network model, and we investigate how it affects performance measured in terms of reward rate. We find that low tonic NE levels result in unmotivated behavior and high levels in impulsive, inaccurate choices, but that near-optimal performance can occur over a broad middle range. Robustness is greatest when pyramidal cells are less strongly modulated than interneurons, and superior performance can be achieved with phasic NE release, provided only glutamatergic synapses are modulated. We also show that network functions such as sensory information accumulation and short-term memory can be modulated by tonic NE levels, and that previously-observed diverse evoked cell responses may be due to network effects
Dimension Reduction and Dynamics of a Spiking Neural Network Model for Decision Making under Neuromodulation
Previous models of neuromodulation in cortical circuits have used either physiologically based networks of spiking neurons or simplified gain adjustments in low-dimensional connectionist models. Here we reduce a high-dimensional spiking neuronal network model, first to a four-population mean-field model and then to a two-population model. This provides a realistic implementation of neuromodulation in low-dimensional decision-making models, speeds up simulations by three orders of magnitude, and allows bifurcation and phase-plane analyses of the reduced models that illuminate neuromodulatory mechanisms. As modulation of excitation-inhibition varies, the network can move from unaroused states, through optimal performance to impulsive states, and eventually lose inhibition-driven winner-take-all behavior: all are clear outcomes of the bifurcation structure. We illustrate the value of reduced models by a study of the speed-accuracy tradeoff in decision making. The ability of such models to recreate neuromodulatory dynamics of the spiking network will accelerate the pace of future experiments linking behavioral data to cellular neurophysiology
The Separatrix Algorithm for Synthesis and Analysis of Stochastic Simulations with Applications in Disease Modeling
Decision makers in epidemiology and other disciplines are faced with the daunting challenge of designing interventions that will be successful with high probability and robust against a multitude of uncertainties. To facilitate the decision making process in the context of a goal-oriented objective (e.g., eradicate polio by ), stochastic models can be used to map the probability of achieving the goal as a function of parameters. Each run of a stochastic model can be viewed as a Bernoulli trial in which “success” is returned if and only if the goal is achieved in simulation. However, each run can take a significant amount of time to complete, and many replicates are required to characterize each point in parameter space, so specialized algorithms are required to locate desirable interventions. To address this need, we present the Separatrix Algorithm, which strategically locates parameter combinations that are expected to achieve the goal with a user-specified probability of success (e.g. 95%). Technically, the algorithm iteratively combines density-corrected binary kernel regression with a novel information-gathering experiment design to produce results that are asymptotically correct and work well in practice. The Separatrix Algorithm is demonstrated on several test problems, and on a detailed individual-based simulation of malaria
Optimal population-level infection detection strategies for malaria control and elimination in a spatial model of malaria transmission
Mass campaigns with antimalarial drugs are potentially a powerful tool for
local elimination of malaria, yet current diagnostic technologies are
insufficiently sensitive to identify all individuals who harbor infections. At
the same time, overtreatment of uninfected individuals increases the risk of
accelerating emergence of drug resistance and losing community acceptance.
Local heterogeneity in transmission intensity may allow campaign strategies
that respond to index cases to successfully target subpatent infections while
simultaneously limiting overtreatment. While selective targeting of hotspots of
transmission has been proposed as a strategy for malaria control, such
targeting has not been tested in the context of malaria elimination. Using
household locations, demographics, and prevalence data from a survey of four
health facility catchment areas in southern Zambia and an agent-based model of
malaria transmission and immunity acquisition, a transmission intensity was fit
to each household based on neighborhood age-dependent malaria prevalence. A set
of individual infection trajectories was constructed for every household in
each catchment area, accounting for heterogeneous exposure and immunity.
Various campaign strategies (mass drug administration, mass screen and treat,
focal mass drug administration, snowball reactive case detection, pooled
sampling, and a hypothetical serological diagnostic) were simulated and
evaluated for performance at finding infections, minimizing overtreatment,
reducing clinical case counts, and interrupting transmission. For malaria
control, presumptive treatment leads to substantial overtreatment without
additional morbidity reduction under all but the highest transmission
conditions. Selective targeting of hotspots with drug campaigns is an
ineffective tool for elimination due to limited sensitivity of available field
diagnostics
Malaria elimination campaigns in the Lake Kariba region of Zambia: a spatial dynamical model
Background As more regions approach malaria elimination, understanding how
different interventions interact to reduce transmission becomes critical. The
Lake Kariba area of Southern Province, Zambia, is part of a multi-country
elimination effort and presents a particular challenge as it is an
interconnected region of variable transmission intensities.
Methods In 2012-13, six rounds of mass-screen-and-treat drug campaigns were
carried out in the Lake Kariba region. A spatial dynamical model of malaria
transmission in the Lake Kariba area, with transmission and climate modeled at
the village scale, was calibrated to the 2012-13 prevalence survey data, with
case management rates, insecticide-treated net usage, and drug campaign
coverage informed by surveillance. The model was used to simulate the effect of
various interventions implemented in 2014-22 on reducing regional transmission,
achieving elimination by 2022, and maintaining elimination through 2028.
Findings The model captured the spatio-temporal trends of decline and rebound
in malaria prevalence in 2012-13 at the village scale. Simulations predicted
that elimination required repeated mass drug administrations coupled with
simultaneous increase in net usage. Drug campaigns targeted only at high-burden
areas were as successful as campaigns covering the entire region.
Interpretation Elimination in the Lake Kariba region is possible through
coordinating mass drug campaigns with high-coverage vector control. Targeting
regional hotspots is a viable alternative to global campaigns when human
migration within an interconnected area is responsible for maintaining
transmission in low-burden areas
Characterization of the infectious reservoir of malaria with an agent-based model calibrated to age-stratified parasite densities and infectiousness
Background Elimination of malaria can only be achieved through removal of all
vectors or complete depletion of the infectious reservoir in humans.
Mechanistic models can be built to synthesize diverse observations from the
field collected under a variety of conditions and subsequently used to query
the infectious reservoir in great detail. Methods The EMOD model of malaria
transmission was calibrated to prevalence, incidence, asexual parasite density,
gametocyte density, infection duration, and infectiousness data from 9 study
sites. The infectious reservoir was characterized by diagnostic detection limit
and age group over a range of transmission intensities with and without case
management and vector control. Mass screen-and-treat drug campaigns were tested
for likelihood of achieving elimination. Results The composition of the
infectious reservoir by diagnostic threshold is similar over a range of
transmission intensities, and higher intensity settings are biased toward
infections in children. Recent ramp-ups in case management and use of
insecticide-treated bednets reduce the infectious reservoir and shift the
composition toward submicroscopic infections. Mass campaigns with antimalarial
drugs are highly effective at interrupting transmission if deployed shortly
after ITN campaigns. Conclusions Low density infections comprise a substantial
portion of the infectious reservoir. Proper timing of vector control, seasonal
variation in transmission intensity, and mass drug campaigns allows lingering
population immunity to help drive a region toward elimination.Comment: submitted to Malaria Journal on March 31, 201
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