Agent-based modelling of complex factors impacting malaria prevalence.

BACKGROUND: Increasingly complex models have been developed to characterize the transmission dynamics of malaria. The multiplicity of malaria transmission factors calls for a realistic modelling approach that incorporates various complex factors such as the effect of control measures, behavioural impacts of the parasites to the vector, or socio-economic variables. Indeed, the crucial impact of household size in eliminating malaria has been emphasized in previous studies. However, increasing complexity also increases the difficulty of calibrating model parameters. Moreover, despite the availability of much field data, a common pitfall in malaria transmission modelling is to obtain data that could be directly used for model calibration. METHODS: In this work, an approach that provides a way to combine in situ field data with the parameters of malaria transmission models is presented. This is achieved by agent-based stochastic simulations, initially calibrated with hut-level experimental data. The simulation results provide synthetic data for regression analysis that enable the calibration of key parameters of classical models, such as biting rates and vector mortality. In lieu of developing complex dynamical models, the approach is demonstrated using most classical malaria models, but with the model parameters calibrated to account for such complex factors. The performance of the approach is tested against a wide range of field data for Entomological Inoculation Rate (EIR) values. RESULTS: The overall transmission characteristics can be estimated by including various features that impact EIR and malaria incidence, for instance by reducing the mosquito-human contact rates and increasing the mortality through control measures or socio-economic factors. CONCLUSION: Complex phenomena such as the impact of the coverage of the population with long-lasting insecticidal nets (LLINs), changes in behaviour of the infected vector and the impact of socio-economic factors can be included in continuous level modelling. Though the present work should be interpreted as a proof of concept, based on one set of field data only, certain interesting conclusions can already be drawn. While the present work focuses on malaria, the computational approach is generic, and can be applied to other cases where suitable in situ data is available.

Statistical approach for parameter identification by Turing patterns.

Prevailing theories in biological pattern formation, such as in morphogenesis or multicellular structures development, have been based on purely chemical processes, with the Turing models as the prime example. Recent studies have challenged the approach, by underlining the role of mechanical forces. A quantitative discrimination of competing theories is difficult, however, due to the elusive character of the processes: different mechanisms may result in similar patterns, while patterns obtained with a fixed model and fixed parameter values, but with small random perturbations of initial values, will significantly differ in shape, while being of the "same" type. In this sense each model parameter value corresponds to a family of patterns, rather than a fixed solution. For this situation we create a likelihood that allows a statistically sound way to distinguish the model parameters that correspond to given patterns. The method allows us to identify model parameters of reaction-diffusion systems by using Turing patterns only, i.e., the steady-state solutions of the respective equations without the use of transient data or initial values. The method is tested with three classical models of pattern formation: the FitzHugh-Nagumo model, Gierer-Meinhardt system and Brusselator reaction-diffusion system. We quantify the accuracy achieved by different amounts of training data by Bayesian sampling methods. We demonstrate how a large enough ensemble of patterns leads to detection of very small but systematic structural changes, practically impossible to distinguish with the naked eye.

Presynaptic cholinergic neuromodulation alters the temporal dynamics of short-term depression at parvalbumin-positive basket cell synapses from juvenile CA1 mouse hippocampus.

Parvalbumin-positive basket cells (PV BCs) of the CA1 hippocampus are active participants in theta (5-12 Hz) and gamma (20-80 Hz) oscillations in vivo. When PV BCs are driven at these frequencies in vitro, inhibitory postsynaptic currents (IPSCs) in synaptically connected CA1 pyramidal cells exhibit paired-pulse depression (PPD) and multiple-pulse depression (MPD). Moreover, PV BCs express presynaptic muscarinic acetylcholine receptors (mAChRs) that may be activated by synaptically released acetylcholine during learning behaviors in vivo. Using acute hippocampal slices from the CA1 hippocampus of juvenile PV-GFP mice, we performed whole cell recordings from synaptically connected PV BC-CA1 pyramidal cell pairs to investigate how bath application of 10 µM muscarine impacts PPD and MPD at CA1 PV BC-pyramidal cell synapses. In accordance with previous studies, PPD and MPD magnitude increased with stimulation frequency. mAChR activation reduced IPSC amplitude and transiently reduced PPD, but MPD was largely maintained. Consistent with a reduction in release probability (pr), MPD and mAChR activation increased both the coefficient of variation of IPSC amplitudes and the fraction of failures. Using variance-mean analysis, we converted MPD trains to pr functions and developed a kinetic model that optimally fit six distinct pr conditions. The model revealed that vesicular depletion caused MPD and that recovery from depression was dependent on calcium. mAChR activation reduced the presynaptic calcium transient fourfold and initial pr twofold, thereby reducing PPD. However, mAChR activation slowed calcium-dependent recovery from depression during sustained repetitive activity, thereby preserving MPD. Thus the activation of presynaptic mAChRs optimally protects PV BCs from vesicular depletion during short bursts of high-frequency activity.

Generalized correlation integral vectors: A distance concept for chaotic dynamical systems.

Several concepts of fractal dimension have been developed to characterise properties of attractors of chaotic dynamical systems. Numerical approximations of them must be calculated by finite samples of simulated trajectories. In principle, the quantities should not depend on the choice of the trajectory, as long as it provides properly distributed samples of the underlying attractor. In practice, however, the trajectories are sensitive with respect to varying initial values, small changes of the model parameters, to the choice of a solver, numeric tolerances, etc. The purpose of this paper is to present a statistically sound approach to quantify this variability. We modify the concept of correlation integral to produce a vector that summarises the variability at all selected scales. The distribution of this stochastic vector can be estimated, and it provides a statistical distance concept between trajectories. Here, we demonstrate the use of the distance for the purpose of estimating model parameters of a chaotic dynamic model. The methodology is illustrated using computational examples for the Lorenz 63 and Lorenz 95 systems, together with a framework for Markov chain Monte Carlo sampling to produce posterior distributions of model parameters.

A mathematical model of Rift Valley Fever with human host.

Rift Valley Fever is a vector-borne disease mainly transmitted by mosquito. To gain some quantitative insights into its dynamics, a deterministic model with mosquito, livestock, and human host is formulated as a system of nonlinear ordinary differential equations and analyzed. The disease threshold [Formula: see text] is computed and used to investigate the local stability of the equilibria. A sensitivity analysis is performed and the most sensitive model parameters to the measure of initial disease transmission [Formula: see text] and the endemic equilibrium are determined. Both [Formula: see text] and the disease prevalence in mosquitoes are more sensitive to the natural mosquito death rate, d(m). The disease prevalence in livestock and humans are more sensitive to livestock and human recruitment rates, [Formula: see text] and [Formula: see text], respectively, suggesting isolation of livestock from humans is a viable preventive strategy during an outbreak. Numerical simulations support the analytical results in further exploring theoretically the long-term dynamics of the disease at the population level.

Septohippocampal transmission from parvalbumin-positive neurons features rapid recovery from synaptic depression.

Parvalbumin-containing projection neurons of the medial-septum-diagonal band of Broca ([Formula: see text]) are essential for hippocampal rhythms and learning operations yet are poorly understood at cellular and synaptic levels. We combined electrophysiological, optogenetic, and modeling approaches to investigate [Formula: see text] neuronal properties. [Formula: see text] neurons had intrinsic membrane properties distinct from acetylcholine- and somatostatin-containing MS-DBB subtypes. Viral expression of the fast-kinetic channelrhodopsin ChETA-YFP elicited action potentials to brief (1-2 ms) 470 nm light pulses. To investigate [Formula: see text] transmission, light pulses at 5-50 Hz frequencies generated trains of inhibitory postsynaptic currents (IPSCs) in CA1 stratum oriens interneurons. Using a similar approach, optogenetic activation of local hippocampal PV ([Formula: see text]) neurons generated trains of [Formula: see text]-mediated IPSCs in CA1 pyramidal neurons. Both synapse types exhibited short-term depression (STD) of IPSCs. However, relative to [Formula: see text] synapses, [Formula: see text] synapses possessed lower initial release probability, transiently resisted STD at gamma (20-50 Hz) frequencies, and recovered more rapidly from synaptic depression. Experimentally-constrained mathematical synapse models explored mechanistic differences. Relative to the [Formula: see text] model, the [Formula: see text] model exhibited higher sensitivity to calcium accumulation, permitting a faster rate of calcium-dependent recovery from STD. In conclusion, resistance of [Formula: see text] synapses to STD during short gamma bursts enables robust long-range GABAergic transmission from MS-DBB to hippocampus.

Reduced models of algae growth.

The simulation of biological systems is often plagued by a high level of noise in the data, as well as by models containing a large number of correlated parameters. As a result, the parameters are poorly identified by the data, and the reliability of the model predictions may be questionable. Bayesian sampling methods provide an avenue for proper statistical analysis in such situations. Nevertheless, simulations should employ models that, on the one hand, are reduced as much as possible, and, on the other hand, are still able to capture the essential features of the phenomena studied. Here, in the case of algae growth modeling, we show how a systematic model reduction can be done. The simplified model is analyzed from both theoretical and statistical points of view.

Modeling host-seeking behavior of African malaria vector mosquitoes in the presence of long-lasting insecticidal nets.

The efficiency of spatial repellents and long-lasting insecticide-treated nets (LLINs) is a key research topic in malaria control. Insecticidal nets reduce the mosquito-human contact rate and simultaneously decrease mosquito populations. However, LLINs demonstrate dissimilar efficiency against different species of malaria mosquitoes. Various factors have been proposed as an explanation, including differences in insecticide-induced mortality, flight characteristics, or persistence of attack. Here we present a discrete agent-based approach that enables the efficiency of LLINs, baited traps and Insecticide Residual Sprays (IRS) to be examined. The model is calibrated with hut-level experimental data to compare the efficiency of protection against two mosquito species: Anopheles gambiae and Anopheles arabiensis. We show that while such data does not allow an unambiguous identification of the details of how LLINs alter the vector behavior, the model calibrations quantify the overall impact of LLINs for the two different mosquito species. The simulations are generalized to community-scale scenarios that systematically demonstrate the lower efficiency of the LLINs in control of An. arabiensis compared to An. gambiae.

Serum complement C3/C4 ratio, a novel marker for recurrent cardiovascular events.

Acute coronary syndrome is an inflammatory disease, during which the complement cascade is activated. We assessed the complement C3 and C4 concentration ratio (C3/C4 ratio) in serum as a potential measurement to predict cardiovascular attacks. Patients with acute coronary syndrome (n=148) were followed after an initial attack for subsequent ischemic cardiovascular events (composite end point of death, myocardial infarction, recurrent unstable angina, or stroke). During the follow-up period (average 555 days), 44 patients met an end point. Blood samples were taken at hospitalization, 1 week, 3 months, and 1 year after hospital admission. Serum complement C3 and C4 concentrations and the C3/C4 ratio were analyzed. Patients with an end point had, throughout the follow-up period, a higher C3/C4 ratio than patients without these end points (repeated measures analysis of variance, p=0.007). When all traditional cardiovascular risk factors and other potential confounding factors were included in a Cox multivariate logistic regression survival analysis, the C3/C4 ratio emerged as the novel risk factor for any new cardiovascular event (odds ratio 1.33, 95% confidence interval 1.08 to 1.63, p=0.007). When the C3/C4 ratio was divided into 4 quartiles, 24% in quartiles 1 and 2 (lowest) and 48% in quartile 4 (highest) had end points during follow-up (odds ratio 3.04, 95% confidence interval 1.27 to 7.29, p=0.01). In conclusion, increased serum C3/C4 ratio is a readily available and novel marker for recurrent cardiovascular events in acute coronary syndrome. The relative increase in serum C3 protein and decrease in C4 protein could explain changes in the C3/C4 ratio.

Optimal control problems of epidemic systems with parameter uncertainties: application to a malaria two-age-classes transmission model with asymptomatic carriers.

The main scope of this paper is to study the optimal control practices of malaria, by discussing the implementation of a catalog of optimal control strategies in presence of parameter uncertainties, which is typical of infectious diseases data. In this study we focus on a deterministic mathematical model for the transmission of malaria, including in particular asymptomatic carriers and two age classes in the human population. A partial qualitative analysis of the relevant ODE system has been carried out, leading to a realistic threshold parameter. For the deterministic model under consideration, four possible control strategies have been analyzed: the use of Long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic and asymptomatic individuals. The numerical results show that using optimal control the disease can be brought to a stable disease free equilibrium when all four controls are used. The Incremental Cost-Effectiveness Ratio (ICER) for all possible combinations of the disease-control measures is determined. The numerical simulations of the optimal control in the presence of parameter uncertainty demonstrate the robustness of the optimal control: the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the designing of cost-effective strategies for disease controls with multiple interventions, even under considerable uncertainty of model parameters.

Segmentation of Overlapping Elliptical Objects in Silhouette Images.

Segmentation of partially overlapping objects with a known shape is needed in an increasing amount of various machine vision applications. This paper presents a method for segmentation of clustered partially overlapping objects with a shape that can be approximated using an ellipse. The method utilizes silhouette images, which means that it requires only that the foreground (objects) and background can be distinguished from each other. The method starts with seedpoint extraction using bounded erosion and fast radial symmetry transform. Extracted seedpoints are then utilized to associate edge points to objects in order to create contour evidence. Finally, contours of the objects are estimated by fitting ellipses to the contour evidence. The experiments on one synthetic and two different real data sets showed that the proposed method outperforms two current state-of-art approaches in overlapping objects segmentation.

A kinetic model for the frequency dependence of cholinergic modulation at hippocampal GABAergic synapses.

In this paper we use a simple model of presynaptic neuromodulation of GABA signaling to decipher paired whole-cell recordings of frequency dependent cholinergic neuromodulation at CA1 parvalbumin-containing basket cell (PV BC)-pyramidal cell synapses. Variance-mean analysis is employed to normalize the data, which is then used to estimate parameters in the mathematical model. Various parameterizations and hidden parameter dependencies are investigated using Markov Chain Monte Carlo (MCMC) parameter estimation techniques. This analysis reveals that frequency dependence of cholinergic modulation requires both calcium-dependent recovery from depression and mAChR-induced inhibition of presynaptic calcium entry. A reduction in calcium entry into the presynaptic terminal in the kinetic model accounted for the frequency-dependent effects of mAChR activation.

Modeling the effects and uncertainties of contaminated sediment remediation scenarios in a Norwegian fjord by Markov chain Monte Carlo simulation.

Multimedia environmental fate models are useful tools to investigate the long-term impacts of remediation measures designed to alleviate potential ecological and human health concerns in contaminated areas. Estimating and communicating the uncertainties associated with the model simulations is a critical task for demonstrating the transparency and reliability of the results. The Extended Fourier Amplitude Sensitivity Test(Extended FAST) method for sensitivity analysis and Bayesian Markov chain Monte Carlo (MCMC) method for uncertainty analysis and model calibration have several advantages over methods typically applied for multimedia environmental fate models. Most importantly, the simulation results and their uncertainties can be anchored to the available observations and their uncertainties. We apply these techniques for simulating the historical fate of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the Grenland fjords, Norway, and for predicting the effects of different contaminated sediment remediation (capping) scenarios on the future levels of PCDD/Fs in cod and crab therein. The remediation scenario simulations show that a significant remediation effect can first be seen when significant portions of the contaminated sediment areas are cleaned up, and that increase in capping area leads to both earlier achievement of good fjord status and narrower uncertainty in the predicted timing for this.

Effect of nutrient loading on bacterioplankton community composition in lake mesocosms.

Changes in bacterioplankton community composition were followed in mesocosms set up in the littoral of Lake Vesijärvi, southern Finland, over two summers. Increasing nitrogen and phosphorus concentrations in the mesocosms represented different trophic states, from mesotrophic to hypertrophic. In 1998, the mesocosms were in a turbid state with a high biomass of phytoplankton, whereas in 1999, macrophytes proliferated and a clear-water state prevailed. The bacterial communities in the mesocosms also developed differently, as shown by denaturing gradient gel electrophoresis profiling of partial 16S rRNA gene fragments and by nonmetric multidimensional scaling analysis. In 1998, nutrient treatments affected the diversity and clustering of bacterial communities strongly, but in 1999, the bacterial communities were less diversified and not clearly affected by treatments. Canonical correspondence analysis indicated that bacterioplankton communities in the mesocosms were influenced by environmental physicochemical variables linked to the increasing level of eutrophication. Nitrogen concentration correlated directly with the bacterioplankton composition. In addition, the high nutrient levels had indirect effects through changes in the biomass and composition of phyto- and zooplankton. Sequencing analysis showed that the dominant bacterial divisions remained the same, but the dominant phylotypes changed during the 2-year period. The occurrence of Verrucomicrobia correlated with more eutrophic conditions, whereas the occurrence of Actinobacteria correlated with less eutrophic conditions.