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.

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.

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.

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 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.