Monte Carlo Simulation Models Evolving in Replicated Runs: A Methodology to Choose the Optimal Experimental Sample Size

Mosca, Roberto; Revetria, Roberto

doi:10.1155/2012/463873

Cited by 28 publications

(15 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same principle also applies to a set of stochastic (Monte Carlo) simulation models in other domains (e.g., traffic flow, financial problems, risk analysis, supply chain forecasting, etc. ), where, in most cases, the standard practice is to report the averages and standard deviations of the measures of interest (known as the Measures of Effectiveness , or MOEs) [43,44]. …”

Section: Introductionmentioning

confidence: 99%

Examining the impact of larval source management and insecticide-treated nets using a spatial agent-based model of Anopheles gambiae and a landscape generator tool

Arifin

Madey

Collins

2013

Malar J

View full text Add to dashboard Cite

BackgroundAgent-based models (ABMs) have been used to estimate the effects of malaria-control interventions. Early studies have shown the efficacy of larval source management (LSM) and insecticide-treated nets (ITNs) as vector-control interventions, applied both in isolation and in combination. However, the robustness of results can be affected by several important modelling assumptions, including the type of boundary used for landscapes, and the number of replicated simulation runs reported in results. Selection of the ITN coverage definition may also affect the predictive findings. Hence, by replication, independent verification of prior findings of published models bears special importance.MethodsA spatially-explicit entomological ABM of Anopheles gambiae is used to simulate the resource-seeking process of mosquitoes in grid-based landscapes. To explore LSM and replicate results of an earlier LSM study, the original landscapes and scenarios are replicated by using a landscape generator tool, and 1,800 replicated simulations are run using absorbing and non-absorbing boundaries. To explore ITNs and evaluate the relative impacts of the different ITN coverage schemes, the settings of an earlier ITN study are replicated, the coverage schemes are defined and simulated, and 9,000 replicated simulations for three ITN parameters (coverage, repellence and mortality) are run. To evaluate LSM and ITNs in combination, landscapes with varying densities of houses and human populations are generated, and 12,000 simulations are run.ResultsGeneral agreement with an earlier LSM study is observed when an absorbing boundary is used. However, using a non-absorbing boundary produces significantly different results, which may be attributed to the unrealistic killing effect of an absorbing boundary. Abundance cannot be completely suppressed by removing aquatic habitats within 300 m of houses. Also, with density-dependent oviposition, removal of insufficient number of aquatic habitats may prove counter-productive. The importance of performing large number of simulation runs is also demonstrated. For ITNs, the choice of coverage scheme has important implications, and too high repellence yields detrimental effects. When LSM and ITNs are applied in combination, ITNs’ mortality can play more important roles with higher densities of houses. With partial mortality, increasing ITN coverage is more effective than increasing LSM coverage, and integrating both interventions yields more synergy as the densities of houses increase.ConclusionsUsing a non-absorbing boundary and reporting average results from sufficiently large number of simulation runs are strongly recommended for malaria ABMs. Several guidelines (code and data sharing, relevant documentation, and standardized models) for future modellers are also recommended.

show abstract

Section: Introductionmentioning

confidence: 99%

Examining the impact of larval source management and insecticide-treated nets using a spatial agent-based model of Anopheles gambiae and a landscape generator tool

Arifin

Madey

Collins

2013

Malar J

View full text Add to dashboard Cite

show abstract

“…The Monte Carlo simulation was set‐up for 300 runs to generate a stochastic dataset of feed cell count, feed cell viability, and feed turbidity, based on the fitted log‐normal distribution. Three hundred iterations were found to be sufficient to reach convergence and hence provided a reliable probability distribution of all the feed input parameters . Using this stochastic dataset, pressure profiles for all the 300 clarification lots were predicted using the developed ANN network.…”

Section: Resultsmentioning

confidence: 99%

“…Three hundred iterations were found to be sufficient to reach convergence and hence provided a reliable probability distribution of all the feed input parameters. 35 Using this stochastic dataset, pressure profiles for all the 300 clarification lots were predicted using the developed ANN network. The filter loading capacity was determined for each case using a cutoff pressure of 1.1 bar.…”

Section: Variable Filter Sizingmentioning

confidence: 99%

Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

Agarwal

Rathore

Hadpe

et al. 2016

Biotechnology Progress

View full text Add to dashboard Cite

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016.

show abstract

“…in the form of a probability density function (PDF) and also making proper use of the Monte Carlo method [22,23].…”

Section: Discussionmentioning

confidence: 99%

A design of experiments/response surface methodology approach to study the economic sustainability of a 1 MWe photovoltaic plant

Bendato

Mosca

2015

Renewable and Sustainable Energy Reviews

Self Cite

View full text Add to dashboard Cite

Monte Carlo Simulation Models Evolving in Replicated Runs: A Methodology to Choose the Optimal Experimental Sample Size

Cited by 28 publications

References 4 publications

Examining the impact of larval source management and insecticide-treated nets using a spatial agent-based model of Anopheles gambiae and a landscape generator tool

Examining the impact of larval source management and insecticide-treated nets using a spatial agent-based model of Anopheles gambiae and a landscape generator tool

Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

A design of experiments/response surface methodology approach to study the economic sustainability of a 1 MWe photovoltaic plant

Contact Info

Product

Resources

About