Calibration of Large Spatial Models: A Multistage, Multiobjective Optimization Technique

Villa, Ferdinando; Voinov, Alexey; Fitz, Carl; Costanza, Robert

doi:10.1007/0-387-21555-7_4

Cited by 5 publications

(7 citation statements)

References 31 publications

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“…The six target variables were combined hierarchically into a model performance index (MPI; Villa et al 2004). The basic criteria (indicated by the prefix 'c_') were defined by the minimum of the ratio of the simulated value to the reference value and their inverse ratio.…”

Section: Evaluation Of Simulation Resultsmentioning

confidence: 99%

Modelling forest recolonization by an epiphytic lichen using a landscape genetic approach

et al. 2006

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The process of recolonization after disturbance is crucial for the persistence and dynamics of patch-tracking metapopulations. We developed a model to compare the spatial distribution and spatial genetic structure of the epiphytic lichen Lobaria pulmonaria within the perimeter of two reconstructed 19th century disturbances with a nearby reference area without stand-level disturbance. Population genetic data suggested that after stand-replacing disturbance, each plot was colonized by one or a few genotypes only, which subsequently spread clonally within a local neighborhood. The model (cellular automaton) aimed at testing the validity of this interpretation and at assessing the relative importance of local dispersal of clonal propagules vs. long-distance dispersal of clonal and/or sexual diaspores. A reasonable model fit was reached for the empirical data on host tree distribution, lichen distribution, and tree-and plot-level genotype diversity of the lichen in the reference area. Although model calibration suggested a predominance of local dispersal of clonal propagules, a substantial contribution of immigration of non-local genotypes by long-distance dispersal was needed to reach the observed levels of genotype diversity. The model could not fully explain the high degree of clonality after stand-replacing disturbance, suggesting that the dispersal process itself may not be stationary but depend on the ecological conditions related to disturbance.

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Section: Evaluation Of Simulation Resultsmentioning

confidence: 99%

Modelling forest recolonization by an epiphytic lichen using a landscape genetic approach

et al. 2006

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“…Using parasite transmission models for producing regional-scale intervention predictions presents a number of difficulties, which chiefly arise from the heterogeneity that underlies infection patterns across a spatial domain [ 17 , 18 , 55 – 58 ]. At the heart of these challenges is the problem of how best to scale transmission processes up from the local setting to predict phenomena at coarser hierarchical scales of space and time, particularly when inference on aggregate properties of entities of interest is based on models developed using components and processes estimated at small fine-scale levels [ 55 – 57 , 59 , 60 ].…”

Section: Discussionmentioning

confidence: 99%

Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020

Michael

Singh

Mayala

et al. 2017

BMC Med

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BackgroundThere are growing demands for predicting the prospects of achieving the global elimination of neglected tropical diseases as a result of the institution of large-scale nation-wide intervention programs by the WHO-set target year of 2020. Such predictions will be uncertain due to the impacts that spatial heterogeneity and scaling effects will have on parasite transmission processes, which will introduce significant aggregation errors into any attempt aiming to predict the outcomes of interventions at the broader spatial levels relevant to policy making. We describe a modeling platform that addresses this problem of upscaling from local settings to facilitate predictions at regional levels by the discovery and use of locality-specific transmission models, and we illustrate the utility of using this approach to evaluate the prospects for eliminating the vector-borne disease, lymphatic filariasis (LF), in sub-Saharan Africa by the WHO target year of 2020 using currently applied or newly proposed intervention strategies.Methods and ResultsWe show how a computational platform that couples site-specific data discovery with model fitting and calibration can allow both learning of local LF transmission models and simulations of the impact of interventions that take a fuller account of the fine-scale heterogeneous transmission of this parasitic disease within endemic countries. We highlight how such a spatially hierarchical modeling tool that incorporates actual data regarding the roll-out of national drug treatment programs and spatial variability in infection patterns into the modeling process can produce more realistic predictions of timelines to LF elimination at coarse spatial scales, ranging from district to country to continental levels. Our results show that when locally applicable extinction thresholds are used, only three countries are likely to meet the goal of LF elimination by 2020 using currently applied mass drug treatments, and that switching to more intensive drug regimens, increasing the frequency of treatments, or switching to new triple drug regimens will be required if LF elimination is to be accelerated in Africa. The proportion of countries that would meet the goal of eliminating LF by 2020 may, however, reach up to 24/36 if the WHO 1% microfilaremia prevalence threshold is used and sequential mass drug deliveries are applied in countries.ConclusionsWe have developed and applied a data-driven spatially hierarchical computational platform that uses the discovery of locally applicable transmission models in order to predict the prospects for eliminating the macroparasitic disease, LF, at the coarser country level in sub-Saharan Africa. We show that fine-scale spatial heterogeneity in local parasite transmission and extinction dynamics, as well as the exact nature of intervention roll-outs in countries, will impact the timelines to achieving national LF elimination on this continent.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-017-0933-2) contains sup...

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“…Using parasite transmission models for producing regional-scale intervention predictions presents a number of difficulties, which chiefly arise from the heterogeneity that underlies infection patterns across a spatial domain [17,18,[55][56][57][58]. At the heart of these challenges is the problem of how best to scale transmission processes up from the local setting to predict phenomena at coarser hierarchical scales of space and time, particularly when inference on aggregate properties of entities of interest is based on models developed using components and processes estimated at small fine-scale levels [55-57, 59, 60].…”

Section: Discussionmentioning

confidence: 99%

“…2) for conducting the search, analysis, and integration of the required data, and, on the other, given gaps in these data, also consideration of how to best estimate the needed localized data using various methods of interpolation or prediction (see Methods). These vagaries in the type of input data, whether contributed by the limited availability of measured data at the scale of modeling (MDA coverages) or through errors in the data estimated for sample sites (by mapping (e.g., mf prevalence, VC coverage), modelbased predictions (e.g., ABR values), or derivations (mf age prevalences)), mean that errors in model calibration and therefore in the precision of our predictions are inevitable [58]. While this cautions against the uncritical use of the present modeling results, it is also important to realize that this limitation in data for undertaking spatially structured modeling is partly procedural and therefore fixable.…”

Section: Discussionmentioning

confidence: 99%

Continental-Scale, Data-Driven Predictive Assessment of Eliminating the Vector-Borne Disease, Lymphatic Filariasis, in Sub-Saharan Africa by 2020

Michael¹,

Singh²,

Mayala³

et al. 2018

Journal of Vascular Surgery: Venous and Lymphatic Disorders

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Calibration of Large Spatial Models: A Multistage, Multiobjective Optimization Technique

Cited by 5 publications

References 31 publications

Modelling forest recolonization by an epiphytic lichen using a landscape genetic approach

Modelling forest recolonization by an epiphytic lichen using a landscape genetic approach

Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020

Continental-Scale, Data-Driven Predictive Assessment of Eliminating the Vector-Borne Disease, Lymphatic Filariasis, in Sub-Saharan Africa by 2020

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