An application of the theory of probabilities to the study of a priori pathometry. Part I. Proceedings of the Royal Society A: Mathematical.
Working with Dynamic Crop Models
Phys Eng Sci. CrossRef Google Scholar. Generalized linear models, second edition. Google Scholar. Infectious diseases of humans: dynamics and control. One of the key, must have references in the field. Lays out framework to think about dynamics of infectious diseases and presents basic theory and key results, with a focus on designing control and eradication strategies.
Keeling MJ, Rohani P. Modeling infectious diseases in humans and animals. A model-based estimate of the mean incubation period for AIDS in homosexual men. Transmission dynamics of HIV infection. Some problems in the prediction of future numbers of cases of the acquired immunodeficiency syndrome in the UK. Pandemic potential of a strain of influenza A H1N1 : early findings.
Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility. Lancet Infect Dis. Analysis of MERS outbreak that incorporated a wide array of different data sources including human mobility, phylogenetic and case data into mechanistic models to allow inference on key transmission parameters. Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections.
N Engl J Med. Hospital outbreak of Middle East respiratory syndrome coronavirus. Reconstruction of 60 years of chikungunya epidemiology in the Philippines demonstrates episodic and focal transmission. J Infect Dis. Estimating dengue transmission intensity from sero-prevalence surveys in multiple countries. Revisiting Rayong: shifting seroprofiles of dengue in Thailand and their implications for transmission and control. Am J Epidemiol. Assessment of the global measles mortality reduction goal: results from a model of surveillance data. Uses mechanistic models to make key inferences about the global burden of disease in the presence of imperfect data.
Valle D, Clark J. Improving the modeling of disease data from the government surveillance system: a case study on malaria in the Brazilian Amazon. PLoS Comput Biol. Role of social networks in shaping disease transmission during a community outbreak of H1N1 pandemic influenza. A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data.
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Stat Med. Inferring influenza dynamics and control in households. Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J R Soc Interface. Sequential Monte Carlo without likelihoods. Phylodynamic inference and model assessment with approximate Bayesian computation: influenza as a case study [Internet].
Dynamic modeling of mineral contents in greenhouse tomato crop
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Vaccine [Internet]. Example of how sequential Monte Carlo methods can be used to parametrize complex transmission models. Measuring the performance of vaccination programs using cross-sectional surveys: a likelihood framework and retrospective analysis [Internet]. Wallinga J, editor.
Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: a simple seasonal adjustment method performs best. Trop Med Int Health. Chaves LF, Pascual M. Climate cycles and forecasts of cutaneous leishmaniasis, a nonstationary vector-borne disease. PLoS Med. Interactions between serotypes of dengue highlight epidemiological impact of cross-immunity. Generalized reproduction numbers and the prediction of patterns in waterborne disease.
Book Working With Dynamic Crop Models: Evaluation, Analysis, Parameterization, And Applications
Socially structured human movement shapes dengue transmission despite the diffusive effect of mosquito dispersal. Impact of human mobility on the emergence of dengue epidemics in Pakistan. Forecasting influenza epidemics in Hong Kong. Estimating the future number of cases in the Ebola epidemic—Liberia and Sierra Leone, MMWR Suppl.
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Real-time influenza forecasts during the season. Nat Commun. Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda. PLoS One. Ensemble modeling of the likely public health impact of a pre-erythrocytic malaria vaccine. Epidemic Prediction Initiative [Internet]. Dengue Forecasting. Yasmin S. Ebola infections fewer than predicted by disease models [Internet].
Scientific American. Dumbill E. Planning for big data. Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data. Erlander S, Stewart NF. The gravity model in transportation analysis: theory and extensions. Utretch: VSP; Commentary: containing the Ebola outbreak—the potential and challenge of mobile network data. Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. The impact of biases in mobile phone ownership on estimates of human mobility. The quality control of long-term climatological data using objective data analysis.
J Appl Meteorol. High-resolution gridded population datasets for Latin America and the Caribbean in , , and Sci Data. A world malaria map: Plasmodium falciparum endemicity in The malaria Atlas Project: developing global maps of malaria risk. Global distribution maps of the leishmaniases. Elife [Internet]. Using global maps to predict the risk of dengue in Europe. Acta Trop. Mapping the zoonotic niche of Marburg virus disease in Africa. Algeria Infect Genet Evol.
The global distribution of the arbovirus vectors Aedes aegypti and Ae. Kraft, H. Frede, and L. Houska tobias. Computer simulations are widely used to support curve showed a large equifinality. We attribute this slightly decision making and planning in the agriculture sector. On poorer model performance to missing leaf senescence, which the one hand, many plant growth models use simplified hy- is currently not implemented in PMF.
The most constrained drological processes and structures — for example, by the use parameters for the plant growth model were the radiation-use of a small number of soil layers or by the application of sim- efficiency and the base temperature. Cross validation helped ple water flow approaches. On the other hand, in many hy- to identify deficits in the model structure, pointing out the drological models plant growth processes are poorly repre- need for including agricultural management options in the sented.
Hence, fully coupled models with a high degree of coupled model. We coupled two of such high-process-oriented indepen- dent models and calibrated both models simultaneously. However modelling comes along with diation use efficiency, degree days, water shortage and dy- limitations. Different models can lead to deviating results namic root biomass allocation. The Monte Carlo-based generalized likelihood uncertainty Such effects are represented by model uncertainty.
Further- estimation GLUE method was applied to parameterize the more, the selection of input parameters can change the re- coupled model and to investigate the related uncertainty of sults and also increase uncertainty. This effect is commonly model predictions. Overall, 19 model parameters 4 for CMF known as parameter uncertainty.
The importance of a comprehen- winter wheat Triticum aestivum L.
Field observations for model evaluation included models applied in the field of decision making has also been soil water content and the dry matter of roots, storages, stems highlighted by Kersebaum et al. The shape parameter of the retention curve n was Most current plant growth models integrate plant growth highly constrained, whereas other parameters of the retention and hydrological processes tightly, leading to very complex models. Therefore, the calibration of such models is often Published by Copernicus Publications on behalf of the European Geosciences Union.
Houska et al. In a number of studies e. Pathak et al. He et al. However, model. They used modifications of the variance of model in such a setup, feedbacks between biomass production and errors and mean squared error as likelihood functions. Mo hydrology are not considered Pauwels et al.
Alterna- and Beven applied the method with the index of tively, the past years have seen modular model developments agreement as a likelihood function for calibration of a soil— and the promotion of comprehensive model-coupling strate- vegetation—atmosphere transfer model. Kraft et al. Hence, the objectives tual modelling experiments Multsch et al. Instead of calibrating single models step by step, we favour — In-depth analysis of the coupled model setup through the use of a Monte Carlo algorithm to screen the hyper- a GLUE analysis to investigate the sensitivity of plant dimensional parameter space for behavioural model runs of growth and hydrological model parameters and to de- the entire coupled model and apply the GLUE general- rive a range of behavioural model runs.
This behaviour is known winter wheat. The former describes an acceptable model we used a set of three likelihood functions model efficiency, application, allowing for some degree of error in simulat- bias and coefficient of determination. Subsequently, we will ing a target value defined in an a priori threshold crite- distinguish between i forcing data e. The latter describes parameter sets which return un- servations , ii input data e. A further distinction is made between constrained and un- constrained parameters Christiaens and Feyen, The 2 Materials and methods more sensitive a model parameter for predicting a given tar- get value is, the more constrained it becomes in the remaining 2.
The level of improvement of the model by the GLUE ap- 2. However, the A plot-scale hydrological model for the unsaturated zone was choice of the likelihood function itself also has a strong in- built by using the catchment modelling framework CMF fluence on the results, which has also been reported by He Kraft, CMF is a computer program used for setting et al. A programming library the likelihood function to ensure statistical validity. A num- facilitates the design of water transport models between soil ber of likelihood functions have been applied: for example, layers in up to three dimensions.
It allows the development the inverse error variance with a shaping factor Beven and of detailed mechanistic models as well as lumped large-scale Binley, , the Nash and Sutcliffe model efficiency Freer linear storage-based models. A model in CMF functions as et al.
It works as an extension to , model bias and coefficient of determination. Python and can easily be coupled with other models. A number of studies applied the GLUE method to achieve The specific realization of CMF was done with a one- a better understanding of plant growth models and their dimensional setup. Water fluxes were simulated with the parameters. For example, Wang et al. We simulated the soil moisture with the Biogeosciences, 11, —, www. In this concept the actual water uptake ties van Genuchten, for 50 soil layers with a uniform is distributed over the rooting zone and occurs as a sink term thickness of 0.
The ksat parameter was used to simulate in the Richards equation. The allocation of water uptake in the saturated conductivity.
The porosity parameter is defined PMF depends on the relation of the root biomass in each soil by pore volume per soil volume, while alpha and n as known layer and the total root biomass in the rooting zone. Influ- van Genuchten parameters. The interaction of the lowest soil ences of water are incorporated according to the soil mois- layer with the groundwater is modelled as a Neumann bound- ture conditions with a crop-specific response function.
The ary condition. To initiate the water content of CMF we used response function is related to the soil matrix potential and meteorological data for the year and calibrated it for the the water content. According to Feddes et al. The crop-specific response function includes a dimensionless water stress index.
The resulting actual wa- As a plant growth model, we used the plant growth mod- ter uptake from each layer is the product of the stress index elling framework PMF , developed by Multsch et al. PMF is a dynamic and integrative tool for setting up individ- The root growth takes place during sowing and the devel- ual plant models. In general, PMF consists of four core ele- opment stage anthesis.
During this part of the growing sea- ments: i plant model, ii process library, iii plant building son, a fraction of the total biomass is allocated to the root. The basic idea of PMF is to di- Root growth includes the calculation of the total underground vide the plant into its parts — root, shoot, stem, leaf and stor- biomass as a fraction from the total plant biomass, the cal- age — which interact during the growth process. This struc- culated vertical growth elongation and the distribution of ture builds up the plant model. A process library contains the root biomass over the rooting zone branching.
The last mathematical formulations of biophysical processes, such as group of parameters are the basal crop coefficients kcbini , biomass accumulation, water uptake and development. The kcbmid and kcbend , which are used to assess plant transpira- user can connect the plant model with a set of biophysical tion from potential evapotranspiration Table 1. The transpi- processes by using the plant building set. The plant parame- ration and evaporation in PMF are simulated according to the ters are taken from the crop database.
The biomass accumulation is affected by the radiation use The potential PET is adjusted with crop-specific coefficients efficiency RUE. The higher the RUE, the higher is the to account for different growing stages. This crop coefficient biomass accumulation. RUE is used to calculate the biomass is low at the end of the growing period of winter wheat to growth with the biomass radiation-use-efficiency concept account for a lower transpiration as a consequence of leaf Monteith and Moss, The mathematical solution of senescence. For our case study, because of the lack of avail- the radiation use efficiency in PMF is based on Acevedo able phenological data, we did not activate the vernalization et al.
The photosynthetically active absorbed radia- module. The important process of vernalization will be tested tion is calculated by solar radiation and its intercepted frac- in future when phenological data are available. Plant parti- tion. The simulation of biomass accumulation from photo- tioning is done according to biomass fractions of each plant synthetic active radiation is performed with the canopy ex- organ according to a table given by De Vries The tinction coefficient k , where the radiation is directly trans- fraction of biomass which is allocated to each organ depends formed into dry matter or assimilated CO2.
An overview of on the growing stage. Root growth and stem elongation oc- this concept is given by Sinclair and Horie The min- curs until anthesis. After that stage, dry matter is only al- imum temperature for plant development is defined by the located to the above-ground biomass.
At the very end of the base temperature tbase. This acts as a threshold tempera- growing season the storage organs are filled. All PMF param- ture above which development occurs. Each plant develop- eters are chosen on the basis of their influence on roots, stems ment step is defined by a temperature sum. If the tempera- and leaves or storages dry-matter outputs, based on one- ture sum is reached, then the developing process begins. If parameter-at-a-time sensitivity analyses and expert knowl- this parameter is too high, then the plant starts its growing edge.
For simplicity, the parameter tbase is independent from further environmental influences. However, the defi- dough stage and maturity. Root elongation determines the nition of the functional boundary between the models is a daily root growth rate. Root water uptake in PMF equals a major issue in model coupling — that is, which processes macroscopic approach type 2 Feddes et al. It necessary to be ensure that www. PAR stands for photosyn- thetically active radiation.
Minimal to maximal input is the range for the GLUE analysis, while the output is the constrained range of the observed behavioural parameter sets cf. Customer satisfaction our priority. Book Description Condition: Brand New. Customer Satisfaction guaranteed!!. Excellent Customer Service.
Book Description Academic Press Inc , Book Description Elsevier. Book Description Academic Press, Seller Inventory M Condition: NEW. For all enquiries, please contact Herb Tandree Philosophy Books directly - customer service is our primary goal. Jones ; Francois Brun. Publisher: Academic Press , This specific ISBN edition is currently not available. View all copies of this ISBN edition:. Synopsis About this title This second edition of Working with Dynamic Crop Models is meant for self-learning by researchers or for use in graduate level courses devoted to methods for working with dynamic models in crop, agricultural, and related sciences.
About the Author : Daniel Wallach focuses on the application of statistical methods of dynamic systems, specifically on agronomy models. Review : "This edition adds chapters on the basics of dynamic system models, statistics, and simulation; examples of how the methods can be applied to real-world problems; advanced methods for parameter estimation, model evaluation, and data assimilation; a new chapter on how the topics fit together in a complete modeling project; and information on how to use the R language and platform. Buy New Learn more about this copy.