Bayesian prediction of Amblyomma variegatum dynamics using hidden process models

In silico evaluation of tick and TBD control practices requires a predictive dynamic framework that (1) approximates key density dependant / independent processes affecting tick numbers (2) captures the effects of external stochasticity (3) integrates prior knowledge (4) quantifies uncertainties in model choice, parameter estimates and predictions. Ecological time series are arguably the single most important data type for fitting and testing ecological forecasting models, yet, for want of a coherent methodological framework, fitting stochastic non-linear dynamic models to ecological time series whilst meeting these four requirements has long been an elusive goal. Two recently proposed algorithms, PMCMC [1] and SMC2 [2], could change this. These algorithms use particle filtering to fit non-linear stochastic hidden process models to time series and can, in theory, provide Bayesian inference for biological process models. But how much biological detail can be integrated into models under this paradigm and whether these algorithms really represent the state of the art in ecological forecasting remain open questions. We explore these questions by fitting population dynamic models containing various levels of biological detail to A. variegatum time series obtained from the 13 year Caribbean Amblyomma Program. Ecological interactions are inherently non-linear and even the simplest non-linear systems can exhibit complex dynamics [3]. Identifying key sources of non-linearity is a fundamental pre-requisite for ecological forecasting. We explore whether simple non-linear models can characterize A. variegatum population dynamics using modern Bayesian methods. The relative advantages and disadvantages of the new methods and their implications for control program evaluation are discussed. (Résumé d'auteur

FORECASTING OF ECOLOGICAL AND ECONOMIC CONSEQUENCES OF FLOODING OF COAL MINES

The main objective of the paper is to determination of the impact of groundwater near coal mines on the health of the local population in order to compensate for this impact on the cost of treatment for peopl

A Study on Green Economy Indicators and Modeling: Russian Context

This article aims to assess and forecast the dynamics of a regional green economy. The research relevance is determined by the need to develop theoretical and methodological basis of the green economy for the transition period and to identify criteria basis for assessing the state and regional level of it. The authors applied the modern methods, which allowed to model criteria considering data uncertainty and both static and dynamic criteria. The research process involved the methods of scientific analysis, comparison and synthesis, the theory of fuzzy sets, and fuzzy modeling. The main principles and methodology of the criteria evaluation for a regional green economy are proposed. The principal methodological approach in this research combines the current state and dynamics of the green economy in evaluating and forecasting the conditions of data uncertainty. The research results form a theoretical, methodological, and practical basis for assessing the current state and level of a regional green economy development, determining the effectiveness of environmental and economic programs, optimizing financial management, conducting environmental monitoring, and developing state plans.The research was funded by the grant of the Ministry of Education and Science of the Russian Federation to Perm National Research Polytechnic University # 26.6884.2017/8.9 "Sustainable development of urban areas and the improvement of the human environment.

Econometric modelling for short-term inflation forecasting in the EMU.

Inflation forecasts are in great demand by agents in financial markets and monetary authorities that also require frequent updates. In the case of the EMU, these can be done monthly using Harmonised Indices of Consumer Prices (HICP). Analysing the HICP it was detected in a previous paper that breaking down the HICP in a vector of n sectors so that each price index component corresponds to a group of relatively homogeneous markets, or in a vector of n countries, there are in both cases fewer than (n-1) cointegration relationships. It can then be said that the components of the index are not fully cointegrated in the sense that there is more than one common trend in the HICP vector. In such a case, one way to increase sample information about the HICP trend is to consider the n price components and approach disaggregated econometric modelling. The paper shows that the breakdown that joins both criteria by considering a price index for each large group of markets in each country improves EMU inflation forecasts and establishes a framework in which general and specific explanatory variables and non-linear structures can be introduced for further improvements. The paper shows that VEqCM of ten price indices " two sectors by five geographical areas " including three cointegration relationships, with a sector-block diagonal restriction, generates forecasts of the year-on-year inflation rate in the HICP such that their error variances are one third or one fifth of the forecast errors from an aggregate ARIMA model, depending whether the horizon is three or twelve months. This vector model also provides better forecasts than single-equation models or alternative vector models for the components. A successful formulation of the vector model requires the inclusion of dummy variables to take account of special events such as seasonality changes due to sales, the introduction of the euro, Greece becoming a member of the EMU, the introduction of ecological taxes, bad weather periods and others events altering the evolution of unprocessed food prices, etc. and the inclusion of international Brent prices in euros. With the breakdown used in the paper it is shown that a usual measure of core inflation is not a good predictor of total inflation, but the interest in core inflation could lie in the fact that its corresponding price index is constructed with price indices in which innovations are more persistent than those in the other consumer price indexes excluded from the core. The disaggregated forecasts presented in this paper are useful for policy-making because they tell us which sectors have the highest expected inflation rates and how persistent are the shocks affecting different sectors

Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

IntroductionCutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions.Methodology/principal findingsWe fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation.SignificanceThese outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets

Bayesian Learning and Predictability in a Stochastic Nonlinear Dynamical Model

Bayesian inference methods are applied within a Bayesian hierarchical modelling framework to the problems of joint state and parameter estimation, and of state forecasting. We explore and demonstrate the ideas in the context of a simple nonlinear marine biogeochemical model. A novel approach is proposed to the formulation of the stochastic process model, in which ecophysiological properties of plankton communities are represented by autoregressive stochastic processes. This approach captures the effects of changes in plankton communities over time, and it allows the incorporation of literature metadata on individual species into prior distributions for process model parameters. The approach is applied to a case study at Ocean Station Papa, using Particle Markov chain Monte Carlo computational techniques. The results suggest that, by drawing on objective prior information, it is possible to extract useful information about model state and a subset of parameters, and even to make useful long-term forecasts, based on sparse and noisy observations

Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the best predictive model.Comment: 19 pages, 1 table, 3 figure

PICES Press, Vol. 18, No. 2, Summer 2010

•The 2010 Inter-sessional Science Board Meeting: A Note from the Science Board Chairman (pp. 1-3) •2010 Symposium on “Effects of Climate Change on Fish and Fisheries” (pp. 4-11) •2009 Mechanism of North Pacific Low Frequency Variability Workshop (pp. 12-14) •The Fourth China-Japan-Korea GLOBEC/IMBER Symposium (pp. 15-17, 23) •2010 Sendai Ocean Acidification Workshop (pp. 18-19, 31) •2010 Sendai Coupled Climate-to-Fish-to-Fishers Models Workshop (pp. 20-21) •2010 Sendai Salmon Workshop on Climate Change (pp. 22-23) •2010 Sendai Zooplankton Workshop (pp. 24-25, 28) •2010 Sendai Workshop on “Networking across Global Marine Hotspots” (pp. 26-28) •The Ocean, Salmon, Ecology and Forecasting in 2010 (pp. 29, 44) •The State of the Northeast Pacific during the Winter of 2009/2010 (pp. 30-31) •The State of the Western North Pacific in the Second Half of 2009 (pp. 32-33) •The Bering Sea: Current Status and Recent Events (pp. 34-35, 39) •PICES Seafood Safety Project: Guatemala Training Program (pp. 36-39) •The Pacific Ocean Boundary Ecosystem and Climate Study (POBEX) (pp. 40-43) •PICES Calendar (p. 44

Nonindigenous Aquatic Species

Online resource center, maintained by U.S.G.S., provides information, data, links about exotic plants, invertebrates, vertebrates, diseases and parasites. Central repository contains accurate and spatially referenced biogeographic accounts of alien aquatic species. Search for species by state, drainage area, citation in texts; find fact sheets, maps showing occurrence in the U.S. Or, for each taxon, review list of exotic species, find scientific, common name, photo, status; link to facts and distribution map. Educational levels: General public, High school