Environmetrics最新文献

{"title":"The Effect of the North Atlantic Oscillation on Monthly Precipitation in Selected European Locations: A Non-Linear Time Series Approach","authors":"Changli He,&nbsp;Jian Kang,&nbsp;Annastiina Silvennoinen,&nbsp;Timo Teräsvirta","doi":"10.1002/env.2896","DOIUrl":"https://doi.org/10.1002/env.2896","url":null,"abstract":"<p>In this article, the relationship between the monthly precipitation in 30 European cities and towns, and two Algerian ones, and the North Atlantic Oscillation (NAO) index is characterized using the Vector Seasonal Shifting Mean and Covariance Autoregressive model, extended to contain exogenous variables. The results, based on monthly time series from 1851 up until 2020, include shifting monthly means for the rainfall series and the estimated coefficients of the exogenous NAO variable. They suggest that in the north and the west, the amount of rain in the boreal winter months has increased or stayed the same during the observation period, whereas in the Mediterranean area, there have been systematic decreases. Results on the North Atlantic Oscillation indicate that the NAO has its strongest effect on precipitation during the winter months. The (negative) effect is particularly strong in Western Europe, Lisbon, and the Mediterranean rim. In contrast, the effect in northern locations is positive for the winter months. The constancy of error variances and correlations is tested and, if rejected, the time-varying alternative is estimated. A spatial relationship between the error correlations and the distance between the corresponding pairs of cities is estimated.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2896","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discussion on Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models","authors":"Ansgar Steland","doi":"10.1002/env.70003","DOIUrl":"https://doi.org/10.1002/env.70003","url":null,"abstract":"&lt;p&gt;I congratulate the authors for their interesting and insightful paper. Their findings contribute to the ongoing discussion of the pros and cons of machine learning methods and statistical approaches in environmetrics. In my discussion, I want to address some issues in the design of the comparison study and the interpretation of the results, and add some general thoughts. Moreover, I report about my own analyses of the Indiana citizen science data set used by the authors. Specifically, I developed a non-linear ARIMA regression model with improved heteroscedasticity-consistent uncertainty estimation, which turns out to substantially outperform the best method of (Bonas et al. &lt;span&gt;2025&lt;/span&gt;). I also applied a couple of machine learning approaches not examined there, which I regard as quite accessible general purpose machine learning methods. One of those methods is recommended by the early 2025 state-of-the-art AI large language models. I report about the interesting results in the last section.&lt;/p&gt;&lt;p&gt;The authors argue that for non-linear and non-stationary processes, machine learning methods are typically superior due to their non-parametric structure. But here one should recall that the classical but still popular class of feedforward artificial neural networks trained by backpropagation is non-linear least squares regression with a certain non-linear regression function known up to a parameter vector. In its entirety, this was mainly recognized and elaborated by the econometrician Halbert White, see White (&lt;span&gt;1992&lt;/span&gt;). And, of course, there are many methods classified as traditional statistical approaches which are non-parametric as well. The differences are more with respect to the dimensionality and sample size, and how the methods deal with it. Many machine learning methods such as artificial neural networks fit high-dimensional overparametrized parametric models, whereas statistical methods usually work with specifically chosen stochastic models aiming at parsimony. Such ML methods process high-dimensional inputs as given, and the purpose of the first stage of a model is to learn features from the input data, sometimes being agnostic with respect to the type and meaning of the input variables. The guiding principle is that one only fixes the basic topology (e.g., fully-connected layers or convolutional layers), and the model can extract optimal features provided the learning sample is large enough. This is in contrast to statistical approaches, which tend to design a model for specific types of inputs and deal with high dimensionality by suitable preprocessing steps, human-controlled feature generation and/or specific assumptions and related estimation methods, especially sparse models and sparse estimation, which combine estimation and variable selection. A further distinction is that statistical models and modeling using stochastic processes often impose a certain structure, which is derived from domain knowledge (e.g., physic","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semiparametric Copula-Based Confidence Intervals on Level Curves for the Evaluation of the Risk Level Associated to Bivariate Events","authors":"Albert Folcher,&nbsp;Jean-François Quessy","doi":"10.1002/env.70005","DOIUrl":"https://doi.org/10.1002/env.70005","url":null,"abstract":"&lt;p&gt;If &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;X&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;Y&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left(X,Yright) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; is a random pair with distribution function &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;X&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;Y&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;x&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;y&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mi&gt;ℙ&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;X&lt;/mi&gt;\u0000 &lt;mo&gt;≤&lt;/mo&gt;\u0000 &lt;mi&gt;x&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;Y&lt;/mi&gt;\u0000 &lt;mo&gt;≤&lt;/mo&gt;\u0000 &lt;mi&gt;y&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {F}_{X,Y}left(x,yright)=mathbb{P}left(Xle x,Yle yright) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, one can define the level curve of probability &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ p $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; as the values of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;x&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;y&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;mo&gt;∈&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℝ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left(x,yright)in {mathbb{R}}^2 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; such that &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;X&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;Y&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;x&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;y&lt;/mi&gt;\u0000 ","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”","authors":"Paolo Maranzano,&nbsp;Paul A. Parker","doi":"10.1002/env.70001","DOIUrl":"https://doi.org/10.1002/env.70001","url":null,"abstract":"<p>We contribute to the discussion of the insightful article “Assessing predictability of environmental time series with statistical and machine learning models” by Bonas et al. (2024), in which the authors commend their effort in comparing a wide range of methodologies for the challenging task of predicting environmental time series data. We focus our discussion on two topics of interest to us. First, we consider extensions of the explored methodologies that allow for heteroscedastic error terms. Second, we consider non-Gaussianity and fitting models on transformed data. For both of these points, we will make use of the authors' supplied code and data in order to extend their examples. Ultimately, we find that modeling of heteroscedasticity error terms has the potential to improve both point and interval estimates for these environmental time series. We also find that the use of transformations to handle non-Gaussianity can improve interval estimates.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stacking Weights and Model Space Selection in Frequentist Model Averaging for Benchmark Dose Estimation","authors":"Jens Riis Baalkilde,&nbsp;Niels Richard Hansen,&nbsp;Signe Marie Jensen","doi":"10.1002/env.70002","DOIUrl":"https://doi.org/10.1002/env.70002","url":null,"abstract":"<p>In dose-response modeling, several models can often yield satisfactory fits to the observed data. The current practice in risk assessment is to use model averaging, which is a way to combine multiple models in a weighted average. A key parameter in risk assessment is the benchmark dose, the dose resulting in a predefined abnormal change in response. Current practice when applying frequentist model averaging is to use weights based on the Akaike Information Criterion (AIC). This paper introduces stacking weights as an alternative for dose-response modeling and generalizes a Diversity Index from dichotomous to continuous responses for model space selection. Three simulation studies were conducted to evaluate the new methods. They showed that, in three realistic scenarios, recommended strategies generally performed well, with stacking weights outperforming AIC weights in several cases. Strategies involving model selection were less effective. However, in a challenging scenario, none of the methods performed well. Due to the promising results of stacking weights, they have been added to the R package “bmd.”</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”","authors":"Nathaniel K. Newlands,&nbsp;Vyacheslav Lyubchich","doi":"10.1002/env.70000","DOIUrl":"https://doi.org/10.1002/env.70000","url":null,"abstract":"<p>The relative merits of machine learning and statistical methods are discussed recently by Bonas et al. 2004, who raise important open questions for the statistical community regarding the value-added benefits of data science and the future role of environmental statistics. Specifically, they identify three major knowledge gaps where statistics is seen as crucial to strengthening inference in machine learning (ML): to provide an ML model-based framework amenable to explainability, to determine the best approach for quantifying uncertainty in relation to complex environmental dynamics, and to comprehensively identify ML's value-added benefits. We continue this discussion by exploring these general questions and sharing our perspective and insights from our modeling of marine and terrestrial ecosystem dynamics. We propose several lines of inquiry where environmental statisticians and data scientists could collaboratively advance predictive analytics.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does the Quality of Political Institutions Matter for the Effectiveness of Environmental Taxes? An Empirical Analysis on CO2 Emissions","authors":"Donatella Baiardi,&nbsp;Simona Scabrosetti","doi":"10.1002/env.2895","DOIUrl":"https://doi.org/10.1002/env.2895","url":null,"abstract":"<p>Focusing on a sample of 39 countries in the period 1996–2017, we analyze whether the relationship between environmental taxes and CO₂ emissions depends on the quality of political institutions. Our results show that an increase in the environmental tax revenue is related to a reduction in CO₂ emissions only in countries with more consolidated democratic institutions, higher civil society participation, and less corrupt governments. Moreover, the relationship between CO₂ emissions and revenue neutral shifts to different tax sources depends not only on the quality of political institutions, but also on the kind of externality the policymaker aims at correcting.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2895","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Clustering of Circular Time Series With Applications to Wind Data","authors":"Ángel López-Oriona,&nbsp;Ying Sun,&nbsp;Rosa María Crujeiras","doi":"10.1002/env.2902","DOIUrl":"https://doi.org/10.1002/env.2902","url":null,"abstract":"<p>In environmental science, practitioners often deal with data recorded sequentially along time, such as time series of wind direction or wind speed. In this context, clustering of time series is a useful tool to carry out exploratory analyses. While most of the proposals are focused on real-valued time series, very few works consider circular time series, despite the frequent appearance of these objects in many disciplines. In this manuscript, a dissimilarity for circular time series is introduced and used in combination with a soft clustering method. The metric relies on a measure of serial dependence considering circular arcs, thus taking advantage of the directional character inherent to the series range. The clustering approach is able to group together time series generated from similar stochastic processes. Some simulations show that the method exhibits a reasonable clustering effectiveness, outperforming alternative techniques in many contexts. Two interesting applications involving time series of wind direction in Saudi Arabia show the potential of the proposed approach.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2902","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatially Explicit Model to Disentangle Effects of Environment on Annual Fish Reproduction","authors":"Ilaria Pia,&nbsp;Elina Numminen,&nbsp;Lari Veneranta,&nbsp;Jarno Vanhatalo","doi":"10.1002/env.2894","DOIUrl":"https://doi.org/10.1002/env.2894","url":null,"abstract":"<p>Population growth models are essential tools for natural resources management and conservation since they provide understanding on factors affecting renewal of natural animal populations. However, we still do not properly understand how the processes underlying reproduction of natural animal populations are affected by the environment at the spatial scale at which reproduction actually happens. A particular challenge for analyzing these processes is that observations from different life cycle stages are often collected at different spatial scales, and there is a lack of statistical methods to link local and spatially aggregated information. We address this challenge by developing spatially explicit population growth models for annually reproducing fish. Our approach integrates mechanistic Ricker and Beverton–Holt population growth models with a zero-inflated species distribution model and utilizes the hierarchical Bayesian approach to estimate the model parameters from data with varying spatial support: local scale count data on offspring and environment, and areal data from commercial fisheries informing about a spawning stock size. We show, both theoretically and empirically, that our models are identifiable and have good inferential performance. As a proof of concept application, we used the proposed models to analyze the drivers of whitefish <i>Coregonus laveratus</i> (L.) s.l.) reproduction along the Finnish coast of the Gulf of Bothnia in the Baltic Sea. The results show that the proposed model provides novel understanding beyond what would be attainable with earlier methods. The distributions of the reproduction areas, spawner density, and maximum proliferation rate were strongly dependent on local environmental conditions, but the effects and the relative importance of the covariates varied between these processes. The proposed models can be extended to other systems and organisms and enable ecologists to extract a better understanding of processes driving animal reproduction.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Causal Inference With Mechanistic Ecological Models: Evaluating Targeted Culling on Chronic Wasting Disease Dynamics in Cervids","authors":"Juan Francisco Mandujano Reyes,&nbsp;Ting Fung Ma,&nbsp;Ian P. McGahan,&nbsp;Daniel J. Storm,&nbsp;Daniel P. Walsh,&nbsp;Jun Zhu","doi":"10.1002/env.2901","DOIUrl":"https://doi.org/10.1002/env.2901","url":null,"abstract":"<p>Spatiotemporal causal inference methods are needed to detect the effect of interventions on indirectly measured epidemiological outcomes that go beyond studying spatiotemporal correlations. Chronic wasting disease (CWD) causes neurological degeneration and eventual death to white-tailed deer (<i>Odocoileus virginianus</i>) in Wisconsin. Targeted culling involves removing deer after traditional hunting seasons in areas with high CWD prevalence. The evaluation of the causal effects of targeted culling in the spread and growth of CWD is an important unresolved research and CWD management question that can guide surveillance efforts. Reaction–diffusion partial differential equations (PDEs) can be used to mechanistically model the underlying spatiotemporal dynamics of wildlife diseases, like CWD, allowing researchers to make inference about unobserved epidemiological quantities. These models indirectly regress spatiotemporal covariates on diffusion and growth rates parameterizing such PDEs, obtaining associational conclusions. In this work we develop an innovative method to obtain causal estimators for the effect of targeted culling interventions on CWD epidemiological processes using an inverse-probability-of-treatment-weighted technique by means of marginal structural models embedded in the PDE fitting process. Additionally we establish a novel scheme for sensitivity analysis under unmeasured confounder for testing the hypothesis of a significant causal effect in the indirectly measured epidemiological outcomes. Our methods can be broadly used to study the impact of spatiotemporal interventions and treatment exposures in the epidemiological evolution of infectious diseases that can help to inform future efforts to mitigate public health implications and wildlife disease burden.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2901","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}