Systems approaches to animal disease surveillance and resource allocation: methodological frameworks for behavioral analysis

While demands for animal disease surveillance systems are growing, there has been little applied research that has examined the interactions between resource allocation, cost-effectiveness, and behavioral considerations of actors throughout the livestock supply chain in a surveillance system context. These interactions are important as feedbacks between surveillance decisions and disease evolution may be modulated by their contextual drivers, influencing the cost-effectiveness of a given surveillance system. This paper identifies a number of key behavioral aspects involved in animal health surveillance systems and reviews some novel methodologies for their analysis. A generic framework for analysis is discussed, with exemplar results provided to demonstrate the utility of such an approach in guiding better disease control and surveillance decisions

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

Ключевые тенденции и перспективы устойчивого развития системы городского расселения в Российской Федерации

The subject of the study is the problem of the spatial distribution of the population in Russia and its regions. The relevance of research is determined by the key trends in the processes of distribution and movement of human capital in the context of cities. The paper aims to analyze the key trends and prospects for the development of the Russian settlement system at the federal, regional, and local levels. The research methods include a critical analysis of approaches to territorial settlement optimization, the modern system of urban settlement in Russia and its regions based on the use of statistical data of the Federal State Statistics Service for 2011-2019. The authors carried out a comparative analysis of the studied processes by countries using the Zipf method, as well as by Russian regions using the the Lorenz coefficient. It has been determined that the group of regions with an increase in the level of differentiation of urban settlement is characterized by a high level of depopulation of small and medium-sized cities with a contraction and concentration of the population in the largest city of the region, which creates additional risks for the sustainable development of the territory. A decrease in the level of differentiation of urban settlement is observed in regions where the share of the population of both small and medium-sized cities (but at a slower pace) and large ones is decreasing. The scientific novelty of the study lies in the development of an approach to improving the settlement system in Russia based on the application of the Lorenz coefficient and modelling methods. The authors conclude that an uneven system of settlement has developed in Russia, creating prerequisites for the emergence of new imbalances and threats to the complex sustainable development of the country’s territory. In this regard, it is advisable to develop an appropriate document in the field of state policy at the federal level or clarify similar issues within the framework of existing documents, as well as to increase the scientific validity of the measures taken using formalized methods of forecasting and planning. A promising direction in this area is the development of an agent-based model that allows increasing the efficiency of the distribution of financial resources for the development of social infrastructure. The results of the study justify the expediency of reallocating financial resources of the budget to ensure state policy in the field of development of the settlement system in the country.Предметом исследования является проблема пространственного распределения населения на территории России и ее регионов. Актуальность исследования определяется ключевыми тенденциями в процессах распределения и перемещения человеческого капитала в разрезе городов. Целью исследования является анализ ключевых тенденций и перспектив развития системы расселения России на федеральном, региональном и местном уровнях. Методы исследования включают критический анализ подходов к оптимизации территориального расселения, современной системы городского расселения в России и ее регионах на основе использования статистических данных Росстата за 2011-2019 гг. Проведен сравнительный анализ исследуемых процессов в разрезе стран с использованием метода Ципфа, а также в разрезе регионов России с применением коэффициента Лоренца. Определено, что группа регионов, в которых наблюдается рост дифференциации городов по численности населения, характеризуется высоким уровнем депопуляции малых и средних городов при стягивании и концентрации населения в самом крупном городе субъекта, что создает дополнительные риски для устойчивого развития территории. Снижение уровня дифференциации городов наблюдается в субъектах, где уменьшается доля населения как малых и средних городов (однако более медленными темпами), так и крупных. Научная новизна исследования заключается в разработке подхода к совершенствованию системы расселения в России на основе применения коэффициента Лоренца и методов моделирования. Сделан вывод о том, что в России сложилась неравномерная система расселения, создающая предпосылки для возникновения новых диспропорций и угроз комплексному устойчивому развитию территории страны. В связи этим целесообразным представляется разработка на федеральном уровне соответствующего документа в области государственной политики или уточнение подобных вопросов в рамках существующих документов, а также повышение научной обоснованности принимаемых мер за счет применения формализованных методов прогнозирования и планирования. Перспективным направлением в данной сфере является разработка агент-ориентированной модели, позволяющей повысить эффективность распределения финансовых средств на развитие социальной инфраструктуры. Полученные результаты позволят обосновывать целесообразность перераспределения финансовых ресурсов бюджета для обеспечения государственной политики в сфере развития системы расселения в стране

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

Rôle de la perturbation par le vent dans les forêts tropicales via un modèle dynamique de végétation et l'observation satellitaire

Les perturbations naturelles ont une influence importante sur la structure, la composition et le fonctionnement des forêts tropicales et un rôle dans la régulation des cycles biogéochimiques. La fréquence et l'intensité des perturbations naturelles sont modifiés par les changements climatiques : une meilleure connaissance de leur mécanisme d'action est nécessaire pour prédire les conséquences de cette modification. La modélisation permet d'évaluer le rôle de chacun des processus écologiques et leur lien avec les facteurs environnementaux. Les outils de la télédétection nous informent sur la structure et le fonctionnement des forêts à large échelle, et peuvent être utiles à la calibration et la validation des modèles de végétation. Dans cette thèse, j'ai employé ces deux approches pour examiner comment les forêts tropicales sont façonnées par les perturbations naturelles, notamment le vent, qui est un facteur majeur de perturbation dans de nombreuses régions tropicales. Dans un premier temps, j'ai évalué la transférabilité d'un modèle individu-centré et spatialement explicite via un test de sensibilité et la calibration des paramètres globaux. Le modèle prédit correctement la structure de la forêt sur deux sites contrastés, et sa réponse est cohérente avec les variations du forçage climatique. La calibration d'un petit nombre de paramètres clés a été nécessaire, dont notamment celui qui contrôle la mortalité. Pour étudier la sensibilité du modèle à la mortalité, j'ai mis en œuvre un module de dégâts de vents fondé sur les principes biophysiques et couplé avec la vitesse de vent, afin de modéliser les réponses de la forêt aux évènements de vent extrême. Avec l'augmentation du niveau de perturbation, la hauteur de la canopée diminue de manière constante mais la biomasse montre une réponse non-linéaire. L'intensité du vent a un fort impact sur la hauteur de la canopée et la biomasse, mais pas la fréquence des évènements de vent extrême. Finalement, j'ai testé si les données radar des satellites Sentinel-1 pourraient servir à détecter les trouées dues aux perturbations naturelles en Guyane française. Les données Sentinel-1 détectent plus de trouées naturelles au-dessus de 0.2 ha que les données satellitaires optiques, et elles présentent un patron spatial cohérent avec les images optiques. Le niveau de perturbation ne varie pas en fonction de l'altitude. Nous avons trouvé plus de perturbations pendant les saisons sèches, ce qui pourrait être dû à la réponse tardive des précipitations plutôt qu'à la réponse directe de la sècheresse. En conclusion, cette thèse démontre que l'intégration entre la modélisation et la télédétection éclairent les effets des perturbations naturelles sur les forêts tropicales. Les résultats qui en découlent peuvent servir à étudier d'autres types de perturbations et leurs interactions sur une large échelle.Natural disturbances have an important influence on the structure, composition and functioning of tropical forests and a role in the regulation of biogeochemical cycles. The frequency and intensity of natural disturbances are modified by climate change: a better knowledge of their mechanism of action is necessary to predict the consequences of this modification. Modeling allows us to evaluate the role of each of the ecological processes and their link with environmental factors. Remote sensing tools inform us about the structure and functioning of forests at large scales, and can be useful for the calibration and validation of vegetation models. In this thesis, I employed both approaches to examine how tropical forests are shaped by natural disturbances, particularly wind, which is a major disturbance factor in many tropical regions. First, I evaluated the transferability of a spatially explicit, individual-based model via sensitivity testing and calibration of global parameters. The model correctly predicts forest structure at two contrasting sites, and its response is consistent with variations in climate forcing. Calibration of a small number of key parameters was required, including the parameter controlling mortality and crown allometry. To investigate the sensitivity of the model to mortality, I implemented a wind damage module based on biophysical principles and coupled with wind speed to model forest responses to extreme wind events. With increasing disturbance level, canopy height decreased steadily but biomass showed a non-linear response. Wind intensity had a strong impact on canopy height and biomass, but not the frequency of extreme wind events. Finally, I tested whether radar data from Sentinel-1 satellites could be used to detect gaps due to natural disturbances in French Guiana. The Sentinel-1 data detected more natural gaps above 0.2 ha than the optical satellite data, and they showed a spatial pattern consistent with the optical images. The level of disturbance did not vary with altitude. We found more disturbance during dry seasons, which could be due to the delayed response of precipitation rather than the direct response of drought. In conclusion, this thesis demonstrates that the integration between modeling and remote sensing sheds light on the effects of natural disturbances on tropical forests. The resulting results can be used to study other types of disturbances and their interactions on a large scale

Implementation of an Agent-Based Model for Devil Facial Tumor Disease in Tasmanian Devils, and Evaluation of Interventions

This thesis presents a geographical agent-based model to investigate different interventions that may be used to combat the spread of devil facial tumour disease (DFTD). DFTD is a clonally transmissible cancer that spreads as an allograft through bite wounds between Tasmanian devils [15]. The population of Tasmanian devils has been reduced by up to 90% since the first documented case of DFTD in 1996, and continued spread of DFTD threatens the survival of the species. The agent-based model presented here uses geographic data to simulate the devil maturation and mating, both spread and progress of DFTD, but also external pressures such as road kill, rodenticide, dog attacks, and generally lower survival in urban settings. Capturing these external pressures addresses a critical gap in current research which can highlight the importance of necessary interventions to preserve the species. Multiple interventions were investigated, including translocation of devils from a disease-free external population, translocation of devils from within Tasmania, use of an injection vaccine, and use of an oral bait vaccine. The injection vaccine increased the devil days lived (DDL) from the baseline of 6.81x10^8 to 7.76x10^8 and decreased the mean daily incidence of DFTD from the baseline of 52.43 to 39.27. Similarly, the oral bait vaccine intervention increased the DDL from 6.81x10^8 to 8.34x10^8, and decreased the mean daily incidence rate from 52.43 to 24.91, using the most aggressive distribution campaign. This oral bait vaccine campaign resulted in eradication of DFTD in the model. As the injection vaccine assumes an intensive trapping effort across the island, which can be very resource intensive, the more promising intervention is the oral bait vaccine due to its significantly lower resource investment and potential for disease eradication

Tree mortality submodels drive simulated long-term forest dynamics: assessing 15 models from the stand to global scale

Models are pivotal for assessing future forest dynamics under the impacts of changing climate and management practices, incorporating representations of tree growth, mortality, and regeneration. Quantitative studies on the importance of mortality submodels are scarce. We evaluated 15 dynamic vegetation models (DVMs) regarding their sensitivity to different formulations of tree mortality under different degrees of climate change. The set of models comprised eight DVMs at the stand scale, three at the landscape scale, and four typically applied at the continental to global scale. Some incorporate empirically derived mortality models, and others are based on experimental data, whereas still others are based on theoretical reasoning. Each DVM was run with at least two alternative mortality submodels. Model behavior was evaluated against empirical time series data, and then, the models were subjected to different scenarios of climate change. Most DVMs matched empirical data quite well, irrespective of the mortality submodel that was used. However, mortality submodels that performed in a very similar manner against past data often led to sharply different trajectories of forest dynamics under future climate change. Most DVMs featured high sensitivity to the mortality submodel, with deviations of basal area and stem numbers on the order of 10–40% per century under current climate and 20–170% under climate change. The sensitivity of a given DVM to scenarios of climate change, however, was typically lower by a factor of two to three. We conclude that (1) mortality is one of the most uncertain processes when it comes to assessing forest response to climate change, and (2) more data and a better process understanding of tree mortality are needed to improve the robustness of simulated future forest dynamics. Our study highlights that comparing several alternative mortality formulations in DVMs provides valuable insights into the effects of process uncertainties on simulated future forest dynamics

Foundations of GAM Research. Methodological Guidelines for Designing and Conducting Research that Combines Games and Agent-based Models

This thesis presents the development of the games and agent-based model methodology and provides methodological guidelines for using GAM research, i.e., combining games and agent-based models in research. GAM research is rooted in complexity sciences and transdisciplinary research, offering valuable insights into complex, adaptable systems. GAM research has particular relevance in decision-making and complex-system management, thus fostering collaboration among scientists and non-academics from various disciplines. It is an engaging platform for data collection and stakeholder processes, thus enriching causal explanations. It should be noted that GAM research has the potential to overcome the limitations of traditional methods by facilitating hypothesis testing with simulation-based observations of human behaviours. Investigations in GAM research can change how social science addresses pressing global challenges. The immersive nature of games combined with agent-based models offers an innovative approach that attracts diverse participants, making it a promising tool for science that reaches beyond the classic academic spheres. As a comprehensive handbook, this thesis offers researchers inspiration and references for conducting GAM research across diverse application domains. This thesis presents an assessment of the state of research that combines games and agent-based models and proposes a structured approach to making progress in this field. Addressing the lack of a standardised methodology, this thesis is aimed at improving research practices, transparency, and replicability . Practical advice is provided for guiding researchers through designing and conducting GAM research, thus promoting rigorous and comprehensive studies