Unifying Evolutionary and Network Dynamics

Many important real-world networks manifest "small-world" properties such as scale-free degree distributions, small diameters, and clustering. The most common model of growth for these networks is "preferential attachment", where nodes acquire new links with probability proportional to the number of links they already have. We show that preferential attachment is a special case of the process of molecular evolution. We present a new single-parameter model of network growth that unifies varieties of preferential attachment with the quasispecies equation (which models molecular evolution), and also with the Erdos-Renyi random graph model. We suggest some properties of evolutionary models that might be applied to the study of networks. We also derive the form of the degree distribution resulting from our algorithm, and we show through simulations that the process also models aspects of network growth. The unification allows mathematical machinery developed for evolutionary dynamics to be applied in the study of network dynamics, and vice versa.Comment: 11 pages, 12 figures, Accepted for publication in Physical Review

Centers, Peripheries, and Popularity: The Emergence of Norms in Simulated Networks of Linguistic Influence

We simulate the dynamics of diffusion and establishment of norms, variants adopted by the majority of agents, in a large social influence network with scale-free small-world properties. Diffusion is modeled as the probabilistic uptake of one of several competing variants by agents of unequal social standing. We find that novel variants diffuse following an S-curve and stabilize as norms when three conditions are simultaneously satisfied: the network comprises both extremely highly connected agents (centers) and very isolated members (peripheries), and agents pay proportionally more attention to better connected, more “popular”, neighbors. These findings shed light on little known dynamic properties of centers and peripheries in large influence networks. They show that centers, structural equivalents of highly influential leaders in empirical studies of social networks, are propagators of linguistic influence, while certain peripheral individuals, or loners, can act either as repositories of old forms or initiators of new variants depending on the current state of the rest of the population

IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery

Irrigation mapping plays a crucial role in effective water management, essential for preserving both water quality and quantity, and is key to mitigating the global issue of water scarcity. The complexity of agricultural fields, adorned with diverse irrigation practices, especially when multiple systems coexist in close quarters, poses a unique challenge. This complexity is further compounded by the nature of Landsat's remote sensing data, where each pixel is rich with densely packed information, complicating the task of accurate irrigation mapping. In this study, we introduce an innovative approach that employs a progressive training method, which strategically increases patch sizes throughout the training process, utilizing datasets from Landsat 5 and 7, labeled with the WRLU dataset for precise labeling. This initial focus allows the model to capture detailed features, progressively shifting to broader, more general features as the patch size enlarges. Remarkably, our method enhances the performance of existing state-of-the-art models by approximately 20%. Furthermore, our analysis delves into the significance of incorporating various spectral bands into the model, assessing their impact on performance. The findings reveal that additional bands are instrumental in enabling the model to discern finer details more effectively. This work sets a new standard for leveraging remote sensing imagery in irrigation mapping.Comment: Full version of the paper will be appearing in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 202

Combining participatory influenza surveillance with modeling and forecasting

Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Objectives: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. Methods: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), InfluenzaNet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using InfluenzaNet and Flu Near You). Results: WISDM based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. InfluenzaNet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities; and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. Conclusions: While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long term forecasting of Influenza activity in data poor parts of the world

A Framework for Modeling Human Behavior in Large-scale Agent-based Epidemic Simulations

Acknowledgements We thank Cuebiq; mobility data is provided by Cuebiq, a location intelligence and measurement platform. Through its Data for Good program, Cuebiq provides access to aggregated mobility data for academic research and humanitarian initiatives. This first-party data is collected from anonymized users who have opted-in to provide access to their location data anonymously, through a GDPR and CCPA compliant framework. To further preserve privacy, portions of the data are aggregated to the census-block group level. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.Peer reviewedPublisher PD

Simulation-Assisted Optimization for Large-Scale Evacuation Planning with Congestion-Dependent Delays

Evacuation planning is a crucial part of disaster management. However, joint optimization of its two essential components, routing and scheduling, with objectives such as minimizing average evacuation time or evacuation completion time, is a computationally hard problem. To approach it, we present MIP-LNS, a scalable optimization method that utilizes heuristic search with mathematical optimization and can optimize a variety of objective functions. We also present the method MIP-LNS-SIM, where we combine agent-based simulation with MIP-LNS to estimate delays due to congestion, as well as, find optimized plans considering such delays. We use Harris County in Houston, Texas, as our study area. We show that, within a given time limit, MIP-LNS finds better solutions than existing methods in terms of three different metrics. However, when congestion dependent delay is considered, MIP-LNS-SIM outperforms MIP-LNS in multiple performance metrics. In addition, MIP-LNS-SIM has a significantly lower percent error in estimated evacuation completion time compared to MIP-LNS

Using Agent-Based Simulation to Investigate Behavioral Interventions in a Pandemic Simulating Behavioral Interventions in a Pandemic

Simulation is a useful tool for evaluating behavioral interventions when the adoption rate among a population is uncertain. Individual agent models are often prohibitively expensive, but, unlike stochastic models, allow studying compliance heterogeneity. In this paper we demonstrate the feasibility of evaluating behavioral intervention policies using large-scale data-driven agent-based simulations. We explain how the simulation is calibrated with respect to real-world data, and demonstrate the utility of our approach by studying the effectiveness of interventions used in Virginia in early 2020 through counterfactual simulations

A framework for modeling human behavior in large-scale agent-based epidemic simulations

Agent-based modeling is increasingly being used in computational epidemiology to characterize important behavioral dimensions, such as the heterogeneity of the individual responses to interventions, when studying the spread of a disease. Existing agent-based simulation frameworks and platforms currently fall in one of two categories: those that can simulate millions of individuals with simple behaviors (e.g., based on simple state machines), and those that consider more complex and social behaviors (e.g., agents that act according to their own agenda and preferences, and deliberate about norm compliance) but, due to the computational complexity of reasoning involved, have limited scalability. In this paper, we present a novel framework that enables large-scale distributed epidemic simulations with complex behaving social agents whose decisions are based on a variety of concepts and internal attitudes such as sense, knowledge, preferences, norms, and plans. The proposed framework supports simulations with millions of such agents that can individually deliberate about their own knowledge, goals, and preferences, and can adapt their behavior based on other agents’ behaviors and on their attitude toward complying with norms. We showcase the applicability and scalability of the proposed framework by developing a model of the spread of COVID-19 in the US state of Virginia. Results illustrate that the framework can be effectively employed to simulate disease spreading with millions of complex behaving agents and investigate behavioral interventions over a period of time of months