Tootfinder

"Inside a plan to use AI to amplify doubts about the dangers of pollutants"
#AI #ArtificialIntelligence #Climate

I've been in meetings where #ACC "experts" testified "truthpartly" - but not "truthfully" - about chemical hazards - referencing old tests of health impacts (1970s/80s knowledge) while ignoring the mass of robust data developed in the late 90s and 2000s. This is another repeat performance for #ACC

I've been in meetings where #ACC "experts" testified "truthpartly" - but not "truthfully" - about chemical hazards - referencing old tests of health impacts (1970s/80s knowledge) while ignoring the mass of robust data developed in the late 90s and 2000s. This is another repeat performance for #ACC

Probably Approximately Correct Causal Discovery
Mian Wei, Somesh Jha, David Page
https://arxiv.org/abs/2507.18903 https://arxiv.org/pdf/2507.18903

Probably Approximately Correct Causal Discovery
The discovery of causal relationships is a foundational problem in artificial intelligence, statistics, epidemiology, economics, and beyond. While elegant theories exist for accurate causal discovery given infinite data, real-world applications are inherently resource-constrained. Effective methods for inferring causal relationships from observational data must perform well under finite data and time constraints, where "performing well" implies achieving high, though not perfect accuracy. In hi…

Why can't Epidemiology be automated (yet)?
David Bann, Ed Lowther, Liam Wright, Yevgeniya Kovalchuk
https://arxiv.org/abs/2507.15617 https://

Why can't Epidemiology be automated (yet)?
Recent advances in artificial intelligence (AI) - particularly generative AI - present new opportunities to accelerate, or even automate, epidemiological research. Unlike disciplines based on physical experimentation, a sizable fraction of Epidemiology relies on secondary data analysis and thus is well-suited for such augmentation. Yet, it remains unclear which specific tasks can benefit from AI interventions or where roadblocks exist. Awareness of current AI capabilities is also mixed. Here, w…

Replaced article(s) found for cs.SI. https://arxiv.org/list/cs.SI/new
[1/1]:
- Epidemiology-informed Network for Robust Rumor Detection
Wei Jiang, Tong Chen, Xinyi Gao, Wentao Zhang, Lizhen Cui, Hongzhi Yin

Riesz representers for the rest of us
Nicholas T. Williams, Oliver J. Hines, Kara E. Rudolph
https://arxiv.org/abs/2507.19413 https://arxiv.org/pdf/2507.19…

Riesz representers for the rest of us
The application of semiparametric efficient estimators, particularly those that leverage machine learning, is rapidly expanding within epidemiology and causal inference. Much of the recent methodological literature on these estimators relies heavily on the Riesz representation theorem and Riesz regression. This paper aims to introduce the Riesz representation theorem to an applied audience, explaining why and how Riesz regression is becoming widely used in the semiparametric estimator statistic…

Improving wastewater-based epidemiology through strategic placement of samplers
Anthony J Wood, Jessica Enright, Aeron R Sanchez, Ewan Colman, Rowland R Kao
https://arxiv.org/abs/2506.14331

Improving wastewater-based epidemiology through strategic placement of samplers
Wastewater-based epidemiology (WBE) is a fast emerging method for passively monitoring diseases in a population. By measuring the concentrations of pathogenic materials in wastewater, WBE negates demographic biases in clinical testing and healthcare demand, and may act as a leading indicator of disease incidence. For a WBE system to be effective, it should detect the presence of a new pathogen of concern early enough and with enough precision that it can still be localised and contained. In t…

(Bicycle) Speed and safety
For years I've heard people talking about how motor-assisted bicycles "are safer" because they can "match traffic speed", but I think this is mistaken.  Here's three reasons why. For me the most compelling reason comes from a 2007 paper by Laurie F. Beck, Ann M. Dellinger, and Mary E. O’Neil in the American Journal of Epidemiology, "Motor Vehicle Crash Injury Rates by Mode of Travel, United States: Using Exposure-Based Methods…

(Bicycle) Speed and safety
For years I’ve heard people talking about how motor-assisted bicycles “are safer” because they can “match traffic speed”, but I think this is mistaken.  Here’s t…

Inference on Nonlinear Counterfactual Functionals under a Multiplicative IV Model
Yonghoon Lee, Mengxin Yu, Jiewen Liu, Chan Park, Yunshu Zhang, James M. Robins, Eric J. Tchetgen Tchetgen
https://arxiv.org/abs/2507.15612

Inference on Nonlinear Counterfactual Functionals under a Multiplicative IV Model
Instrumental variable (IV) methods play a central role in causal inference, particularly in settings where treatment assignment is confounded by unobserved variables. IV methods have been extensively developed in recent years and applied across diverse domains, from economics to epidemiology. In this work, we study the recently introduced multiplicative IV (MIV) model and demonstrate its utility for causal inference beyond the average treatment effect. In particular, we show that it enables ide…

A Compartmental Model for Epidemiology with Human Behavior and Stochastic Effects
Christian Parkinson, Weinan Wang
https://arxiv.org/abs/2507.01046 https:/…

A Compartmental Model for Epidemiology with Human Behavior and Stochastic Effects
We propose a compartmental model for epidemiology wherein the population is split into groups with either comply or refuse to comply with protocols designed to slow the spread of a disease. Parallel to the disease spread, we assume that noncompliance with protocols spreads as a social contagion. We begin by deriving the reproductive ratio for a deterministic version of the model, and use this to fully characterize the local stability of disease free equilibrium points. We then append the determ…

Estimating the Number of Opioid Overdoses in British Columbia Using Relational Evidence with Tree Structure
Mallory J Flynn, Paul Gustafson, Michael A. Irvine
https://arxiv.org/abs/2506.21024

Estimating the Number of Opioid Overdoses in British Columbia Using Relational Evidence with Tree Structure
In many fields, populations of interest are hidden from data for a variety of reasons, though their magnitude remains important in determining resource allocation and appropriate policy. In public health and epidemiology, linkages or relationships between sources of data may exist due to intake structure of care providers, referrals, or other related health programming. These relationships often admit a tree structure, with the target population represented by the root, and paths from root-to-l…

This https://arxiv.org/abs/2411.11867 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Stability in Reaction Network Models via an Extension of the Next Generation Matrix Method
In this essay, we investigate some relations between Chemical Reaction Networks (CRN) and Mathematical Epidemiology (ME) and report on several pleasant surprises which we had simply by putting these two topics together. Firstly, we propose a definition of ME models as a subset of CRN models. Secondly, we review a fundamental stability result for boundary points, known in ME as the NGM method since it replaces the investigation of the Jacobian by that of a matrix whose origins lie in probabi…

Kinetic formulation of compartmental epidemic models
Carolina Strecht-Fernandes, Fabio A. C. C. Chalub
https://arxiv.org/abs/2506.13551 https://

Kinetic formulation of compartmental epidemic models
We introduce a kinetic model that couples the movement of a population of individuals with the dynamics of a pathogen in the same population. The model is formally shown to generalize the well-known compartmental models in mathematical epidemiology. We prove the existence and uniqueness of solutions in appropriate spaces for particular instances of the model. We finish with some examples, and discuss possible applications of this modeling approach.

VaxPulse: Active Global Vaccine Infodemic Risk Assessment
Gerardo Luis Dimaguila (Epidemiology Informatics, Centre for Health Analytics, Melbourne Children's Campus, Australia, Department of Paediatrics, The University of Melbourne, Australia), Muhammad Javed (Epidemiology Informatics, Centre for Health Analytics, Melbourne Children's Campus, Australia, Global Vaccine Data Network, University of Auckland, New Zealand), Jeremiah Munakabayo (Epidemiology Informatics, Centre for H…

VaxPulse: Active Global Vaccine Infodemic Risk Assessment
Vaccine infodemics, driven by misinformation, disinformation, and inauthentic online behaviours, pose significant threats to global public health. This paper presents our response to this challenge, demonstrating how we developed VaxPulse Vaccine Infodemic Risk Assessment Lifecycle (VIRAL), an AI-powered social listening platform designed to monitor and assess vaccine-related infodemic risks. Leveraging interdisciplinary expertise and international collaborations, VaxPulse VIRAL integrates mach…

Akaike information criterion for segmented regression models
Kazuki Nakajima, Yoshiyuki Ninomiya
https://arxiv.org/abs/2506.08760 https://

Akaike information criterion for segmented regression models
In segmented regression, when the regression function is continuous at the change-points that are the boundaries of the segments, it is also called joinpoint regression, and the analysis package developed by \cite{KimFFM00} has become a standard tool for analyzing trends in longitudinal data in the field of epidemiology. In addition, it is sometimes natural to expect the regression function to be discontinuous at the change-points, and in the field of epidemiology, this model is used in \cite{J…

Controlling the Spread of Epidemics on Networks with Differential Privacy
Dung Nguyen, Aravind Srinivasan, Renata Valieva, Anil Vullikanti, Jiayi Wu
https://arxiv.org/abs/2506.00745

Controlling the Spread of Epidemics on Networks with Differential Privacy
Designing effective strategies for controlling epidemic spread by vaccination is an important question in epidemiology, especially in the early stages when vaccines are limited. This is a challenging question when the contact network is very heterogeneous, and strategies based on controlling network properties, such as the degree and spectral radius, have been shown to be effective. Implementation of such strategies requires detailed information on the contact structure, which might be sensitiv…

This https://arxiv.org/abs/2411.11867 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Stability in Reaction Network Models via an Extension of the Next Generation Matrix Method
In this essay, we investigate some relations between Chemical Reaction Networks (CRN) and Mathematical Epidemiology (ME) and report on several pleasant surprises which we had simply by putting these two topics together. Firstly, we propose a definition of ME models as a subset of CRN models. Secondly, we review a fundamental stability result for boundary points, known in ME as the NGM method since it replaces the investigation of the Jacobian by that of a matrix whose origins lie in probabi…

Low-EFFourth: A computational framework for generating and studying multilevel model ensembles in low-dimensional systems
Francisco de Melo Vir\'issimo
https://arxiv.org/abs/2506.03313

Low-EFFourth: A computational framework for generating and studying multilevel model ensembles in low-dimensional systems
This paper introduces Low-EFFourth (LEF4), a MATLAB-based computational framework designed for generating and studying multilevel model ensembles in continuous dynamical systems. Initially developed to address questions in climate modelling, LEF4 can also be used in other disciplines such as epidemiology, economics, and engineering, with minimal modifications to the code. The framework provides an efficient and flexible approach for investigating uncertainties arising from initial conditions, m…

A note on metapopulation models
Diepreye Ayabina, Hasan Sevil, Adam Kleczkowski, M. Gabriela M. Gomes
https://arxiv.org/abs/2506.04284 https://

A note on metapopulation models
Metapopulation models are commonly used in ecology, evolution, and epidemiology. These models usually entail homogeneity assumptions within patches and study networks of migration between patches to generate insights into conservation of species, differentiation of populations, and persistence of infectious diseases. Here, focusing on infectious disease epidemiology, we take a complementary approach and study the effects of individual variation within patches while neglecting any form of diseas…

Parameterized Critical Node Cut Revisited
Du\v{s}an Knop, Nikolaos Melissinos, Manolis Vasilakis
https://arxiv.org/abs/2506.23363 https://

Parameterized Critical Node Cut Revisited
Given a graph $G$ and integers $k, x \geq 0$, the Critical Node Cut problem asks whether it is possible to delete at most $k$ vertices from $G$ such that the number of remaining pairs of connected vertices is at most $x$. This problem generalizes Vertex Cover (when $x = 0$), and has applications in network design, epidemiology, and social network analysis. We investigate the parameterized complexity of Critical Node Cut under various structural parameters. We first significantly strengthen exis…

Structural Identifiability of Compartmental Models: Recent Progress and Future Directions
Nicolette Meshkat, Anne Shiu
https://arxiv.org/abs/2507.04496 htt…

Structural Identifiability of Compartmental Models: Recent Progress and Future Directions
We summarize recent progress on the theory and applications of structural identifiability of compartmental models. On the applications side, we review identifiability analyses undertaken recently for models arising in epidemiology, oncology, and other areas; and we summarize common approaches for handling models that are unidentifiable. We also highlight recent theoretical and algorithmic results on how to reparametrize unidentifiable models and, in the context of linear compartmental models, h…

ExDiff: A Framework for Simulating Diffusion Processes on Complex Networks with Explainable AI Integration
Annamaria Defilippo, Ugo Lomoio, Barbara Puccio, Pierangelo Veltri, Pietro Hiram Guzzi
https://arxiv.org/abs/2506.04271

ExDiff: A Framework for Simulating Diffusion Processes on Complex Networks with Explainable AI Integration
Understanding and controlling diffusion processes in complex networks is critical across domains ranging from epidemiology to information science. Here, we present ExDiff, an interactive and modular computational framework that integrates network simulation, graph neural networks (GNNs), and explainable artificial intelligence (XAI) to model and interpret diffusion dynamics. ExDiff combines classical compartmental models with deep learning techniques to capture both the structural and temporal …