arXiv subjects

Robert Moss

Publications and source records attributed to Robert Moss.

6 recordsLinked to original sources

arXiv2025

Modelling the impact of improving access to healthcare on Hepatitis B prevalence in the Thai-Myanmar border region

Introduction: In Thailand, Hepatitis B is still endemic despite a strong program to eliminate the disease. A higher prevalence is reported in the border region and among migrants due to physical, financial and cultural barriers. Policies and programs targeting the border region and migrant communities have been suggested. Models can be used to understand and quantify the impact of these policies, given they can capture the heterogeneity within the population. Methods: In this study, we developed an Agent-based model that captures the differences between the Thai and migrant populations living in this region, notably the higher level of mobility, lower access to healthcare, and the higher prevalence of Hepatitis B among migrants, by modelling the origin of each individual explicitly. We used the model to estimate future trends of Hepatitis B prevalence in Thailand near the border with Myanmar under different scenarios of intervention. Results: Our study shows that although the current intervention level is effective in the Thai population, it is insufficient to reach national elimination targets due to high prevalence in migrants. Improving access to healthcare for migrants and the border region could potentially help to reach elimination targets, and we quantified the level of improvement needed to achieve elimination. Conclusion: Although there already exist policies to make healthcare more accessible to migrants and the border regions, they are still not yet effective due to financial and cultural barriers. Bringing down those barriers could reduce Hepatitis B prevalence in those communities and regions and contribute to reaching elimination targets in a reasonable timeline.

q-bio.OT↗

arXiv2024

A hybrid framework for compartmental models enabling simulation-based inference

Multi-scale systems often exhibit a combination of stochastic and deterministic dynamics. In compartmental models, low occupancy compartments tend to exhibit stochastic dynamics while high occupancy compartments tend to follow deterministic dynamics. Representing both dynamics with existing methods is challenging. Failing to account for stochasticity in small populations can produce ``atto-foxes'', for example in the Lotka-Volterra ordinary differential equation (ODE) model. This limitation becomes problematic when studying the extinction of species or the clearance of infection, but it can be overcome by using discrete stochastic models, such as continuous time Markov chains (CTMCs). Unfortunately, simulating CTMCs is impractical for many realistic models, where discrete events have very high frequencies. In this work, we develop a novel mathematical framework to couple continuous ODEs and discrete CTMCs: ``Jump-Switch-Flow'' (JSF). In this framework, compartments can reach extinct states (``absorbing states''), thereby resolving atto-fox-type problems. JSF has the desired behaviours of exact CTMC simulation, but is substantially computationally faster than existing alternatives, by at least one order of magnitude, and can even obtain constant scaling, irrespective of compartment occupancy. We demonstrate JSF's utility for simulation-based inference, particularly multi-scale problems, with several case-studies. In a simulation study, we demonstrate how JSF can enable a more nuanced analysis of the efficacy of public health interventions. We also carry out a novel analysis of longitudinal within-host data from SARS-CoV-2 infections to quantify the timing of viral clearance. In this work, we show how JSF offers a novel approach to compartmental model simulation.

q-bio.PE↗

arXiv2022

Bayesian Non-Homogeneous Hidden Markov Model with Variable Selection for Investigating Drivers of Seizure Risk Cycling

A major issue in the clinical management of epilepsy is the unpredictability of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies, which are a stochastic measurement of seizure risk. We consider a Bayesian non-homogeneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers significant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data. For inference, we implement an efficient sampler that employs stochastic search and data augmentation techniques. We evaluate model performance on simulated seizure count data. We then demonstrate the clinical utility of the proposed model by analyzing daily seizure count data from 133 patients with Dravet syndrome collected through the Seizure Tracker TM system, a patient-reported electronic seizure diary. We report on the dynamics of seizure risk cycling, including validation of several known pharmacologic relationships. We also uncover novel findings characterizing the presence and volatility of risk states in Dravet syndrome, which may directly inform counseling to reduce the unpredictability of seizures for patients with this devastating cause of epilepsy.

stat.AP↗

arXiv2020

From climate change to pandemics: decision science can help scientists have impact

Scientific knowledge and advances are a cornerstone of modern society. They improve our understanding of the world we live in and help us navigate global challenges including emerging infectious diseases, climate change and the biodiversity crisis. For any scientist, whether they work primarily in fundamental knowledge generation or in the applied sciences, it is important to understand how science fits into a decision-making framework. Decision science is a field that aims to pinpoint evidence-based management strategies. It provides a framework for scientists to directly impact decisions or to understand how their work will fit into a decision process. Decision science is more than undertaking targeted and relevant scientific research or providing tools to assist policy makers; it is an approach to problem formulation, bringing together mathematical modelling, stakeholder values and logistical constraints to support decision making. In this paper we describe decision science, its use in different contexts, and highlight current gaps in methodology and application. The COVID-19 pandemic has thrust mathematical models into the public spotlight, but it is one of innumerable examples in which modelling informs decision making. Other examples include models of storm systems (eg. cyclones, hurricanes) and climate change. Although the decision timescale in these examples differs enormously (from hours to decades), the underlying decision science approach is common across all problems. Bridging communication gaps between different groups is one of the greatest challenges for scientists. However, by better understanding and engaging with the decision-making processes, scientists will have greater impact and make stronger contributions to important societal problems.

cs.CY↗

arXiv2017

Alternative gravity rotation curves for the Little Things Survey

Galactic rotation curves have proven to be the testing ground for dark matter bounds in spiral galaxies of all morphologies. Dwarf galaxies serve as an increasingly interesting case of rotation curve dynamics due to their typically rising rotation curve as opposed to the flattening curve of large spirals. These galaxies usually vary in galactic structure and mostly terminate at small radial distances. This, coupled with the fact that Cold Dark Matter theories struggle with the universality of galactic rotation curves, allow for exclusive features of alternative gravitational models to be analyzed. Recently, the THINGS (The HI Nearby Galactic Survey) has been extended to include a sample of 25 dwarf galaxies now known as the LITTLE THINGS Survey. Here, we present a thorough application of alternative gravitational models to the LITTLE THINGS survey, specifically focusing on conformal gravity and Modified Newtonian Dynamics. An analysis and discussion of the results of the fitting procedure of the two alternative gravitational models are explored, as well as the resulting rotation curve predictions of each. Further, we show how these two alternative gravitational models account for the recently observed universal trends in centripetal accelerations in spiral galaxies. We posit here that both conformal gravity and MOND can provide an accurate description of the galactic dynamics without the need for dark matter.

gr-qc↗

arXiv2014

A Comparison of Monte Carlo Tree Search and Mathematical Optimization for Large Scale Dynamic Resource Allocation

Dynamic resource allocation (DRA) problems are an important class of dynamic stochastic optimization problems that arise in a variety of important real-world applications. DRA problems are notoriously difficult to solve to optimality since they frequently combine stochastic elements with intractably large state and action spaces. Although the artificial intelligence and operations research communities have independently proposed two successful frameworks for solving dynamic stochastic optimization problems---Monte Carlo tree search (MCTS) and mathematical optimization (MO), respectively---the relative merits of these two approaches are not well understood. In this paper, we adapt both MCTS and MO to a problem inspired by tactical wildfire and management and undertake an extensive computational study comparing the two methods on large scale instances in terms of both the state and the action spaces. We show that both methods are able to greatly improve on a baseline, problem-specific heuristic. On smaller instances, the MCTS and MO approaches perform comparably, but the MO approach outperforms MCTS as the size of the problem increases for a fixed computational budget.

math.OC↗