publications

2023

1. Differentiable Agent-Based Epidemiology

   Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, and 4 more authors

   In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

   Abs

   Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments. Agent-based models (ABMs) are an increasingly popular simulation paradigm that can represent the heterogeneity of contact interactions with granular detail and agency of individual behavior. However, conventional ABM frameworks not differentiable and present challenges in scalability; due to which it is non-trivial to connect them to auxiliary data sources. In this paper, we introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources. This provides an array of practical benefits for calibration, forecasting, and evaluating policy interventions. We demonstrate the efficacy of GradABM via extensive experiments with real COVID-19 and influenza datasets.

2. Qwind3: UV Line-Driven Accretion Disc Wind Models for AGN Feedback

   Arnau Quera-Bofarull, Chris Done, Cedric G Lacey, and 2 more authors

   Monthly Notices of the Royal Astronomical Society, Jan 2023

   Abs

   The ultraviolet (UV) bright accretion disc in active galactic nuclei (AGNs) should give rise to line driving, producing a powerful wind that may play an important role in AGN feedback as well as in producing structures like the broad-line region. However, coupled radiation-hydrodynamic codes are complex and expensive, so we calculate the winds instead using a non-hydrodynamical approach (the qwind framework). The original qwind model assumed the initial conditions in the wind, and had only simple radiation transport. Here, we present an improved version that derives the wind initial conditions and has significantly improved ray tracing to calculate the wind absorption self-consistently, given the extended nature of the UV emission. We also correct the radiation flux for relativistic effects and assess the impact of this on the wind velocity. These changes mean the model is more physical, so its predictions are more robust. We find that, even when accounting for relativistic effects, winds can regularly achieve velocities ≃(0.1-0.5)c, and carry mass-loss rates that can be up to 80 per cent of the accreted mass for black hole masses of 107-9 M⊙, and mass accretion rates of 50 per cent of the Eddington rate. Overall, the ratio of kinetic power carried by the wind to bolometric luminosity increases with mass accretion rate at a given black hole mass, unlike the constant fraction generally assumed in current cosmological simulations that include AGN feedback. The updated code, qwind3, is publicly available in GitHub.1

3. Don’t Simulate Twice: One-shot Sensitivity Analyses via Automatic Differentiation

   Arnau Quera-Bofarull, Ayush Chopra, Joseph Aylett-Bullock, and 4 more authors

   In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, Jan 2023

   Abs

   Agent-based models (ABMs) are a promising tool to simulate complex environments. Their rapid adoption requires scalable specification, efficient data-driven calibration, and validation through sensitivity analyses. Recent progress in tensorized and differentiable ABM design (GradABM) has enabled fast calibration of million-size populations, however, validation through sensitivity analysis is still computationally prohibitive due to the need for running the model a large number of times. Here, we present a novel methodology that uses automatic differentiation to perform a sensitivity analysis on a calibrated ABM without requiring any further simulations. The key insight is to leverage gradients of a GradABM to compute exact partial derivatives of any model output with respect to an arbitrary combination of parameters. We demonstrate the benefits of this approach on a case study of the first wave of COVID-19 in London, where we investigate the causes of variations in infections by age, socio-economic index, ethnicity, and geography. Finally, we also show that the same methodology allows for the design of optimal policy interventions. The code to reproduce the presented results is made available on GitHub (https://github.com/arnauqb/one\textsubscriptshot\textsubscriptsensitivity).

2022

1. Epidemiological Modelling in Refugee and Internally Displaced People Settlements: Challenges and Ways Forward

   Joseph Aylett-Bullock, Robert Tucker Gilman, Ian Hall, and 23 more authors

   BMJ Global Health, Mar 2022

   Abs

   The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several challenges including data availability and quality, the applicability of existing models to those contexts, the accurate modelling of cultural differences or specificities of those operational settings, the communication of results and uncertainties, as well as the alignment of strategic goals between diverse partners in complex situations. In this paper, we systematically review the limited epidemiological modelling work applied to refugee and IDP settlements so far, and discuss challenges and identify lessons learnt from the process. With the likelihood of disease outbreaks expected to increase in the future as more people are displaced due to conflict and climate change, we call for the development of more approaches and models specifically designed to include the unique features and populations of refugee and IDP settlements. To strengthen collaboration between the modelling and the humanitarian public health communities, we propose a roadmap to encourage the development of systems and frameworks to share needs, build tools and coordinate responses in an efficient and scalable manner, both for this pandemic and for future outbreaks.

2. Bayesian Emulation and History Matching of JUNE

   I. Vernon, J. Owen, J. Aylett-Bullock, and 11 more authors

   Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Oct 2022

   Abs

   We analyze JUNE: a detailed model of COVID-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. JUNE requires substantial computational resources to evaluate, making model calibration and general uncertainty analysis extremely challenging. We describe and employ the uncertainty quantification approaches of Bayes linear emulation and history matching to mimic JUNE and to perform a global parameter search, hence identifying regions of parameter space that produce acceptable matches to observed data, and demonstrating the capability of such methods. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.

2021

1. Operational Response Simulation Tool for Epidemics within Refugee and IDP Settlements: A Scenario-Based Case Study of the Cox’s Bazar Settlement

   Joseph Aylett-Bullock, Carolina Cuesta-Lazaro, Arnau Quera-Bofarull, and 12 more authors

   PLOS Computational Biology, Oct 2021

   Abs

   The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox’s Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable.

2. Vaccinations or Non-Pharmaceutical Interventions:Safe Reopening of Schools in England

   Carolina Cuesta-Lazaro, Arnau Quera-Bofarull, Joseph Aylett-Bullock, and 8 more authors

   medRxiv, Sep 2021

   Abs

   With high levels of the Delta variant of COVID-19 circulating in England during September 2021, schools are set to reopen with few school-based non-pharmaceutical interventions (NPIs). In this paper, we present simulation results obtained from the individual-based model, JUNE, for school opening after a prior vaccination campaign using an optimistic set of assumptions about vaccine efficacy and the likelihood of prior-reinfection. We take a scenario-based approach to modelling potential interventions to assess relative changes rather than real-world forecasts. Specifically, we assess the effects of vaccinating those aged 16-17, those aged 12-17, and not vaccinating children at all relative to only vaccinating the adult population. Vaccinating children in the 12-15 age group would have had a significant impact on the course of the epidemic, saving thousands of lives overall in these simulations. In the absence of such a vaccination campaign our simulations show there could still be a significant positive impact on the epidemic (fewer cases, fewer deaths) by continuing NPI strategies in schools. Our analysis suggests that the best results are likely derived from a combination of vaccinations and NPIs.

2020

1. June: Open-Source Individual-Based Epidemiology Simulation

   Joseph Aylett-Bullock, Carolina Cuesta-Lazaro, Arnau Quera-Bofarull, and 11 more authors

   Royal Society Open Science, Sep 2020

   Abs

   We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. June provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply June to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.

2. Qwind Code Release: A Non-Hydrodynamical Approach to Modelling Line-Driven Winds in Active Galactic Nuclei

   Arnau Quera-Bofarull, Chris Done, Cedric Lacey, and 3 more authors

   Monthly Notices of the Royal Astronomical Society, Jun 2020

   Abs

   Ultraviolet (UV) line driven winds may be an important part of the active galactic nucleus (AGN) feedback process, but understanding their impact is hindered by the complex nature of the radiation hydrodynamics. Instead, we have taken the approach pioneered by Risaliti & Elvis, calculating only ballistic trajectories from radiation forces and gravity, but neglecting gas pressure. We have completely re-written their QWIND code using more robust algorithms, and can now quickly model the acceleration phase of these winds for any AGN spectral energy distribution spanning UV and X-ray wavebands. We demonstrate the code using an AGN with black hole mass \10\^8 M_⊙emitting at half the Eddington rate and show that this can effectively eject a wind with velocities \≃(0.1-0.2) c\. The mass loss rates can be up to \≃0.3 M_⊙per year, consistent with more computationally expensive hydrodynamical simulations, though we highlight the importance of future improvements in radiation transfer along the multiple different lines of sight illuminating the wind. The code is fully public, and can be used to quickly explore the conditions under which AGN feedback can be dominated by accretion disc winds.

2019

1. XNet: A Convolutional Neural Network (CNN) Implementation for Medical x-Ray Image Segmentation Suitable for Small Datasets

   Joseph Bullock, Carolina Cuesta-Lázaro, and Arnau Quera-Bofarull

   In Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, Mar 2019

   Abs

   X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions. We apply a machine learning approach to this problem, presenting an end-to-end solution which results in robust and efficient inference. Since medical institutions often do not have the resources to process and label the large quantity of X-Ray images usually needed for neural network training, we design an end-to-end solution for small datasets, while achieving state-of-the-art results. Our implementation produces an overall accuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical image processing techniques, such as clustering and entropy based methods, while improving upon the output of existing neural networks used for segmentation in non-medical contexts. The code used for this project is available online.\textsuperscript1

2018

1. Gravitational Corrections to Light Propagation in a Perturbed FLRW Universe and Corresponding Weak-Lensing Spectra

   Carolina Cuesta-Lazaro, Arnau Quera-Bofarull, Robert Reischke, and 1 more author

   Monthly Notices of the Royal Astronomical Society, Jun 2018

   Abs

   When the gravitational lensing of the large-scale structure is calculated from a cosmological model a few assumptions enter: (i) one assumes that the photons follow unperturbed background geodesics, which is usually referred to as the Born approximation, (ii) the lenses move slowly, (iii) the source-redshift distribution is evaluated relative to the background quantities, and (iv) the lensing effect is linear in the gravitational potential. Even though these approximations are small individually they could sum up, especially since they include local effects such as the Sachs–Wolfe and peculiar motion, but also non-local ones like the Born approximation and the integrated Sachs–Wolfe effect. In this work, we will address all points mentioned and perturbatively calculate the effect on a tomographic cosmic shear power spectrum of each effect individually as well as all cross-correlations. Our findings show that each effect is at least 4–5 orders of magnitude below the leading order lensing signal. Finally, we sum up all effects to estimate the overall impact on parameter estimation by a future cosmological weak-lensing survey such as Euclid in a wcold dark matter cosmology with parametrization Ωm, σ8, ns, h, w0, and wa, using five tomographic bins. We consistently find a parameter bias of 10-5, which is therefore completely negligible for all practical purposes, confirming that other effects such as intrinsic alignments, magnification bias and uncertainties in the redshift distribution will be the dominant systematic source in future surveys.