1 code implementation • 8 Jul 2024 • Makkunda Sharma, Fan Yang, Duy-Nhat Vo, Esra Suel, Swapnil Mishra, Samir Bhatt, Oliver Fiala, William Rudgard, Seth Flaxman
Using our dataset we benchmark multiple models, from low-level satellite imagery models such as MOSAIKS , to deep learning foundation models, which include both generic vision models such as Self-Distillation with no Labels (DINOv2) models and specific satellite imagery models such as SatMAE.
1 code implementation • 3 May 2024 • Jacob Curran-Sebastian, Frederik Mølkjær Andersen, Samir Bhatt
We also demonstrate how to combine our model with a deterministic approximation, such that longer term projections can be generated that still incorporate the uncertainty from the early growth phase of the epidemic.
no code implementations • 24 Nov 2023 • Matthew J. Penn, Christl A. Donnelly, Samir Bhatt
Player tracking data remains out of reach for many professional football teams as their video feeds are not sufficiently high quality for computer vision technologies to be used.
1 code implementation • 9 Jun 2023 • Matthew J Penn, Neil Scheidwasser, Joseph Penn, Christl A Donnelly, David A Duchêne, Samir Bhatt
Phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics.
no code implementations • 31 May 2023 • Elizaveta Semenova, Swapnil Mishra, Samir Bhatt, Seth Flaxman, H Juliette T Unwin
Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance.
no code implementations • 1 May 2023 • Nicolas Banholzer, Thomas Mellan, H Juliette T Unwin, Stefan Feuerriegel, Swapnil Mishra, Samir Bhatt
Here, we compare short-term probabilistic forecasts of popular mechanistic models based on the renewal equation with forecasts of statistical time series models.
1 code implementation • 25 Apr 2023 • Matthew J Penn, Neil Scheidwasser, Mark P Khurana, David A Duchêne, Christl A Donnelly, Samir Bhatt
Binary phylogenetic trees inferred from biological data are central to understanding the shared history among evolutionary units.
2 code implementations • 9 Apr 2023 • Elizaveta Semenova, Prakhar Verma, Max Cairney-Leeming, Arno Solin, Samir Bhatt, Seth Flaxman
Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs).
no code implementations • 31 Oct 2022 • Christian Morgenstern, Daniel J. Laydon, Charles Whittaker, Swapnil Mishra, David Haw, Samir Bhatt, Neil M. Ferguson
For example, more developed countries in Europe typically had more cautious approaches to the COVID-19 pandemic, prioritising healthcare, and excess deaths over economic performance.
1 code implementation • 25 Oct 2022 • Matthew J. Penn, Daniel J. Laydon, Joseph Penn, Charles Whittaker, Christian Morgenstern, Oliver Ratmann, Swapnil Mishra, Mikko S. Pakkanen, Christl A. Donnelly, Samir Bhatt
Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters).
no code implementations • 21 Oct 2022 • Xenia Miscouridou, Samir Bhatt, George Mohler, Seth Flaxman, Swapnil Mishra
Here we develop a new class of spatiotemporal Hawkes processes that can capture both triggering and clustering behavior and we provide an efficient method for performing inference.
no code implementations • 20 Sep 2022 • Giovanni Charles, Timothy M. Wolock, Peter Winskill, Azra Ghani, Samir Bhatt, Seth Flaxman
Epidemic models are powerful tools in understanding infectious disease.
1 code implementation • 20 Oct 2021 • Elizaveta Semenova, Yidan Xu, Adam Howes, Theo Rashid, Samir Bhatt, Swapnil Mishra, Seth Flaxman
Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling.
1 code implementation • 12 Jul 2021 • Mikko S. Pakkanen, Xenia Miscouridou, Matthew J. Penn, Charles Whittaker, Tresnia Berah, Swapnil Mishra, Thomas A. Mellan, Samir Bhatt
We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling.
1 code implementation • 24 Mar 2021 • Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Samir Bhatt, Bernhard Schölkopf, Stefan Bauer
Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak.
1 code implementation • 22 Feb 2021 • Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra, Xenia Miscouridou, Ricardo P Schnekenberg, Charles Whittaker, Michaela Vollmer, Seth Flaxman, Samir Bhatt, Thomas A Mellan
An important example of this problem is the nowcasting of COVID-19 mortality: given a stream of reported counts of daily deaths, can we correct for the delays in reporting to paint an accurate picture of the present, with uncertainty?
1 code implementation • 8 Sep 2020 • Iwona Hawryluk, Swapnil Mishra, Seth Flaxman, Samir Bhatt, Thomas A. Mellan
The approach is shown to be useful in practice when applied to a real problem - to perform model selection for a semi-mechanistic hierarchical Bayesian model of COVID-19 transmission in South Korea involving the integration of a 200D density.
no code implementations • 13 Jul 2020 • Michaela A. C. Vollmer, Ben Glampson, Thomas A. Mellan, Swapnil Mishra, Luca Mercuri, Ceire Costello, Robert Klaber, Graham Cooke, Seth Flaxman, Samir Bhatt
We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE.
2 code implementations • 16 Jun 2020 • Chiara Forlani, Samir Bhatt, Michela Cameletti, Elias Krainski, Marta Blangiardo
Unlike other examples, we jointly model the response (concentration level at monitoring stations) and the dispersion model outputs on different scales, accounting for the different sources of uncertainty.
Applications
1 code implementation • 23 Apr 2020 • Seth Flaxman, Swapnil Mishra, Axel Gandy, H Juliette T Unwin, Helen Coupland, Thomas A. Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Callizo, Imperial College COVID-19 Response Team, Charles Whittaker, Peter Winskill, Xiaoyue Xi, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A. C. Vollmer, Neil M. Ferguson, Samir Bhatt
Our model estimates these changes by calculating backwards from temporal data on observed to estimate the number of infections and rate of transmission that occurred several weeks prior, allowing for a probabilistic time lag between infection and death.
Applications Methodology
2 code implementations • 17 Feb 2020 • Swapnil Mishra, Seth Flaxman, Tresnia Berah, Harrison Zhu, Mikko Pakkanen, Samir Bhatt
We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions to enable statistical inference (such as the integral of a log Gaussian process).
no code implementations • 22 Feb 2019 • Philip Milton, Emanuele Giorgi, Samir Bhatt
Kernel methods are an incredibly popular technique for extending linear models to non-linear problems via a mapping to an implicit, high-dimensional feature space.
no code implementations • 30 Oct 2018 • Lars Lorch, Abir De, Samir Bhatt, William Trouleau, Utkarsh Upadhyay, Manuel Gomez-Rodriguez
We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps.
no code implementations • 15 Nov 2017 • Jean-Francois Ton, Seth Flaxman, Dino Sejdinovic, Samir Bhatt
The use of covariance kernels is ubiquitous in the field of spatial statistics.
no code implementations • 10 Dec 2016 • Samir Bhatt, Ewan Cameron, Seth R. Flaxman, Daniel J Weiss, David L Smith, Peter W Gething
Maps of infectious disease---charting spatial variations in the force of infection, degree of endemicity, and the burden on human health---provide an essential evidence base to support planning towards global health targets.