Search Results for author: Samir Bhatt

Found 25 papers, 14 papers with code

KidSat: satellite imagery to map childhood poverty dataset and benchmark

1 code implementation8 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.

Modelling the Stochastic Importation Dynamics and Establishment of Novel Pathogenic Strains using a General Branching Processes Framework

1 code implementation3 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.

Continuous football player tracking from discrete broadcast data

no code implementations24 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.

Leaping through tree space: continuous phylogenetic inference for rooted and unrooted trees

1 code implementation9 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.

Phylo2Vec: a vector representation for binary trees

1 code implementation25 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.

PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling

2 code implementations9 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).

Bayesian Inference Gaussian Processes

The interaction of transmission intensity, mortality, and the economy: a retrospective analysis of the COVID-19 pandemic

no code implementations31 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.

Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes

no code implementations21 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.

PriorVAE: Encoding spatial priors with VAEs for small-area estimation

1 code implementation20 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.

Decoder Gaussian Processes

Unifying incidence and prevalence under a time-varying general branching process

1 code implementation12 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.

Epidemiology Probabilistic Programming

Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases

1 code implementation24 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.

Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting

1 code implementation22 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?

Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model Selection

1 code implementation8 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.

Benchmarking Epidemiology +1

A unified machine learning approach to time series forecasting applied to demand at emergency departments

no code implementations13 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.

BIG-bench Machine Learning Time Series +1

A joint bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA

2 code implementations16 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

$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC

2 code implementations17 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).

Computational Efficiency Gaussian Processes +2

Spatial Analysis Made Easy with Linear Regression and Kernels

no code implementations22 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.

regression

Stochastic Optimal Control of Epidemic Processes in Networks

no code implementations30 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.

Point Processes

Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation

no code implementations10 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.

Spatial Interpolation

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