84 citations · 143 across the 16 of their papers we have counts for
16 papers
KidSat: satellite imagery to map childhood poverty dataset and benchmark
Makkunda Sharma, Fan Yang, Duy-Nhat Vo +6
Satellite imagery has emerged as an important tool to analyse demographic, health, and development indicators. While various deep learning models have been built for these tasks, e…
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya
Elizaveta Semenova, Swapnil Mishra, Samir Bhatt +2
Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the st…
A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models
Nicolas Banholzer, Thomas Mellan, H Juliette T Unwin +3
Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. While different models for short-term forecasting h…
The interaction of transmission intensity, mortality, and the economy: a retrospective analysis of the COVID-19 pandemic
Christian Morgenstern, Daniel J. Laydon, Charles Whittaker +4
The COVID-19 pandemic has caused over 6.4 million registered deaths to date and has had a profound impact on economic activity. Here, we study the interaction of transmission, mort…
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Xenia Miscouridou, Samir Bhatt, George Mohler +2
Hawkes processes are point process models that have been used to capture self-excitatory behavior in social interactions, neural activity, earthquakes and viral epidemics. They can…
Intrinsic Randomness in Epidemic Modelling Beyond Statistical Uncertainty
Matthew J. Penn, Daniel J. Laydon, Joseph Penn +7
Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease r…
Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes
James A. Scott, Axel Gandy, Swapnil Mishra +4
This article introduces epidemia, an R package for Bayesian, regression-oriented modeling of infectious diseases. The implemented models define a likelihood for all observed data w…
PriorVAE: Encoding spatial priors with VAEs for small-area estimation
Elizaveta Semenova, Yidan Xu, Adam Howes +4
Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical m…
Unifying incidence and prevalence under a time-varying general branching process
Mikko S. Pakkanen, Xenia Miscouridou, Matthew J. Penn +5
Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a ti…
Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting
Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra +7
Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An impor…
Semi-Mechanistic Bayesian Modeling of COVID-19 with Renewal Processes
Samir Bhatt, Neil Ferguson, Seth Flaxman +3
We propose a general Bayesian approach to modeling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular an analysis c…
Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model Selection
Iwona Hawryluk, Swapnil Mishra, Seth Flaxman +2
Model selection is a fundamental part of the applied Bayesian statistical methodology. Metrics such as the Akaike Information Criterion are commonly used in practice to select mode…
Inference of COVID-19 epidemiological distributions from Brazilian hospital data
Iwona Hawryluk, Thomas A. Mellan, Henrique H. Hoeltgebaum +8
Knowing COVID-19 epidemiological distributions, such as the time from patient admission to death, is directly relevant to effective primary and secondary care planning, and moreove…
A unified machine learning approach to time series forecasting applied to demand at emergency departments
Michaela A. C. Vollmer, Ben Glampson, Thomas A. Mellan +7
There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily r…
On the derivation of the renewal equation from an age-dependent branching process: an epidemic modelling perspective
Swapnil Mishra, Tresnia Berah, Thomas A. Mellan +6
Renewal processes are a popular approach used in modelling infectious disease outbreaks. In a renewal process, previous infections give rise to future infections. However, while th…
Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
Seth Flaxman, Swapnil Mishra, Axel Gandy +20
Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics. In response, many European countries have imple…