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

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.

Intrinsic Randomness in Epidemic Modelling Beyond Statistical Uncertainty

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).

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

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.

Epidemiology Probabilistic Programming

Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting

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?

Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update

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