Paper 1 ‘Bayesian semi-mechanistic modelling of COVID-19: identifiability, sensitivity, and policy implications’:

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 concerning the effects of non-pharmaceutical interventions (NPIs) in reducing COVID-19 transmission in 11 European countries. The model parameterizes the time varying reproduction number R_t through a regression framework in which covariates can e.g. be governmental interventions or changes in mobility patterns. This allows a joint fit across regions and partial pooling to share strength. This innovation was critical to our timely estimates of the impact of lockdown and other NPIs in the European epidemics, whose validity was borne out by the subsequent course of the epidemic. Our framework provides a fully generative model for latent infections and observations deriving from them, including deaths, cases, hospitalizations, ICU admissions and seroprevalence surveys. One issue surrounding our model’s use during the COVID-19 pandemic is the confounded nature of NPIs and mobility. We use our framework to explore this issue. We have open sourced an R package epidemia implementing our approach in Stan. Versions of the model are used by New York State, Tennessee and Scotland to estimate the current situation and make policy decisions.
 

Paper 2: ‘A sequential Monte Carlo approach for estimation of timevarying reproduction numbers for Covid-19’

The Covid-19 pandemic has required most countries to implement complex sequences of non-pharmaceutical interventions, with the aim of controlling the transmission of the virus in the population. To be able to take rapid decisions, a detailed understanding of the current situation is necessary. Estimates of time-varying, instantaneous reproduction numbers represent a way to quantify the viral transmission in real time. They are often defined through a mathematical compartmental model of the epidemic, like a stochastic SEIR model, whose parameters must be estimated from multiple time series of epidemiological data. Because of very high dimensional parameter spaces (partly due to the stochasticity in the spread models) and incomplete and delayed data, inference is very challenging. We propose a state space formalisation of the model and a sequential Monte Carlo approach which allow to estimate a daily-varying reproduction number for the Covid-19 epidemic in Norway with sufficient precision, on the basis of daily hospitalisation and positive test incidences. The method was in regular use in Norway during the pandemics and appears to be a powerful instrument for epidemic monitoring and management.

The preprints are available here.

Paper 1: ‘Bayesian semi-mechanistic modelling of COVID-19: identifiability, sensitivity, and policy implications’
 
Authors: Samir Bhatt*; Imperial College London
Neil Ferguson*; Imperial College London
Seth Flaxman#; Imperial College London
Axel Gandy#; Imperial College London
Swapnil Mishra*; Imperial College London,
James Scott#; Imperial College London, Department of Mathematics
 
*MRC Centre for Global Infectious Disease Analysis
# Department of Mathematics
 
 
Paper 2: ‘A sequential Monte Carlo approach for estimation of timevarying reproduction numbers for Covid-19’
 
Authors: Geir Storvik; University of Oslo, Department of Mathematics
Alfonso Diz-Lois Palomares; Norwegian Institute of Public Health
Solveig Engebretsen; Norwegian Computing Centre
Gunnar Rø; Norwegian Institute of Public Health
Kenth Engo-Monsen; Telenor Research
Anja Kristoffersen; Norwegian Institute of Public Health
Birgitte De Blasio; Norwegian Institute of Public Health; University of Oslo, Oslo Centre for Biostatistics and Epidemiology
Arnoldo Frigessi; University of Oslo, Oslo Centre for Biostatistics and Epidemiology ; Oslo University Hospital
 

Judith Shorten for RSS Discussion Meetings Committee