Estimates of the proportion of SARS-CoV-2 infected individuals in Sweden

TL;DR

This paper presents a Bayesian SEIR model with a Gaussian process prior to estimate the proportion of SARS-CoV-2 infected individuals in Sweden, integrating death data and infection snapshots to improve regional estimates during the COVID-19 pandemic.

Contribution

It introduces a novel Bayesian SEIR modeling approach with time-varying contact rates and parameter sharing to estimate infection proportions across regions.

Findings

Estimated 13.5% infected in Stockholm by May 15, 2020

Infection estimates range from 2.5% to 15.6% in other regions

Parameter uncertainty affects cumulative infection estimates more than daily death predictions

Abstract

In this paper a Bayesian SEIR model is studied to estimate the proportion of the population infected with SARS-CoV-2, the virus responsible for COVID-19. To capture heterogeneity in the population and the effect of interventions to reduce the rate of epidemic spread, the model uses a time-varying contact rate, whose logarithm has a Gaussian process prior. A Poisson point process is used to model the occurrence of deaths due to COVID-19 and the model is calibrated using data of daily death counts in combination with a snapshot of the the proportion of individuals with an active infection, performed in Stockholm in late March. The methodology is applied to regions in Sweden. The results show that the estimated proportion of the population who has been infected is around 13.5% in Stockholm, by 2020-05-15, and ranges between 2.5% - 15.6% in the other investigated regions. In Stockholm where the peak of daily death counts is likely behind us, parameter uncertainty does not heavily influence the expected daily number of deaths, nor the expected cumulative number of deaths. It does, however, impact the estimated cumulative number of infected individuals. In the other regions, where random sampling of the number of active infections is not available, parameter sharing is used to improve estimates, but the parameter uncertainty remains substantial.