Simulation of the COVID-19 pandemic on the social network of Slovenia: estimating the intrinsic forecast uncertainty

TL;DR

This study models COVID-19 spread in Slovenia using a large social network to quantify forecast uncertainty, highlighting the impact of biological and social factors on epidemic predictions.

Contribution

It introduces a detailed social network-based transmission model with ensemble simulations to estimate intrinsic forecast uncertainty for COVID-19 in Slovenia.

Findings

Infection spreads twice as likely within households/elderly centers.

Uncertainty mainly stems from virus biology in uncontrolled epidemics.

Social network randomness dominates forecast uncertainty in controlled epidemics.

Abstract

In the article a virus transmission model is constructed on a simplified social network. The social network consists of more than 2 million nodes, each representing an inhabitant of Slovenia. The nodes are organised and interconnected according to the real household and elderly-care center distribution, while their connections outside these clusters are semi-randomly distributed and fully-linked. The virus spread model is coupled to the disease progression model. The ensemble approach with the perturbed transmission and disease parameters is used to quantify the ensemble spread, a proxy for the forecast uncertainty. The presented ongoing forecasts of COVID-19 epidemic in Slovenia are compared with the collected Slovenian data. Results show that infection is currently twice more likely to transmit within households/elderly care centers than outside them. We use an ensemble of simulations (N = 1000) to inversely obtain posterior distributions of model parameters and to estimate the COVID-19 forecast uncertainty. We found that in the uncontrolled epidemic, the intrinsic uncertainty mostly originates from the uncertainty of the virus biology, i.e. its reproductive number. In the controlled epidemic with low ratio of infected population, the randomness of the social network becomes the major source of forecast uncertainty, particularly for the short-range forecasts. Social-network-based models are thus essential for improving epidemics forecasting.