  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.
