Simulation of the COVID-19 epidemic on the social network of Slovenia: Estimating the intrinsic forecast uncertainty

• Published in: PloS one 2020-08, Vol.15 (8), p.e0238090-e0238090
• Main Authors: Zaplotnik, Žiga, Gavrić, Aleksandar, Medic, Luka
• Format: Article
• Language: English
• Subjects: Basic Reproduction Number; Betacoronavirus; Computer and Information Sciences; Computer Simulation; Coronavirus Infections - epidemiology; Coronavirus Infections - transmission; Coronaviruses; COVID-19; Data assimilation; Data collection; Disease control; Disease Progression; Disease transmission; Epidemics; Family Characteristics; Forecasting; Forecasts and trends; Households; Humans; Mathematical models; Medicine and Health Sciences; Models, Theoretical; Nodes; Older people; Pandemics; Parameter uncertainty; People and places; Physical Sciences; Pneumonia, Viral - epidemiology; Pneumonia, Viral - transmission; SARS-CoV-2; Slovenia; Slovenia - epidemiology; Social Networking; Social networks; Social organization; Social Sciences; Uncertainty; Viral diseases; Viruses
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0238090

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 undirected. 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 at the end of the first epidemic wave, the infection was twice more likely to transmit within households/elderly care centers than outside them. We use an ensemble of simulations (N = 1000) and data assimilation approach to estimate the COVID-19 forecast uncertainty and to inversely obtain posterior distributions of model parameters. We found that in the uncontrolled epidemic, the intrinsic uncertainty mostly originates from the uncertainty of the virus biology, i.e. its reproduction 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. Virus transmission models with accurate social network models are thus essential for improving epidemics forecasting.