Background The novel coronavirus 2019-nCoV outbreak which was reported by World Health Organization (WHO) at 21th of January 2020 situation report spreads around the globe during next few weeks and causes serious health, social and financial issues. The primary epicentre was reported is Wuhan City, China and together with other affected regions in Asia remains the most monitored area. On the other hand, the European region was experiencing only a few cases until February 29th when the number of confirmed cases reached one thousand infections. Within a few days during the 8th of March the Italian most infected places were locked by restrictive quarantine. In this study we present probabilistic model to track the infection spread trough the European regions based on the model adjusted with worldwide data-sets.
Methods To track and predict the officially reported number of cases we developed probabilistic model with the ability to predict the number of confirmed cases one day ahead. The model has internal/hidden parameters which cannot be directly observed such as the number of infectious individuals or transmissibility rate which is represented by reproduction number $R_t$. The model starts with assumed number of infectious individuals and reproduction number $R_0$ and as more data are gathered from data-sets during time the internal parameters are further estimated. The model is updated each day as the new number of confirmed cases are reported. Particle filters algorithm is used as the back-end method due to its ability to handle multi-modal data distribution.
Results Presented results show the performance of the probabilistic model which is able to handle short-term prediction (in number of days) of confirmed and recovered cases. The estimated reproduction rate is further used in long-term simulation which fits the data gathered world-wide. The one day prediction error is below 5\% of nominal value and as we are located in Czechia the prediction model for our region was tested 4 days forward with the same error. The overal performance of the model was compared to data gathered from China due to the longer history of measurements.
Conclusion We have proposed a probabilistic model which is used with particle filters to predict next moves of the confirmed cases. As a side effect we were able to model internal parameters as reproduction number or recovery rate which can be used for running long-term simulations of virus spreading.
