After widespread transmission of SARS-CoV-2 in EU/EEA countries and the UK over several weeks, the COVID-19 epidemic reached its peak in most countries in April or early May 2020. Some countries have since experienced a sustained decrease in the number of reported cases, progressively reaching the level of transmission reported during the first week of the outbreak. Due to this decrease in transmission and improvements in epidemiological surveillance and healthcare capacity, a number of countries have decided to discontinue several non-pharmaceutical interventions and now plan to gradually phase out their ‘stay-at-home’ policies.

Mathematical modelling of COVID-19 transmission can be used to better analyse the epidemic development in a population over time, produce projections, and inform public health decision-making on interventions. It is particularly useful for the evaluation of public health measures, notably to understand the expected impact of their implementation or release on disease transmission-related indicators. The mathematical modelling approach also allows for the quantification of the uncertainty associated with these estimations and projections. In this report, a dynamic compartmental model of COVID-19 is presented. It aims to provide a short-term 30-day forecast of the expected number of COVID-19 cases, deaths and hospitalised cases (including general hospital ward and intensive care unit) under a set of assumptions. In this first analysis, the baseline scenario corresponds to a ‘status quo’ in which all control measures in place on 2 May 2020 will be continued until the end of the projection period (7 June 2020). The model is based on the epidemiological data and scientific evidence available at the time of publication. Further developments are expected as new information and epidemiological data become available.

The model was developed at ECDC and applied at a national level for EU/EEA countries and the UK. When interpreting predictions of mathematical models for emerging diseases, it is essential to keep in mind the underlying assumptions, limitations and uncertainties resulting from gaps in scientific knowledge and in available data. The inherent sources of uncertainty and the limitations of the mathematical modelling approach taken here are discussed and should be considered when interpreting the results and making comparisons with other mathematical models of COVID-19 transmission.

An assessment of the risk associated with the COVID-19 epidemic and the response strategies applied or envisaged should be based on a comprehensive analysis taking in consideration current uncertainties, the specific epidemiological situation in each country, and outputs of models according to new scientific evidences. Future work in this area intends to promote data sharing and operational forecasting through an ‘ensemble modelling’ approach. This approach combines predictions from different mathematical models to improve on a single‐model forecast, offering more accurate predictions of epidemic trends and clarifying the uncertainties associated with these predictions.