Simulating nationwide realistic individual movements with a detailed geographical structure can help optimize public health policies. However, existing tools have limited resolution or can only account for a limited number of agents. We introduce Epidemap, a new framework that can capture the daily movement of more than 60 million people in a country at a building-level resolution in a realistic and computationally efficient way. By applying it to the case of an infectious disease spreading in France, we uncover hitherto neglected effects, such as the emergence of two distinct peaks in the daily number of cases or the importance of local density in the timing of arrival of the epidemic. Finally, we show that the importance of super-spreading events strongly varies over time.

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Background The COVID-19 pandemic emphasised the importance of access to reliable real-time forecasts for key epidemiological indicators. Given the strong heterogeneity between regions, providing forecasts at the local level is essential for health professionals. Methods We developed a SARS-CoV-2 transmission model in France, COVIDici, that performs parameter estimation using up-to-date vaccination coverage and hospital data to provide forecasts up to a four-week horizon based on the current epidemic trend. We present the model, its associated online tool and perform a retrospective evaluation of the forecasts provided from January to December 2021 by comparing to three standard statistical forecasting methods (auto-regression, exponential smoothing, and ARIMA) at the national and regional levels. Results COVIDici allowed simultaneous real-time visualisation of several indicators of the COVID-19 epidemic at the sub-national level. For anticipating risk of critical care unit overload, it performed worse compared to the baseline methods for forecasts under the three-week horizon, but had better point forecasts at the longest horizons (e.g. four weeks) for 8 of the 13 regions considered depending on the metric. Conclusions Effective communication between modelers and clinicians is essential for utilising forecasts for health care planning. Online visualisation tools and consideration of how metrics can be affected by distortion from non-pharmaceutical government interventions facilitate this dialogue.