Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model

Abstract

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.

COVID-19 İnsidans Oranlarını Modelleme ve Öngörümleme: Fransa'da Gözetim Başlangıcından Beri Akut Solunum Enfeksiyonları (ARI) Zaman Serisi Analizi

“…Time series forecasting of the incidence rates of ARI and COVID-19 in France faces several challenges. One challenge is the heterogeneity between regions, which highlights the need for local-level forecasts [19]. Another challenge is the non-seasonal and non-stationary nature of the pandemic, requiring specialized forecasting methods [20].…”

Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model

“…The scripts and data used to perform the analysis and generate this manuscript are available on GitLab (https://gitlab.in2p3.fr/ete/covidici_public) and archived in Zenodo [22]. The current section provides a overview of the main methods used in both the implementation of COVIDici, while further technical details regarding the implementation of COVIDici as well as all the baseline models is accessible in S1 Text.…”

“…An automated cluster computing workflow refit the COVIDici model using daily updates of hospital, vaccination and testing data downloaded from the SI-VIC database, allowing a Shiny web application (see link in Introduction or [22] for source code) to communicate real-time results to the public. The original 2021 production version permitted users to visualise the combined past and future model fit by national, regional or departmental administrative unit for multiple epidemiological parameters, including ICU admissions, ICU occupancy, mortality (cumulative and daily), temporal reproductive number (R t ), infections (cumulative, daily and current), vaccination coverage and incidence for positive tests.…”