Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions

Abstract

In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.

The impact of spatial connectivity on NPIs effectiveness

“…We integrated seasonality in the regional transmissibility (see Model subsection) based on estimates provided in Ref. 29 . These estimates quantify the impact of seasonal climatic conditions on transmission rate based on daily data from the National Oceanic and Atmospheric Administration ( Figure S12 ).…”

“…Estimates for seasonal transmissibility were obtained from the authors of Ref. 29 . Vaccination data are available from the French Government data hub (https://www.data.gouv.fr/en/datasets/donnees-relatives-aux-personnes-vaccinees-contre-la-covid-19-1/); the normalcy index is available from The Economist (https://www.economist.com/graphic-detail/tracking-thereturn-to-normalcy-after-covid-19); and Alpha variant penetration data are published in the scientific literature (https://pubmed.ncbi.nlm.nih.gov/33663644/).…”

“…Hospitalization data were obtained from the SIVIC dataset 57 . Estimates for seasonal transmissibility were obtained from the authors of Ref 29. .…”

Estimating the population effectiveness of interventions against COVID-19 in France: a modelling study

“…Second, we included a linear mixed effects model that represents transmission as a time-varying variable as a function of a basic transmission rate, NPIs, weather, and VoCs, and which accounts for inter-departmental variations, as in Collin et al. 9 Third, we included the effects of vaccination as the population vaccine effect against transmission ( e vI ) and the population vaccine effect against hospitalization ( e vH ) directly in the compartmental model. Lastly, we explicitly modelled the effect of VoCs on transmission, risk of hospitalization, and vaccine efficacy.…”

A novel, scenario-based approach to comparing non-pharmaceutical intervention strategies across nations

“…Furthermore, several studies have compared how NPI effectiveness varied among nations (Bo et al [9], Liu et al [10]), relying on data sets such as the Johns Hopkins Coronavirus Resource Center [13] or the Oxford COVID-19 Government Response Tracker [14]. Other studies have assessed NPI impacts in selected countries [15], on a regional scale [11, 16], or examined effectiveness in individual countries [17, 18]. Overall, NPIs such as school closures, restricting gatherings, banning public events or mandating masks were found to be effective by a range of studies [10, 19].…”