COVFlow: virus phylodynamics analyses from selected SARS-CoV-2 sequences

Phylodynamic analyses generate important and timely data to optimise public health response to SARS-CoV-2 outbreaks and epidemics. However, their implementation is hampered by the massive amount of sequence data and the difficulty to parameterise dedicated software packages. We introduce the COVFlow pipeline, accessible at https://gitlab.in2p3.fr/ete/CoV-flow, which allows a user to select sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID) database according to user-specified criteria, to perform basic phylogenetic analyses, and to produce an XML file to be run in the Beast2 software package. We illustrate the potential of this tool by studying two sets of sequences from the Delta variant in two French regions. This pipeline can facilitate the use of virus sequence data at the local level, for instance, to track the dynamics of a particular lineage or variant in a region of interest.

Modelling long-term COVID-19 hospital admission dynamics using immune protection waning data

Immune waning is key to the timely anticipation of COVID-19 long-term dynamics. We assess the impact of periodic vaccination campaigns using a compartmental epidemiological model with embedded multiple age structures and empiric time-dependent vaccine protection kinetics. Despite the uncertainty inherent to such scenarios, we show that vaccination campaigns decreases the yearly number of COVID-19 admissions. However, especially if restricted to individuals over 60 years old, vaccination on its own seems insufficient to prevent thousands of hospital admissions and it suffers the comparison with non-pharmaceutical interventions aimed at decreasing infection transmission. The combination of such interventions and vaccination campaigns appear to provide the greatest reduction in hospital admissions.

From Delta to Omicron: analysing the SARS-CoV-2 epidemic in France using variant-specific screening tests (September 1 to December 18, 2021)

We analysed 131,478 SARS-CoV-2 variant screening tests performed in France from September 1st to December 18, 2021. Tests consistent with the presence of the Omicron variant exhibit significantly higher cycle threshold Ct values, which could indicate lower amounts of virus genetic material. We estimate that the transmission advantage of the Omicron variant over the Delta variant is +105% (95% confidence interval: 96-114%). Based on these data, we use mechanistic mathematical modelling to explore scenarios for early 2022.

The importance of the population age-structure: insights from Covid-19 dynamics model structured by age, time since infection and acquired immunity

The Covid-19 pandemic outbreak was followed by a huge amount of modelling studies in order to rapidly gain insights to implement the best public health policies. Most of these compartmental models involved ordinary differential equations (ODEs) systems. Such a formalism implicitly assumes that the time spent in each compartment does not depend on the time already spent in it, which is at odds with the clinical data. To overcome this "memoryless" issue, a widely used solution is to increase and chain the number of compartments of a unique reality (e.g. have infected individual move between several compartments). This allows for greater heterogeneity and thus be closer to the observed situation, but also tends to make the whole model more difficult to apprehend and parameterize. We develop a non-Markovian alternative formalism based on partial differential equations (PDEs) instead of ODEs, which, by construction, provides a memory structure for each compartment thereby allowing us to limit the number of compartments. We apply our model to the French 2021 SARS-CoV-2 epidemic and, while accounting for vaccine-induced and natural immunity, we analyse and determine the major components that contributed to the Covid-19 hospital admissions. The results indicate that the observed vaccination rate alone is not enough to control the epidemic, and a global sensitivity analysis highlights a huge uncertainty attributable to the age-structured contact matrix. Our study shows the flexibility and robustness of PDE formalism to capture national COVID-19 dynamics and opens perspectives to study medium or long-term scenarios involving immune waning or virus evolution.

Spread of SARS-CoV-2 Delta variant infections bearing the S:E484Q and S:T95I mutations in July and August 2021 in France

Analysing 92,598 variant screening tests performed on SARS-CoV-2 positive samples collected in France between 1 July and 31 August 2021 shows an increase of Kappa-like infections. Full genome sequencing reveals that these correspond to Delta variants bearing the S:E484Q mutation. Most of these sequences belong to a phylogenetic cluster and also bear the S:T95I mutation. Further monitoring is needed to determine if this trend is driven by undocumented superspreading events or an early signal of future viral evolutionary dynamics.

Anticipating the hospital burden of future COVID-19 epidemic waves

Forecasting SARS-CoV-2 epidemic trends with confidence more than a few weeks ahead is almost impossible as these entirely depend on political decisions. We address this problem by investigating the consequences for the health system of an epidemic wave of a given size. This approach yields semi-quantitative results that depend on the proportion of the population already infected and vaccinated. We introduce the COVimpact software, which allows users to visualise estimated numbers of ICU admissions, deaths, and infections stratified by age class at the French departmental, regional, or national level caused by the wave. We illustrate the usefulness of our approach by showing that for France, even with a 95% vaccination coverage, the current vaccine efficiency against the delta variant would make a large epidemic wave infecting 25% of the population difficult to sustain for the current hospital bed occupancy capacity. Overall, using the final epidemic wave size and ignoring detailed epidemiological dynamics yields valuable and practical insights to optimise public health response to epidemics.

Predicting COVID-19 incidence in French hospitals using human contact network analytics

Coronavirus disease (COVID-19) was detected in Wuhan, China in 2019 and spread worldwide within few weeks. The COVID-19 epidemic started to gain traction in France in March 2020. Sub-national hospital admissions and deaths were then recorded daily and served as the main policy indicators. Concurrently, mobile phone positioning data have been curated to determine the frequency of users being colocalized within a given distance. Contrarily to individual tracking data, these can provide a proxy of human contact networks between subnational administrative units. Motivated by numerous studies correlating human mobility data and disease incidence, we developed predictive time series models of hospital incidence between July 2020 and April 2021. Adding human contact network analytics such as clustering coefficients, contact network strength, null links or curvature as regressors, we found that predictions can be improved substantially (more than 50%) both at the national and sub-national for up to two weeks. Our sub-national analysis also revealed the importance of spatial structure, as incidence in colocalized administrative units improved predictions. This original application of network analytics from co-localisation data to epidemic spread opens new perspectives for epidemics forecasting and public health. HighlightsO_LIWe use novel human contact network analytics based on colocation data of mobile app users to follow the dynamics of disease incidence and interventions of COVID-19 in France. C_LIO_LITime series predictions of hospital incidence are greatly improved by adding these analytics as regressors. C_LIO_LISub-national analysis highlights both spatial correlations of incidence and network analytics to obtain high-precision predictions. C_LI

Rapid spread of the SARS-CoV-2 δ variant in the area of Paris (France) in June 2021

Analysing 9,030 variant-specific tests performed on SARS-CoV-2 positive samples collected in France between 31 May and 21 June 2021 reveals a rapid growth of the {delta} variant in 3 French regions. The next weeks will prove decisive but the magnitude of the estimated transmission advantages could represent a major challenge for public health authorities.

Inferring SARS-CoV-2 variant within-host kinetics

Since early 2021, SARS-CoV-2 variants of concern (VOCs) have been causing epidemic rebounds in many countries. Their properties are well characterised at the epidemiological level but the potential underlying within-host determinants remain poorly understood. We analyse a longitudinal cohort of 6,944 individuals with 14,304 cycle threshold (Ct) values of qPCR VOC screening tests performed in the general population and hospitals in France between February 6 and August 21, 2021. To convert Ct values into numbers of virus copies, we performed an additional analysis using droplet digital PCR (ddPCR). We find that the number of viral genome copies reaches a higher peak value and has a slower decay rate in infections caused by Alpha variant compared to that caused by historical lineages. Following the evidence that viral genome copies in upper respiratory tract swabs are informative on contagiousness, we show that the kinetics of the Alpha variant translate into significantly higher transmission potentials, especially in older populations. Finally, comparing infections caused by the Alpha and Delta variants, we find no significant difference in the peak viral copy number. These results highlight that some of the differences between variants may be detected in virus load variations.

The SARS-CoV-2 B.1.351/V2 variant is outgrowing the B.1.1.7/V1 variant in French regions in April 2021

SARS-CoV-2 variants threaten our ability to control COVID-19 epidemics. We analyzed 36,590 variant-specific RT-PCR tests performed on samples collected between April 12 and May 7, 2021 in France to compare variant spread. Contrarily to January to March 2021, we found that the V2 variant had a significant transmission advantage over V1 in some regions (15.1 to 16.1% in Ile-de-France and 16.1 to 18.8% in Hauts-de-France). This shift in transmission advantage is consistent with the immune evasion abilities of V2 and the high levels of immunization in these regions.

SARS-CoV-2 variants of concern are associated with lower RT-PCR amplification cycles between January and March 2021 in France

SARS-CoV-2 variants raise concern regarding the mortality caused by COVID-19 epidemics. We analyse 88,375 cycle amplification (Ct) values from variant-specific RT-PCR tests performed between January 26 and March 13, 2021. We estimate that on March 12, nearly 85% of the infections were caused by the V1 variant and that its transmission advantage over wild type strains was between 38 and 44%. We also find that tests positive for V1 and V2/V3 variants exhibit significantly lower cycle threshold (Ct) values.

Epidemiological and clinical insights from SARS-CoV-2 RT-PCR cycle amplification values

The SARS-CoV-2 pandemic has led to an unprecedented daily use of molecular RT-PCR tests. These tests are interpreted qualitatively for diagnosis, and the relevance of the test result intensity, i.e. the number of amplification cycles (Ct), is debated because of strong potential biases. We analyze a national database of tests performed on more than 2 million individuals between January and November 2020. Although we find Ct values to vary depending on the testing laboratory or the assay used, we detect strong significant trends with patient age, number of days after symptoms onset, or the state of the epidemic (the temporal reproduction number) at the time of the test. These results suggest that Ct values can be used to improve short-term predictions for epidemic surveillance.

Rapid SARS-CoV-2 variants spread detected in France using specific RT-PCR testing

SARS-CoV-2 variants raise major concerns regarding the control of COVID-19 epidemics. We analyse 40,000 specific RT-PCR tests performed on SARS-CoV-2-positive samples collected between Jan 26 and Feb 16, 2021. We find a high transmission advantage of variants and show that their spread in the country is more advanced than anticipated.

Estimating dates of origin and end of COVID-19 epidemics

Estimating the date at which an epidemic started in a country and the date at which it can end depending on interventions intensity are important to guide public health responses. Both are potentially shaped by similar factors including stochasticity (due to small population sizes), superspreading events, and memory effects (the fact that the occurrence of some events, e.g. recovering from an infection, depend on the past, e.g. the number of days since the infection). Focusing on COVID-19 epidemics, we develop and analyse mathematical models to explore how these three factors may affect early and final epidemic dynamics. Regarding the date of origin, we find limited effects on the mean estimates, but strong effects on their variances. Regarding the date of extinction following lockdown onset, mean values decrease with stochasticity or with the presence of superspreading events. These results underline the importance of accounting for heterogeneity in infection history and transmission patterns to accurately capture early and late epidemic dynamics.

Rt2: computing and visualising COVID-19 epidemics temporal reproduction number

Analysing the spread of COVID-19 epidemics in a timely manner is essential for public health authorities. However, raw numbers may be misleading because of spatial and temporal variations. We introduce Rt2, an R-program with a shiny interface, which uses incidence data, i.e. number of new cases per day, to compute variations in the temporal reproduction number ([R]t), which corresponds to the average number of secondary infections caused by an infected person. This number is computed with the R0 package, which better captures past variations, and the EpiEstim package, which provides a more accurate estimate of current values. [R]t can be computed in different countries using either the daily number of new cases or of deaths. For France, these numbers can also be computed at the regional and departmental level using also daily numbers of hospital and ICU admissions. Finally, in addition to [R]t, we represent the incidence using a one-week sliding window to buffer daily variations. Overall, Rt2 provides an accurate and timely overview of the state and speed of spread of COVID-19 epidemics at different scales, using different metrics.

Wearing masks and establishing COVID-19 areas reduces secondary attack risk in nursing homes

BackgroundCOVID-19 is spreading rapidly in nursing homes (NHs). It is urgent to evaluate the effect of infection prevention and control (IPC) measures to reduce COVID spreading. MethodsWe analysed COVID-19 outbreaks in 12 NH using rRT-PCR for SARS-CoV-2. We estimated secondary attack risks (SARs) and identified cofactors associated with the proportion of infected residents. ResultsThe SAR was below 5%, suggesting a high efficiency of IPC measures. Mask-wearing or establishment of COVID-19 zones for infected residents were associated with lower SAR. ConclusionsWearing masks and isolating potentially infected residents appear to limit SARS-CoV-2 spread in nursing homes.

Early phylodynamics analysis of the COVID-19 epidemics in France

France was one of the first countries to be reached by the COVID-19 pandemic. Here, we analyse 196 SARS-Cov-2 genomes collected between Jan 24 and Mar 24 2020, and perform a phylodynamics analysis. In particular, we analyse the doubling time, reproduction number ([R]t) and infection duration associated with the epidemic wave that was detected in incidence data starting from Feb 27. Different models suggest a slowing down of the epidemic in Mar, which would be consistent with the implementation of the national lock-down on Mar 17. The inferred distributions for the effective infection duration and[R] t are in line with those estimated from contact tracing data. Finally, based on the available sequence data, we estimate that the French epidemic wave originated between mid-Jan and early Feb. Overall, this analysis shows the potential to use sequence genomic data to inform public health decisions in an epidemic crisis context and calls for further analyses with denser sampling.

Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic

In an epidemic, individuals can widely differ in the way they spread the infection, for instance depending on their age or on the number of days they have been infected for. The latter allows to take into account the variation of infectiousness as a function of time since infection. In the absence of pharmaceutical interventions such as a vaccine or treatment, non-pharmaceutical interventions (e.g. social distancing) are of great importance to mitigate the pandemic. We propose a model with a double continuous structure by host age and time since infection. By applying optimal control theory to our age-structured model, we identify a solution minimizing deaths and costs associated with the implementation of the control strategy itself. This strategy depends on the age heterogeneity between individuals and consists in a relatively high isolation intensity over the older populations during a hundred days, followed by a steady decrease in a way that depends on the cost associated to a such control. The isolation of the younger population is weaker and occurs only if the cost associated with the control is relatively low. We show that the optimal control strategy strongly outperforms other strategies such as uniform constant control over the whole populations or over its younger fraction. These results bring new facts the debate about age-based control interventions and open promising avenues of research, for instance of age-based contact tracing.

Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France

SARS-Cov-2 virus has spread over the world creating one of the fastest pandemics ever. The absence of immunity, asymptomatic transmission, and the relatively high level of virulence of the COVID-19 infection it causes led to a massive flow of patients in intensive care units (ICU). This unprecedented situation calls for rapid and accurate mathematical models to best inform public health policies. We develop an original parsimonious model that accounts for the effect of the age of infection on the natural history of the disease. Analysing the ongoing COVID-19 in France, we estimate the value of the key epidemiological parameters, such as the basic reproduction number [Formula], and the efficiency of the national control strategy. We then use our deterministic model to explore several scenarios posterior to lock-down lifting and compare the efficiency of non pharmaceutical interventions (NPI) described in the literature.

Optimal COVID-19 epidemic control until vaccine deployment

Since Dec 2019, the COVID-19 epidemic has spread over the globe creating one of the greatest pandemics ever witnessed. This epidemic wave will only begin to roll back once a critical proportion of the population is immunised, either by mounting natural immunity following infection, or by vaccination. The latter option can minimise the cost in terms of human lives but it requires to wait until a safe and efficient vaccine is developed, a period estimated to last at least 18 months. In this work, we use optimal control theory to explore the best strategy to implement while waiting for the vaccine. We seek a solution minimizing deaths and costs due to the implementation of the control strategy itself. We find that such a solution leads to an increasing level of control with a maximum reached near the 16th month of the epidemics and a steady decrease until vaccine deployment. The average containment level is approximately 50% during the 25-months period for vaccine deployment. This strategy strongly out-performs others with constant or cycling allocations of the same amount of resources to control the outbreak. This work opens new perspectives to mitigate the effects of the ongoing COVID-19 pandemics, and be used as a proof-of-concept in using mathematical modelling techniques to enlighten decision making and public health management in the early times of an outbreak.