Seroprevalence of human herpes viruses in France, 2018-2022: a multilevel regression and poststratification approach.

BACKGROUND: Information related to herpes simplex virus 1 and 2 (HSV-1 and 2), varicella-zoster virus (VZV), Epstein-Barr virus (EBV), and cytomegalovirus (CMV) seroprevalence in France is either lacking, incomplete, or outdated, despite their public health burden. METHOD: We used routinely collected serological data between 2018 and 2022 to estimate HSV-1, HSV-2, VZV, EBV, and CMV seroprevalence in France. To account for demographic differences between our analytic samples and the French population and get estimates for sparsely sampled districts and age classes, we used a multilevel regression and poststratification approach combined with Bayesian model averaging via stacking weights. RESULTS: The observed seroprevalence (number of positive tests/number of tests) were 64.6% (93,294/144,424), 16.9% (24,316/144,159), 93.0% (141,419/152,084), 83.4% (63,199/75, 781), and 49.0% (23,276/47,525), respectively, for HSV-1, HSV-2, VZV, EBV, and CMV. Between 2018 and 2022, France had a model-based average (equal-tailed interval at 95%) expected seroprevalence equal to 61.1% (60.7,61.5), 14.5% (14.2,14.81), 89.5% (89.3,89.8), 85.6% (85.2,86.0), and 50.5% (49.3,51.7), respectively, for HSV-1, HSV-2, VZV, EBV, and CMV infections. We found an almost certain lower expected seroprevalence in Metropolitan France than in overseas territories for all viruses but VZV, for which it was almost certainly greater. The expected seroprevalences were likely greater among females for all viruses. LIMITATIONS: Our results relied on the assumption that individuals were sampled at random conditionally to variables used to build the poststratification table. IMPLICATIONS: The analysis highlights spatial and demographic patterns in seroprevalence that should be considered for designing tailored public health policies.

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

Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics.

Understanding the role of mask-wearing during COVID-19 on the island of Ireland.

Non-pharmaceutical interventions have played a key role in managing the COVID-19 pandemic, but it is challenging to estimate their impacts on disease spread and outcomes. On the island of Ireland, population mobility restrictions were imposed during the first wave, but mask-wearing was not mandated until about six months into the pandemic. We use data on mask-wearing, mobility, and season, over the first year of the pandemic to predict independently the weekly infectious contact estimated by an epidemiological model. Using our models, we make counterfactual predictions of infectious contact, and ensuing hospitalizations, under a hypothetical intervention where 90% of the population wore masks from the beginning of community spread until the dates of the mask mandates. Over periods including the first wave of the pandemic, there were 1601 hospitalizations with COVID-19 in Northern Ireland and 1521 in the Republic of Ireland. Under the counterfactual mask-wearing scenario, we estimate 512 (95% CI 400, 730) and 344 (95% CI 266, 526) hospitalizations in the respective jurisdictions during the same periods. This could be partly due to other factors that were also changing over time.

Estimating time-dependent contact: a multi-strain epidemiological model of SARS-CoV-2 on the island of Ireland.

Mathematical modelling plays a key role in understanding and predicting the epidemiological dynamics of infectious diseases. We construct a flexible discrete-time model that incorporates multiple viral strains with different transmissibilities to estimate the changing patterns of human contact that generates new infections. Using a Bayesian approach, we fit the model to longitudinal data on hospitalisation with COVID-19 from the Republic of Ireland and Northern Ireland during the first year of the pandemic. We describe the estimated change in human contact in the context of government-mandated non-pharmaceutical interventions in the two jurisdictions on the island of Ireland. We take advantage of the fitted model to conduct counterfactual analyses exploring the impact of lockdown timing and introducing a novel, more transmissible variant. We found substantial differences in human contact between the two jurisdictions during periods of varied restriction easing and December holidays. Our counterfactual analyses reveal that implementing lockdowns earlier would have decreased subsequent hospitalisation substantially in most, but not all cases, and that an introduction of a more transmissible variant - without necessarily being more severe - can cause a large impact on the health care burden.

Intensive Care Unit activity in France from the national database between 2013 and 2019: More critically ill patients, shorter stay and lower mortality rate.

BACKGROUND: Knowledge of the occurrence and outcome of admissions to Intensive Care Units (ICU) over time is important to inform healthcare services planning. This observational study aims at describing the activity of French ICUs between 2013 and 2019. METHODS: Patient admission characteristics, organ dysfunction scores, therapies, ICU and hospital lengths of stay and case fatality were collected from the French National Hospital Database (population-based cohort). Logistic regression models were developed to investigate the association between age, sex, SAPS II, organ failure, and year of care on in-ICU case fatality. FINDINGS: Among 1,594,801 ICU admissions, the yearly ICU admission increased from 3.3 to 3.5 per year per 1000 inhabitants (bed occupancy rate between 83.4 and 84.3%). The mean admission SAPS II was 42 ± 22, with a gradual annual increase. The median lengths of stay in ICU and in hospital were 3 (interquartile range (IQR) = [1-7]) and 11 days (IQR = [6-21]), respectively, with a progressive decrease over time. The in-ICU and hospital mortality case fatalities decreased from 18.0% to 17.1% and from 21.1% to 19.9% between 2013 and 2019, respectively. Male sex, age, SAPS II score, and the occurrence of any organ failure were associated with a higher case fatality rate. After adjustment on age, sex, SAPS II and organ failure, in-ICU case fatality decreased in 2019 as compared to 2013 (adjusted Odds Ratio = 0.87 [95% confidence interval, 0.85-0.89]). INTERPRETATION: During the study, an increasing incidence of ICU admission was associated with higher severity of illness but lower in-ICU case fatality.

Analyzing and Modeling the Spread of SARS-CoV-2 Omicron Lineages BA.1 and BA.2, France, September 2021-February 2022.

We analyzed 324,734 SARS-CoV-2 variant screening tests from France enriched with 16,973 whole-genome sequences sampled during September 1, 2021-February 28, 2022. Results showed the estimated growth advantage of the Omicron variant over the Delta variant to be 105% (95% CI 96%-114%) and that of the BA.2 lineage over the BA.1 lineage to be 49% (95% CI 44%-52%). Quantitative PCR cycle threshold values were consistent with an increased ability of Omicron to generate breakthrough infections. Epidemiologic modeling shows that, in spite of its decreased virulence, the Omicron variant can generate important critical COVID-19 activity in hospitals in France. The magnitude of the BA.2 wave in hospitals depends on the level of relaxing of control measures but remains lower than that of BA.1 in median scenarios.

Variant-specific SARS-CoV-2 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 characterized at the epidemiological level but the potential underlying within-host determinants remain poorly understood. We analyze a longitudinal cohort of 6944 individuals with 14 304 cycle threshold (Ct) values of reverse-transcription quantitative polymerase chain reaction (RT-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.

Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020.

BackgroundThe COVID-19 pandemic has led to an unprecedented daily use of RT-PCR tests. These tests are interpreted qualitatively for diagnosis, and the relevance of the test result intensity, i.e. the number of quantification cycles (Cq), is debated because of strong potential biases.AimWe explored the possibility to use Cq values from SARS-CoV-2 screening tests to better understand the spread of an epidemic and to better understand the biology of the infection.MethodsWe used linear regression models to analyse a large database of 793,479 Cq values from tests performed on more than 2 million samples between 21 January and 30 November 2020, i.e. the first two pandemic waves. We performed time series analysis using autoregressive integrated moving average (ARIMA) models to estimate whether Cq data information improves short-term predictions of epidemiological dynamics.ResultsAlthough we found that the Cq values varied depending on the testing laboratory or the assay used, we detected strong significant trends associated 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. Furthermore, knowing the quartiles of the Cq distribution greatly reduced the error in predicting the temporal reproduction number of the COVID-19 epidemic.ConclusionOur results suggest that Cq values of screening tests performed in the general population generate testable hypotheses and help improve short-term predictions for epidemic surveillance.

Analysing different exposures identifies that wearing masks and establishing COVID-19 areas reduce secondary-attack risk in aged-care facilities.

BACKGROUND: The COVID-19 epidemic has spread rapidly within aged-care facilities (ACFs), where the infection-fatality ratio is high. It is therefore urgent to evaluate the efficiency of infection prevention and control (IPC) measures in reducing SARS-CoV-2 transmission. METHODS: We analysed the COVID-19 outbreaks that took place between March and May 2020 in 12 ACFs using reverse transcription-polymerase chain reaction (RT-PCR) and serological tests for SARS-CoV-2 infection. Using maximum-likelihood approaches and generalized linear mixed models, we analysed the proportion of infected residents in ACFs and identified covariates associated with the proportion of infected residents. RESULTS: The secondary-attack risk was estimated at 4.1%, suggesting a high efficiency of the IPC measures implemented in the region. Mask wearing and the establishment of COVID-19 zones for infected residents were the two main covariates associated with lower secondary-attack risks. CONCLUSIONS: Wearing masks and isolating potentially infected residents appear to be associated with a more limited spread of SARS-CoV-2 in ACFs.

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 Alpha variant and that its transmission advantage over wild type strains was between 38 and 44%. We also find that tests positive for Alpha and Beta/Gamma variants exhibit significantly lower cycle threshold (Ct) values.

SARS-CoV-2 viral RNA levels are not 'viral load'.

Ct values are commonly used as proxies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 'viral load'. Since coronaviruses are positive single-stranded RNA [(+)ssRNA] viruses, current reverse transcription (RT)-qPCR target amplification does not distinguish replicative from transcriptional RNA. Although analyses of Ct values remain informative, equating them with viral load may lead to flawed conclusions as it is presently unknown whether (and to what extent) variation in Ct reflects variation in viral load or in gene expression.

Episome partitioning and symmetric cell divisions: Quantifying the role of random events in the persistence of HPV infections.

Human Papillomaviruses (HPV) are one of the most prevalent sexually transmitted infections (STI) and the most oncogenic viruses known to humans. The vast majority of HPV infections clear in less than 3 years, but the underlying mechanisms, especially the involvement of the immune response, are still poorly known. Building on earlier work stressing the importance of randomness in the type of cell divisions in the clearance of HPV infection, we develop a stochastic mathematical model of HPV dynamics that combines the previous aspect with an explicit description of the intracellular level. We show that the random partitioning of virus episomes upon stem cell division and the occurrence of symmetric divisions dramatically affect viral persistence. These results call for more detailed within-host studies to better understand the relative importance of stochasticity and immunity in HPV infection clearance.

Rapid spread of the SARS-CoV-2 Delta variant in some French regions, June 2021.

We analysed 9,030 variant-specific RT-PCR tests performed on SARS-CoV-2-positive samples collected in France between 31 May and 21 June 2021. This analysis revealed rapid growth of the Delta variant in three of the 13 metropolitan French regions and estimated a +79% (95% confidence interval: 52-110%) transmission advantage compared with the Alpha variant. The next weeks will prove decisive and the magnitude of the estimated transmission advantages of the Delta variant could represent a major challenge for public health authorities.

SARS-CoV-2 virulence evolution: Avirulence theory, immunity and trade-offs.

The COVID-19 pandemic has led to a resurgence of the debate on whether host-parasite interactions should evolve towards avirulence. In this review, we first show that SARS-CoV-2 virulence is evolving, before explaining why some expect the mortality caused by the epidemic to converge towards that of human seasonal alphacoronaviruses. Leaning on existing theory, we then include viral evolution into the picture and discuss hypotheses explaining why the virulence has increased since the beginning of the pandemic. Finally, we mention some potential scenarios for the future.