BackgroundAlthough France was one of the most affected European countries by the COVID-19 pandemic in 2020, the dynamics of SARS-CoV-2 transmissions within France, Europe and worldwide remain only partially characterized during the first year of the pandemic. MethodsHere, we analyzed GISAID deposited sequences from January to December 2020 (n = 638,706 sequences). To tackle the huge number of sequences without the bias of analyzing a single sequence subset, we produced 100 independent and randomly selected sequence datasets and related phylogenetic trees for different geographic scales (worldwide, European countries and French administrative regions) and time periods (first and second half of 2020). We applied a maximum likelihood discrete trait phylogeographic method to date transmission events and to estimate the geographic spread of SARS-CoV-2 to, from and within France, Europe and worldwide. ResultsThe results unraveled two different patterns of inter- and intra-territory transmission events between the first and second half of 2020. Throughout the year, Europe was systematically associated with most of the intercontinental transmissions, for which France has played a pivotal role. SARS-CoV-2 transmissions with France were concentrated with North America and Europe (mainly Italy, Spain, United Kingdom, Belgium and Germany) during the first wave, and were limited to neighboring countries without strong intercontinental transmission during the second one. Regarding French administrative regions, the Paris area was the main source of transmissions during the first wave. But, for the second epidemic wave, it equally contributed to virus spread with Lyon and Marseille area, the two other most densely populated cities in France. ConclusionBy enabling the inclusion of tens of thousands of viral sequences, this original phylogenetic strategy enabled us to robustly depict SARS-CoV-2 transmissions through France, Europe and worldwide in 2020.
Evaluating the characteristics of emerging SARS-CoV-2 variants of concern is essential to inform pandemic risk assessment. A variant may grow faster if it produces a larger number of secondary infections (transmissibility advantage) or if the timing of secondary infections (generation time) is better. So far, assessments have largely focused on deriving the transmissibility advantage assuming the generation time was unchanged. Yet, knowledge of both is needed to anticipate impact. Here we develop an analytical framework to investigate the contribution of both the transmissibility advantage and generation time to the growth advantage of a variant. We find that the growth advantage depends on the epidemiological context (level of epidemic control). More specifically, variants conferring earlier transmission are more strongly favoured when the historical strains have fast epidemic growth, while variants conferring later transmission are more strongly favoured when historical strains have slow or negative growth. We develop these conceptual insights into a statistical framework to infer both the transmissibility advantage and generation time of a variant. On simulated data, our framework correctly estimates both parameters when it covers time periods characterized by different epidemiological contexts. Applied to data for the Alpha and Delta variants in England and in Europe, we find that Alpha confers a +54% [95% CI, 45-63%] transmissibility advantage compared to previous strains, and Delta +140% [98-182%] compared to Alpha, and mean generation times are similar to historical strains for both variants. This work helps interpret variant frequency and will strengthen risk assessment for future variants of concern.
The relationship between SARS-CoV-2 viral load and infectiousness is not known. Using data from a prospective cohort of index cases and high-risk contact, we reconstructed by modelling the viral load at the time of contact and the probability of infection. The effect of viral load was particularly large in household contacts, with a transmission probability that increased to as much as 37% when the viral load was greater than 10 log10 copies per mL. The transmission probability peaked at symptom onset in most individuals, with a median probability of transmission of 15%, that hindered large individual variations (IQR: [8, 37]). The model also projects the effects of variants on disease transmission. Based on the current knowledge that viral load is increased by 2 to 4-fold on average, we estimate that infection with B1.1.7 virus could lead to an increase in the probability of transmission by 8 to 17%.
Objective: We aimed to estimate the risk of infection in Healthcare workers (HCWs) following a high-risk exposure without personal protective equipment (PPE). Methods: We conducted a prospective cohort in HCWs who had a high-risk exposure to SARS-CoV-2-infected subject without PPE. Daily symptoms were self-reported for 30 days, nasopharyngeal swabs for SARS-CoV-2 RT-PCR were performed at inclusion and at days 3, 5, 7 and 12, SARS-CoV-2 serology was assessed at inclusion and at day 30. Confirmed infection was defined by positive RT-PCR or seroconversion, and possible infection by one general and one specific symptom for two consecutive days. Results: Between February 5th and May 30th, 2020, 154 HCWs were enrolled within 14 days following one high-risk exposure to either a hospital patient (70/154; 46.1%) and/or a colleague (95/154; 62.5%). At day 30, 25.0% had a confirmed infection (37/148; 95%CI, 18.4%; 32.9%), and 43.9% (65/148; 95%CI, 35.9%; 52.3%) had a confirmed or possible infection. Factors independently associated with confirmed or possible SARS-CoV-2 infection were being a pharmacist or administrative assistant rather than being from medical staff (adjusted OR (aOR)=3.8, CI95%=1.3;11.2, p=0.01), and exposure to a SARS-CoV-2-infected patient rather than exposure to a SARS-CoV-2-infected colleague (aOR=2.6, CI95%=1.2;5.9, p=0.02). Among the 26 HCWs with a SARS-CoV-2-positive nasopharyngeal swab, 7 (26.9%) had no symptom at the time of the RT-PCR positivity. Conclusions: The proportion of HCWs with confirmed or possible SARS-CoV-2 infection was high. There were less occurrences of high-risk exposure with patients than with colleagues, but those were associated with an increased risk of infection.
To better control the SARS-CoV-2 pandemic, it is essential to quantify the impact of control measures and the fraction of infected individuals that are detected. To this end we developed a deterministic transmission model based on the renewal equation and fitted the model to daily case and death data in the first few months of 2020 in 79 countries and states, representing more than 4 billions individuals. Based on a region-specific infected fatality ratio, we inferred the time-varying probability of case detection and the time-varying decline in transmissiblity. The model was validated by the good correlation between the predicted total number of infected and that found in serosurveys; and most importantly by the strong correlation between the inferred probability of detection and the number of daily tests per inhabitant, with 50% detection achieved with 0.003 daily tests per inhabitants. Most of the decline in transmission was explained by the reductions in transmissibility (social distancing), which avoided 107 deaths in the regions studied over the first four months of 2020. In contrast, symptom-based testing and isolation was not an efficient way to control the spread of the disease, as a large part of transmission happens before symptoms and only a small fraction of infected individuals was typically detected. We developed a phenomenological model to link the number of daily tests with the probability of detection and verified the prediction that increasing test capacity increases the probability of detection less than proportionally. Together these results suggest that little control can be achieved by symptom-based testing and isolation alone.
Repurposed drugs that are immediately available and safe to use constitute a first line of defense against new viral infections. Despite limited antiviral activity against SARS-CoV-2, several drugs are being tested as medication or as prophylaxis to prevent infection. Using a stochastic model of early phase infection, we find that a critical efficacy above 87% is needed to block viral establishment. This can be improved by combination therapy. Below the critical efficacy, establishment of infection can sometimes be prevented, most effectively with drugs blocking viral entry into cells or enhancing viral clearance. Even when a viral infection cannot be prevented, antivirals delay the time to detectable viral loads. This delay flattens the within-host viral dynamic curve, possibly reducing transmission and symptom severity. Thus, antiviral prophylaxis, even with reduced efficacy, could be efficiently used to prevent or alleviate infection in people at high risk.
