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On ignore si les variations climatiques saisonnières, la fermeture des établissements scolaires ou d’autres interventions de santé publique entraîneront un ralentissement de la pandémie actuelle de maladie à coronavirus 2019 (COVID-19). Nous avons voulu déterminer si de façon globale la progression de l’épidémie est associée au climat ou aux interventions de santé publique visant à réduire la transmission du coronavirus du syndrome respiratoire aigu sévère 2 (SRAS-CoV-2). Nous avons procédé à une étude de cohorte prospective des 144 régions géopolitiques de la planète (375 609 cas) présentant au moins 10 cas de COVID-19, avec transmission locale, en date du 20 mars 2020, à l’exclusion de la Chine, de la Corée du Sud, de l’Iran et de l’Italie. Par analyse de régression à effets aléatoires pondérée, nous avons évalué le lien entre la progression de l’épidémie (exprimée sous forme de rapports de taux d’incidence [RTI] comparant les nombres cumulatifs de cas de COVID-19 du 27 mars 2020 à ceux du 20 mars 2020) avec les facteurs de latitude, température, humidité, fermeture des établissements scolaires, interdiction des grands rassemblements et mesures d’éloignement social qui étaient en place les 7 et 13 mars 2020 (période de 14 jours antérieure à l’évaluation). Les analyses univariées ont révélé aucuns lien entre la progression de l’épidémie et les facteurs de latitude et de température, mais des liens négatifs faibles avec l’humidité relative (RTI par 10 %, 0,91, intervalle de confiance [IC] de 95 % 0,85–0,96) et l’humidité absolue (RTI par 5 g/m3 0,92, IC à 95 % 0,85–0,99). Des liens étroits ont été observés avec l’interdiction des grands rassemblements (RTI 0,65, IC à 95 % 0,53–0,79), la fermeture des établissements scolaires (RTI 0,63, IC à 95 % 0,52–0,78) et les mesures d’éloignement social (RTI 0,62, IC à 95 % 0,45–0,85). Dans un modèle multivarié, on a noté un lien étroit avec le nombre de mesures déployées par la santé publique (p pour tendance = 0,001), tandis que le lien avec l’humidité absolue s’atténuait. La progression de l’épidémie de COVID-19 ne s’est pas révélée en lien avec la latitude ni avec la température, mais faiblement en lien avec l’humidité relative ou absolue. À l’inverse, les interventions de santé publique ont été étroitement associées à un ralentissement de la progression de l’épidémie.

Au Canada, on utilise des interventions d’éloignement physique pour ralentir la propagation du SRAS-CoV-2 (coronavirus du syndrome respiratoire aigu sévère 2), mais on ignore au juste quelle en sera l’efficacité. Nous avons évalué comment différentes interventions non pharmacologiques pouvaient être utilisées pour maîtriser la pandémie de COVID-19 (maladie à coronavirus 2019) et alléger le fardeau qu’elle impose au système de santé. Nous avons utilisé un modèle à compartiments structuré selon l’âge pour faire une analyse de la transmission de la COVID-19 dans la population de l’Ontario, au Canada. Nous avons comparé un scénario de référence, soit dépistage limité, isolement et quarantaine, à des scénarios incluant dépistage accru, mesures strictes d’éloignement physique, ou combinaison de dépistage accru et d’éloignement physique moins strict. Les interventions étaient appliquées soit pendant des durées fixes, soit selon un cycle dynamique en fonction de l’occupation projetée des lits dans les unités de soins intensifs (USI). Nous présentons les médianes et les intervalles de crédibilité tirés de 100 expériences répliquées par scénario sur un horizon temporel de 2 ans. Selon le scénario de référence, nous avons estimé que 56 % (intervalle de crédibilité de 95 %, 42 %–63 %) de la population ontarienne contractait l’infection pendant l’épidémie. Au moment du sommet épidémique, nous avons projeté 107 000 (intervalle de crédibilité de 95 %, 60 760–149 000) hospitalisations (soins standards) et 55 500 (intervalle de crédibilité de 95 %, 32 700–75 200) hospitalisations dans les USI. Pour les scénarios à durée fixe, selon les projections, toutes les interventions retardaient et réduisaient la hauteur du sommet épidémique par rapport au scénario de référence, et ce sont les mesures d’éloignement physique strict qui exerçaient le plus d’effet; de même, les interventions de durée plus longue étaient plus efficaces. Selon les projections, les interventions dynamiques réduisaient la proportion de la population atteinte à la fin de la période de 2 ans et pouvaient ramener le nombre médian de cas dans les USI en deçà des estimations actuelles de leur capacité en Ontario. Sans éloignement physique substantiel ou une combinaison d’éloignement physique modéré et de dépistage accru, nous projetons que les ressources des USI pourraient être insuffisantes. L’éloignement physique dynamique maintiendrait la capacité du système de santé en plus de donner un répit psychologique et économique périodique aux populations.

Between February and June 2021, the initial wild-type strains of SARS-CoV-2 were supplanted in Ontario, Canada, by new variants of concern (VOCs), first those with the N501Y mutation (i.e., Alpha/B1.1.17, Beta/B.1.351 and Gamma/P.1 variants) and then the Delta/B.1.617 variant. The increased transmissibility of these VOCs has been documented, but knowledge about their virulence is limited. We used Ontario’s COVID-19 case data to evaluate the virulence of these VOCs compared with non-VOC SARS-CoV-2 strains, as measured by risk of hospitalization, intensive care unit (ICU) admission and death. We created a retrospective cohort of people in Ontario who tested positive for SARS-CoV-2 and were screened for VOCs, with dates of test report between Feb. 7 and June 27, 2021. We constructed mixed-effect logistic regression models with hospitalization, ICU admission and death as outcome variables. We adjusted models for age, sex, time, vaccination status, comorbidities and pregnancy status. We included health units as random intercepts. Our cohort included 212 326 people. Compared with non-VOC SARS-CoV-2 strains, the adjusted elevation in risk associated with N501Y-positive variants was 52% (95% confidence interval [CI] 42%–63%) for hospitalization, 89% (95% CI 67%–117%) for ICU admission and 51% (95% CI 30%–78%) for death. Increased risk with the Delta variant was more pronounced at 108% (95% CI 78%–140%) for hospitalization, 235% (95% CI 160%–331%) for ICU admission and 133% (95% CI 54%–231%) for death. The increasing virulence of SARS-CoV-2 VOCs will lead to a considerably larger, and more deadly, pandemic than would have occurred in the absence of the emergence of VOCs.

Physical-distancing interventions are being used in Canada to slow the spread of severe acute respiratory syndrome coronavirus 2, but it is not clear how effective they will be. We evaluated how different nonpharmaceutical interventions could be used to control the coronavirus disease 2019 (COVID-19) pandemic and reduce the burden on the health care system. We used an age-structured compartmental model of COVID-19 transmission in the population of Ontario, Canada. We compared a base case with limited testing, isolation and quarantine to scenarios with the following: enhanced case finding, restrictive physical-distancing measures, or a combination of enhanced case finding and less restrictive physical distancing. Interventions were either implemented for fixed durations or dynamically cycled on and off, based on projected occupancy of intensive care unit (ICU) beds. We present medians and credible intervals from 100 replicates per scenario using a 2-year time horizon. We estimated that 56% (95% credible interval 42%–63%) of the Ontario population would be infected over the course of the epidemic in the base case. At the epidemic peak, we projected 107 000 (95% credible interval 60 760–149 000) cases in hospital (non-ICU) and 55 500 (95% credible interval 32 700–75 200) cases in ICU. For fixed-duration scenarios, all interventions were projected to delay and reduce the height of the epidemic peak relative to the base case, with restrictive physical distancing estimated to have the greatest effect. Longer duration interventions were more effective. Dynamic interventions were projected to reduce the proportion of the population infected at the end of the 2-year period and could reduce the median number of cases in ICU below current estimates of Ontario’s ICU capacity. Without substantial physical distancing or a combination of moderate physical distancing with enhanced case finding, we project that ICU resources would be overwhelmed. Dynamic physical distancing could maintain health-system capacity and also allow periodic psychological and economic respite for populations.

It is unclear whether seasonal changes, school closures or other public health interventions will result in a slowdown of the current coronavirus disease 2019 (COVID-19) pandemic. We aimed to determine whether epidemic growth is globally associated with climate or public health interventions intended to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We performed a prospective cohort study of all 144 geopolitical areas worldwide (375 609 cases) with at least 10 COVID-19 cases and local transmission by Mar. 20, 2020, excluding China, South Korea, Iran and Italy. Using weighted random-effects regression, we determined the association between epidemic growth (expressed as ratios of rate ratios [RRR] comparing cumulative counts of COVID-19 cases on Mar. 27, 2020, with cumulative counts on Mar. 20, 2020) and latitude, temperature, humidity, school closures, restrictions of mass gatherings, and measures of social distancing during an exposure period 14 days previously (Mar. 7 to 13, 2020). In univariate analyses, there were no associations of epidemic growth with latitude and temperature, but weak negative associations with relative humidity (RRR per 10% 0.91, 95% confidence interval [CI] 0.85–0.96) and absolute humidity (RRR per 5 g/m3 0.92, 95% CI 0.85–0.99). Strong associations were found for restrictions of mass gatherings (RRR 0.65, 95% CI 0.53–0.79), school closures (RRR 0.63, 95% CI 0.52–0.78) and measures of social distancing (RRR 0.62, 95% CI 0.45–0.85). In a multivariable model, there was a strong association with the number of implemented public health interventions (p for trend = 0.001), whereas the association with absolute humidity was no longer significant. Epidemic growth of COVID-19 was not associated with latitude and temperature, but may be associated weakly with relative or absolute humidity. Conversely, public health interventions were strongly associated with reduced epidemic growth.

To understand how viruses spread scientists look at two things. One is – on average – how many other people each infected person spreads the virus to. The other is how much variability there is in the number of people each person with the virus infects. Some viruses like the 2009 influenza H1N1, a new strain of influenza that caused a pandemic beginning in 2009, spread pretty uniformly, with many people with the virus infecting around two other people. Other viruses like SARS-CoV-2, the one that causes COVID-19, are more variable. About 10 to 20% of people with COVID-19 cause 80% of subsequent infections – which may lead to so-called superspreading events – while 60-75% of people with COVID-19 infect no one else. Learning more about these differences can help public health officials create better ways to curb the spread of the virus. Chen et al. show that differences in the concentration of virus particles in the respiratory tract may help to explain why superspreaders play such a big role in transmitting SARS-CoV-2, but not the 2009 influenza H1N1 virus. Chen et al. reviewed and extracted data from studies that have collected how much virus is present in people infected with either SARS-CoV-2, a similar virus called SARS-CoV-1 that caused the SARS outbreak in 2003, or with 2009 influenza H1N1. Chen et al. found that as the variability in the concentration of the virus in the airways increased, so did the variability in the number of people each person with the virus infects. Chen et al. further used mathematical models to estimate how many virus particles individuals with each infection would expel via droplets or aerosols, based on the differences in virus concentrations from their analyses. The models showed that most people with COVID-19 infect no one because they expel little – if any – infectious SARS-CoV-2 when they talk, breathe, sing or cough. Highly infectious individuals on the other hand have high concentrations of the virus in their airways, particularly the first few days after developing symptoms, and can expel tens to thousands of infectious virus particles per minute. By contrast, a greater proportion of people with 2009 influenza H1N1 were potentially infectious but tended to expel relatively little infectious virus when the talk, sing, breathe or cough. These results help explain why superspreaders play such a key role in the ongoing pandemic. This information suggests that to stop this virus from spreading it is important to limit crowd sizes, shorten the duration of visits or gatherings, maintain social distancing, talk in low volumes around others, wear masks, and hold gatherings in well-ventilated settings. In addition, contact tracing can prioritize the contacts of people with high concentrations of virus in their airways.

The speed of vaccine development has been a singular achievement during the COVID-19 pandemic, although uptake has not been universal. Vaccine opponents often frame their opposition in terms of the rights of the unvaccinated. We sought to explore the impact of mixing of vaccinated and unvaccinated populations on risk of SARS-CoV-2 infection among vaccinated people. We constructed a simple susceptible–infectious–recovered compartmental model of a respiratory infectious disease with 2 connected subpopulations: people who were vaccinated and those who were unvaccinated. We simulated a spectrum of patterns of mixing between vaccinated and unvaccinated groups that ranged from random mixing to complete like-with-like mixing (complete assortativity), in which people have contact exclusively with others with the same vaccination status. We evaluated the dynamics of an epidemic within each subgroup and in the population as a whole. We found that the risk of infection was markedly higher among unvaccinated people than among vaccinated people under all mixing assumptions. The contact-adjusted contribution of unvaccinated people to infection risk was disproportionate, with unvaccinated people contributing to infections among those who were vaccinated at a rate higher than would have been expected based on contact numbers alone. We found that as like-with-like mixing increased, attack rates among vaccinated people decreased from 15% to 10% (and increased from 62% to 79% among unvaccinated people), but the contact-adjusted contribution to risk among vaccinated people derived from contact with unvaccinated people increased. Although risk associated with avoiding vaccination during a virulent pandemic accrues chiefly to people who are unvaccinated, their choices affect risk of viral infection among those who are vaccinated in a manner that is disproportionate to the portion of unvaccinated people in the population.

[This corrects the article DOI: 10.1371/journal.pone.0297093.].

We previously demonstrated that when vaccines prevent infection, the dynamics of mixing between vaccinated and unvaccinated sub-populations is such that use of imperfect vaccines markedly decreases risk for vaccinated people, and for the population overall. Risks to vaccinated people accrue disproportionately from contact with unvaccinated people. In the context of the emergence of Omicron SARS-CoV-2 and evolving understanding of SARS-CoV-2 epidemiology, we updated our analysis to evaluate whether our earlier conclusions remained valid. We modified a previously published Susceptible-Infectious-Recovered (SIR) compartmental model of SARS-CoV-2 with two connected sub-populations: vaccinated and unvaccinated, with non-random mixing between groups. Our expanded model incorporates diminished vaccine efficacy for preventing infection with the emergence of Omicron SARS-CoV-2 variants, waning immunity, the impact of prior immune experience on infectivity, \"hybrid\" effects of infection in previously vaccinated individuals, and booster vaccination. We evaluated the dynamics of an epidemic within each subgroup and in the overall population over a 10-year time horizon. Even with vaccine efficacy as low as 20%, and in the presence of waning immunity, the incidence of COVID-19 in the vaccinated subpopulation was lower than that among the unvaccinated population across the full 10-year time horizon. The cumulative risk of infection was 3-4 fold higher among unvaccinated people than among vaccinated people, and unvaccinated people contributed to infection risk among vaccinated individuals at twice the rate that would have been expected based on the frequency of contacts. These findings were robust across a range of assumptions around the rate of waning immunity, the impact of \"hybrid immunity\", frequency of boosting, and the impact of prior infection on infectivity in unvaccinated people. Although the emergence of the Omicron variants of SARS-CoV-2 has diminished the protective effects of vaccination against infection with SARS-CoV-2, updating our earlier model to incorporate loss of immunity, diminished vaccine efficacy and a longer time horizon, does not qualitatively change our earlier conclusions. Vaccination against SARS-CoV-2 continues to diminish the risk of infection among vaccinated people and in the population as a whole. By contrast, the risk of infection among vaccinated people accrues disproportionately from contact with unvaccinated people.