Projecting the COVID-19 epidemic risk in France for the summer 2021.
Ontology highlight
ABSTRACT:
SUBMITTER: Mazzoli M
PROVIDER: S-EPMC8499767 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-EPMC8403659 | biostudies-literature
Project description:The COVID-19 epidemic was reported in the Hubei province in China in December 2019 and then spread around the world reaching the pandemic stage at the beginning of March 2020. Since then, several countries went into lockdown. Using a mechanistic-statistical formalism, we estimate the effect of the lockdown in France on the contact rate and the effective reproduction number R e of the COVID-19. We obtain a reduction by a factor 7 (R e = 0.47, 95%-CI: 0.45-0.50), compared to the estimates carried out in France at the early stage of the epidemic. We also estimate the fraction of the population that would be infected by the beginning of May, at the official date at which the lockdown should be relaxed. We find a fraction of 3.7% (95%-CI: 3.0-4.8%) of the total French population, without taking into account the number of recovered individuals before April 1st, which is not known. This proportion is seemingly too low to reach herd immunity. Thus, even if the lockdown strongly mitigated the first epidemic wave, keeping a low value of R e is crucial to avoid an uncontrolled second wave (initiated with much more infectious cases than the first wave) and to hence avoid the saturation of hospital facilities.
| S-EPMC7290065 | biostudies-literature
Project description:Background. In mid-2020, there was significant concern that the overlapping 2020-2021 influenza season and COVID-19 pandemic would overwhelm already stressed health care systems in the Northern Hemisphere, particularly if influenza immunization rates were low. Methods. Using a mathematical susceptible-exposed-infected-recovered (SEIR) compartmental model incorporating the age-specific viral transmission rates and disease severity of Austin, Texas, a large metropolitan region, we projected the incidence and health care burden for both COVID-19 and influenza across observed levels of SARS-CoV-2 transmission and influenza immunization rates for the 2020-2021 season. We then retrospectively compared scenario projections made in August 2020 with observed trends through June 2021. Results. Across all scenarios, we projected that the COVID-19 burden would dwarf that of influenza. In all but our lowest transmission scenarios, intensive care units were overwhelmed by COVID-19 patients, with the levels of influenza immunization having little impact on health care capacity needs. Consistent with our projections, sustained nonpharmaceutical interventions (NPIs) in Austin prevented COVID-19 from overwhelming health care systems and almost completely suppressed influenza during the 2020-2021 respiratory virus season. Limitations. The model assumed no cross-immunity between SARS-CoV-2 and influenza, which might reduce the burden or slow the transmission of 1 or both viruses. Conclusion. Before the widespread rollout of the SARS-CoV-2 vaccine, COVID-19 was projected to cause an order of magnitude more hospitalizations than seasonal influenza because of its higher transmissibility and severity. Consistent with predictions assuming strong NPIs, COVID-19 strained but did not overwhelm local health care systems in Austin, while the influenza burden was negligible. Implications. Nonspecific NPI efforts can dramatically reduce seasonal influenza burden and preserve health care capacity during respiratory virus season.HighlightsAs the COVID-19 pandemic threatened lives worldwide, the Northern Hemisphere braced for a potential "twindemic" of seasonal influenza and COVID-19.Using a validated mathematical model of influenza and SARS-CoV-2 co-circulation in a large US city, we projected the impact of COVID-19-driven nonpharmaceutical interventions combined with influenza vaccination on health care capacity during the 2020-2021 respiratory virus season.We describe analyses conducted during summer 2020 to help US cities prepare for the 2020-2021 influenza season and provide a retrospective evaluation of the initial projections.
| S-EPMC8915218 | biostudies-literature
Project description:BackgroundPolicy responses to COVID-19 in Victoria, Australia over 2020-2021 have been supported by evidence generated through mathematical modelling. This study describes the design, key findings, and process for policy translation of a series of modelling studies conducted for the Victorian Department of Health COVID-19 response team during this period.MethodsAn agent-based model, Covasim, was used to simulate the impact of policy interventions on COVID-19 outbreaks and epidemic waves. The model was continually adapted to enable scenario analysis of settings or policies being considered at the time (e.g. elimination of community transmission versus disease control). Model scenarios were co-designed with government, to fill evidence gaps prior to key decisions.ResultsUnderstanding outbreak risk following incursions was critical to eliminating community COVID-19 transmission. Analyses showed risk depended on whether the first detected case was the index case, a primary contact of the index case, or a 'mystery case'. There were benefits of early lockdown on first case detection and gradual easing of restrictions to minimise resurgence risk from undetected cases. As vaccination coverage increased and the focus shifted to controlling rather than eliminating community transmission, understanding health system demand was critical. Analyses showed that vaccines alone could not protect health systems and need to be complemented with other public health measures.ConclusionsModel evidence offered the greatest value when decisions needed to be made pre-emptively, or for questions that could not be answered with empiric data and data analysis alone. Co-designing scenarios with policy-makers ensured relevance and increased policy translation.
| S-EPMC10219801 | biostudies-literature
Project description:Vaccine hesitancy and refusal continue to hamper COVID-19 control efforts. Throughout the pandemic, scientists and journalists have attributed lagging COVID-19 vaccination rates to a shifting set of factors including demography, experiences during the height of the pandemic, political views, and beliefs in conspiracy theories and misinformation, among others. However, these factors have rarely been tested comprehensively, in tandem, or alongside other potentially underlying psychological factors, thus limiting our understanding of COVID-19 vaccine hesitancy. This cross-sectional study assesses a diverse set of correlates of COVID-19 vaccine hesitancy identified in previous studies using US survey data (N = 2055) collected in July-August 2021. The survey contained modules designed to assess various sociopolitical domains and anti- and pro-social personality characteristics hypothesized to shape vaccine hesitancy. Using logistic and multinomial regression, we found that the strongest correlate of vaccine hesitancy was belief in misinformation about the COVID-19 vaccines, though we surmise that this common explanation may be endogenous to vaccine hesitancy. Political beliefs explained more variation in vaccine hesitancy-and in particular, vaccine refusal-after belief in COVID-19 vaccine misinformation was excluded from the analysis. Our findings help reconcile numerous disparate findings across the literature with implications for health education and future research.
| S-EPMC9167731 | biostudies-literature
Project description: Not available
| S-EPMC8676422 | biostudies-literature
Project description:In this paper, we use a deterministic epidemic model with memory to estimate the state of the COVID-19 epidemic in France, from early March until mid-December 2020. Our model is in the SEIR class, which means that when a susceptible individual (S) becomes infected, he/she is first exposed (E), i.e. not yet contagious. Then he/she becomes infectious (I) for a certain length of time, during which he/she may infect susceptible individuals around him/her, and finally becomes removed (R), that is, either immune or dead. The specificity of our model is that it assumes a very general probability distribution for the pair of exposed and infectious periods. The law of large numbers limit of such a model is a model with memory (the future evolution of the model depends not only upon its present state, but also upon its past). We present theoretical results linking the (unobserved) parameters of the model to various quantities which are more easily measured during the early stages of an epidemic. We then apply these results to estimate the state of the COVID-19 epidemic in France, using available information on the infection fatality ratio and on the distribution of the exposed and infectious periods. Using the hospital data published daily by Santé Publique France, we gather some information on the delay between infection and hospital admission, intensive care unit (ICU) admission and hospital deaths, and on the proportion of people who have been infected up to the end of 2020.
| S-EPMC8074964 | biostudies-literature
Project description:BackgroundMore than half of the global population is under strict forms of social distancing. Estimating the expected impact of lockdown and exit strategies is critical to inform decision makers on the management of the COVID-19 health crisis.MethodsWe use a stochastic age-structured transmission model integrating data on age profile and social contacts in Île-de-France to (i) assess the epidemic in the region, (ii) evaluate the impact of lockdown, and (iii) propose possible exit strategies and estimate their effectiveness. The model is calibrated to hospital admission data before lockdown. Interventions are modeled by reconstructing the associated changes in the contact matrices and informed by mobility reductions during lockdown evaluated from mobile phone data. Different types and durations of social distancing are simulated, including progressive and targeted strategies, with large-scale testing.ResultsWe estimate the reproductive number at 3.18 [3.09, 3.24] (95% confidence interval) prior to lockdown and at 0.68 [0.66, 0.69] during lockdown, thanks to an 81% reduction of the average number of contacts. Model predictions capture the disease dynamics during lockdown, showing the epidemic curve reaching ICU system capacity, largely strengthened during the emergency, and slowly decreasing. Results suggest that physical contacts outside households were largely avoided during lockdown. Lifting the lockdown with no exit strategy would lead to a second wave overwhelming the healthcare system, if conditions return to normal. Extensive case finding and isolation are required for social distancing strategies to gradually relax lockdown constraints.ConclusionsAs France experiences the first wave of COVID-19 pandemic in lockdown, intensive forms of social distancing are required in the upcoming months due to the currently low population immunity. Extensive case finding and isolation would allow the partial release of the socio-economic pressure caused by extreme measures, while avoiding healthcare demand exceeding capacity. Response planning needs to urgently prioritize the logistics and capacity for these interventions.
| S-EPMC7391016 | biostudies-literature
Project description:We analyze the task-content of occupations operating in about 600 sectors of the economy with a focus on the dimensions that expose workers to contagion risks during the COVID-19 epidemic. We do so in the Italian context, leveraging extremely detailed and granular information from ICP, the Italian equivalent of O*Net (the survey that describes the task content of US occupations). We find that several sectors need physical proximity to operate, mainly in services and retail trade. Workers at risk of complications from COVID-19 (mainly males above the age of 50) are concentrated in sectors characterized by little physical proximity or where working from home is feasible. We then study the sectoral lockdowns put in place by the Italian Government in March 2020. We find that governmental restrictions hit the sectors where the risk of contagion in the workplace was more widespread: the effect is stronger for proximity to the public than that with co-workers. The share of workers who have the possibility to work from home is higher in sectors that were not forced to close. The evidence we provide is useful to identify which activities pose larger risks for contagion among workers in the workplace and where to reinforce safety measures. Supplementary Information The online version contains supplementary material available at 10.1007/s40797-021-00164-1.
| S-EPMC8298199 | biostudies-literature
Project description:IntroductionIn order to evaluate trends in death after COVID-19 vaccination we analyzed the timing of death relative to vaccination date and the causes of death in vaccinated Utahns in 2021.MethodsWe matched people in the Utah immunization registry with documented COVID-19 vaccinations between December 18, 2020 and December 31, 2021 to Utah's 2021 vital statistics death records. Vaccinated people were categorized as having one, two, or ≥ three COVID-19 vaccine doses in a time-updated metric. We examined crude mortality rates by dosing groups in two-week intervals for all deaths, and by COVID-19 versus non-COVID-19 causes, within the 44 weeks following receipt of the most recent vaccine.ResultsWe identified 2,072,908 individuals who received at least one dose of COVID-19 vaccine of whom 10,997 died in 2021. Only 17.5 % of the total vaccinated population was age 65+, while 80.9 % of those who died were over 65. In the four weeks following the first or second vaccination, all-cause mortality was low and then stabilized for the remainder of the evaluation period at a bi-weekly average of 33.0 and 39.0 deaths/100,000 people for one and two doses, respectively. Typical seasonal variation in death was observed among those with two doses. Small sample size precluded analysis of those with ≥ three doses, but trends were similar.ConclusionsMortality rates in the 44 weeks following the COVID-19 vaccination did not show trends suggesting an increase in mortality related to COVID-19 vaccination, reinforcing the safety of COVID-19 vaccines. This represents an accessible approach for local evaluation.
| S-EPMC10073592 | biostudies-literature