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Register a new userA look into the future of the COVID-19 pandemic in Europe: an expert consultation
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How will the coronavirus disease 2019 (COVID-19) pandemic develop in the coming months and years? Based on an expert survey, we examine key aspects that are likely to influence COVID-19 in Europe. The future challenges and developments will strongly depend on the progress of national and global vaccination programs, the emergence and spread of variants of concern, and public responses to nonpharmaceutical interventions (NPIs). In the short term, many people are still unvaccinated, VOCs continue to emerge and spread, and mobility and population mixing is expected to increase over the summer. Therefore, policies that lift restrictions too much and too early risk another damaging wave. This challenge remains despite the reduced opportunities for transmission due to vaccination progress and reduced indoor mixing in the summer. In autumn 2021, increased indoor activity might accelerate the spread again, but a necessary reintroduction of NPIs might be too slow. The incidence may strongly rise again, possibly filling intensive care units, if vaccination levels are not high enough. A moderate, adaptive level of NPIs will thus remain necessary. These epidemiological aspects are put into perspective with the economic, social, and health-related consequences and thereby provide a holistic perspective on the future of COVID-19.
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OBJECTIVES: to describe the first wave of the COVID-19 pandemic with a focus on undetected cases and to evaluate different post-lockdown scenarios. DESIGN: the study introduces a SEIR compartmental model, taking into account the region-specific fraction of undetected cases, the effects of mobility restrictions, and the personal protective measures adopted, such as wearing a mask and washing hands frequently. SETTING AND PARTICIPANTS: the model is experimentally validated with data of all the Italian regions, some European countries, and the US. MAIN OUTCOME MEASURES: the accuracy of the model results is measured through the mean absolute percentage error (MAPE) and Lewis criteria; fitting parameters are in good agreement with previous literature. RESULTS: the epidemic curves for different countries and the amount of undetected and asymptomatic cases are estimated, which are likely to represent the main source of infections in the near future. The model is applied to the Hubei case study, which is the first place to relax mobility restrictions. Results show different possible scenarios. Mobility and the adoption of personal protective measures greatly influence the dynamics of the infection, determining either a huge and rapid secondary epidemic peak or a more delayed and manageable one. CONCLUSIONS: mathematical models can provide useful insights for healthcare decision makers to determine the best strategy in case of future outbreaks.
The all-pervasive lens that humans ordinarily use to watch and analyze the pandemic is time. This article considers an alternative. Instead of tracking incidence as a function of time, new cases are counted as a function of cumulative cases. This resource-centric perspective, which is more natural and physically justified, is the perspective of the virus. In this article, we demonstrate the relevance of this approach by characterizing an outbreak as an independent increments Gaussian process that fluctuates about a deterministic curve, called the incidence-cumulative cases (ICC) curve. We illustrate these concepts on Influenza A and COVID-19 outbreaks in the US. The novel perspective presented here reveals universal properties of disease spread that would otherwise remain hidden.
Within a short period of time, COVID-19 grew into a world-wide pandemic. Transmission by pre-symptomatic and asymptomatic viral carriers rendered intervention and containment of the disease extremely challenging. Based on reported infection case studies, we construct an epidemiological model that focuses on transmission around the symptom onset. The model is calibrated against incubation period and pairwise transmission statistics during the initial outbreaks of the pandemic outside Wuhan with minimal non-pharmaceutical interventions. Mathematical treatment of the model yields explicit expressions for the size of latent and pre-symptomatic subpopulations during the exponential growth phase, with the local epidemic growth rate as input. We then explore reduction of the basic reproduction number R_0 through specific disease control measures such as contact tracing, testing, social distancing, wearing masks and sheltering in place. When these measures are implemented in combination, their effects on R_0 multiply. We also compare our model behaviour to the first wave of the COVID-19 spreading in various affected regions and highlight generic and less generic features of the pandemic development.
This note describes a simple score to indicate the effectiveness of mitigation against infections of COVID-19 as observed by new case counts. The score includes normalization, making comparisons across jurisdictions possible. The smoothing employed provides robustness in the face of reporting vagaries while retaining salient features of evolution, enabling a clearer picture for decision makers and the public.
We studied the COVID-19 pandemic evolution in selected African countries. For each country considered, we modeled simultaneously the data of the active, recovered and death cases. In this study, we used a year of data since the first cases were reported. We estimated the time-dependent basic reproduction numbers, $R_0$, and the fractions of infected but unaffected populations, to offer insights into containment and vaccine strategies in African countries. We found that $R_0leq 4$ at the start of the pandemic but has since fallen to $R_0 sim 1$. The unaffected fractions of the populations studied vary between $1-10$% of the recovered cases.
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