COVID-19: The Second Wave is not due to Cooling-down in Autumn.
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ABSTRACT:
SUBMITTER: Langel W
PROVIDER: S-EPMC8242105 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
Publications
COVID-19: The Second Wave is not due to Cooling-down in Autumn.
Journal of epidemiology and global health 20210324 2
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Project description:The world continues in the grip of COVID-19 with devastated tourism industries and global economies. In a previous paper, it was noted that a country's failure to dampen a first wave of infection or the recurrence of a second wave would serve as disincentives for greatly needed tourists in summer 2020 and would further significantly reduce tourism revenues and potentially accelerate job losses and bankruptcies in affected countries. Countries in the first wave of infection would need to restrain COVID-19 spread swiftly in order to benefit from summer 2020 tourism. Countries that had controlled COVID-19 and who experienced second waves would manifest the same negative effects. In the case of Malta, up to the beginning of July, the country had the lowest COVID-19 numbers in Europe but this ended abruptly when two mass events took place. In a fortnight, the steep escalation of cases led to a downgrade of the country's status to a high-risk destination, with a host of European countries enacting quarantine measures. The Maltese government re-imposed restrictions and COVID-19 numbers slowly started to temporarily decline. As an economy, Malta is highly dependent on the tourism industry, with approximately 17% of GDP reliant on this sector, directly and indirectly. Malta's red listing wrought a heavy toll on the industry. The World Health Organisation has mandated clear criteria for the release of restrictions and this sequence of events should serve as a cautionary tale: heed the advice of our public health colleagues. Highlights • COVID-19 has devastated tourism and global economies.• Tourism is greatly needed to revive economies.• High levels of COVID-19 are tourist disincentives for summer 2020.• Malta had low COVID-19 numbers but two July mass events changed this.• An initial spike in tourism in the first half of August fell drastically thereafter.
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Project description:Some occupational sectors, such as human health and care, food service, cultural and sport activities, have been associated with a higher risk of SARS-CoV-2 infection than other sectors. To curb the spread of SARS-CoV-2, it is preferable to apply targeted non-pharmaceutical interventions on selected economic sectors, rather than a full lockdown. However, the effect of these general and sector-specific interventions on the virus circulation has only been sparsely studied. We assess the COVID-19 incidence under different levels of non-pharmaceutical interventions per economic activity during the autumn 2020 wave in Belgium. The 14-day incidence of confirmed COVID-19 cases per the Statistical Classification of Economic Activities in the European Community (NACE-BEL) sector is modelled by a longitudinal Gaussian-Gaussian two-stage approach. This is based on exhaustive data on all employees in all sectors. In the presence of sanitary protocols and minimal non-pharmaceutical interventions, many sectors with close contact with others show considerably higher COVID-19 14-day incidences than other sectors. The effect of stricter non-pharmaceutical interventions in the general population and non-essential sectors is seen in the timing of the peak incidence and the width and height of the post-peak incidence. In most sectors incidences returned to higher levels after the peak than before and this decrease took longer for the health and care sector. Sanitary protocols for close proximity occupations may be sufficient during periods of low-level virus circulation, but progressively less with increasing circulation. Stricter general and sector-specific non-pharmaceutical interventions adequately decrease COVID-19 incidences, even in close proximity in essential sectors under solely sanitary protocols.
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Project description:A second wave pandemic constitutes an imminent threat to society, with a potentially immense toll in terms of human lives and a devastating economic impact. We employ the epidemic Renormalisation Group (eRG) approach to pandemics, together with the first wave data for COVID-19, to efficiently simulate the dynamics of disease transmission and spreading across different European countries. The framework allows us to model, not only inter and extra European border control effects, but also the impact of social distancing for each country. We perform statistical analyses averaging on different level of human interaction across Europe and with the rest of the World. Our results are neatly summarised as an animation reporting the time evolution of the first and second waves of the European COVID-19 pandemic. Our temporal playbook of the second wave pandemic can be used by governments, financial markets, the industries and individual citizens, to efficiently time, prepare and implement local and global measures.
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Project description:Marissa Renardy and Denise Kirschner University of Michigan Medical School The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. These heteroge- neous patterns are also observed within individual states, with patches of concentrated outbreaks. Data is being generated daily at all of these spatial scales, and answers to questions regarded re- opening strategies are desperately needed. Mathematical modeling is useful in exactly these cases, and using modeling at a county scale may be valuable to further predict disease dynamics for the purposes of public health interventions. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, the home to University of Michigan, Eastern Michigan University, and Google, as well as serving as a sister city to Detroit, MI where there has been a serious outbreak. Here, we apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact net- works based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We also perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases on COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. Through simulations and sensitivity analyses, we explore mechanisms driving magnitude and timing of a second wave of infections upon re-opening. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.
| S-EPMC7359538 | biostudies-literature
Project description:The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, which is home to University of Michigan and Eastern Michigan University, and in close proximity to Detroit, MI, a major epicenter of the epidemic in Michigan. We apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons or long term care facilities). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases of COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. We find that delaying reopening does not reduce the magnitude of the second peak of cases, but only delays it. Reducing levels of casual contact, on the other hand, both delays and lowers the second peak. Through simulations and sensitivity analyses, we explore mechanisms driving the magnitude and timing of a second wave of infections upon re-opening. We find that the most significant factors are workplace and casual contacts and protective measures taken by infected individuals who have sought care. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.
| S-EPMC7455546 | biostudies-literature
Project description: Not available
| S-EPMC8776551 | biostudies-literature
Project description:During 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria's second wave to multiple geographically-structured epidemic time-series indicators. We achieved a good fit overall and for individual health services through a combination of time-varying processes, including case detection, population mobility, school closures, physical distancing and face covering usage. Estimates of the risk of death in those aged ≥75 and of hospitalisation were higher than international estimates, reflecting concentration of cases in high-risk settings. We estimated significant effects for each of the calibrated time-varying processes, with estimates for the individual-level effect of physical distancing of 37.4% (95%CrI 7.2-56.4%) and of face coverings of 45.9% (95%CrI 32.9-55.6%). That the multi-faceted interventions led to the dramatic reversal in the epidemic trajectory is supported by our results, with face coverings likely particularly important.
| S-EPMC8560916 | biostudies-literature
Project description:Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.
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Project description:Background and purposeThe experience gained during the first COVID-19 wave could have mitigated the negative impact on stroke care in the following waves. Our aims were to analyze the characteristics and outcomes of patients with stroke admitted during the second COVID-19 wave and to evaluate the differences in the stroke care provision compared with the first wave.MethodsThis retrospective multicenter cohort study included consecutive stroke patients admitted to any of the seven hospitals with stroke units (SUs) and endovascular treatment facilities in the Madrid Health Region. The characteristics of the stroke patients with or without a COVID-19 diagnosis were compared and the organizational changes in stroke care between the first wave (25 February to 25 April 2020) and second wave (21 July to 21 November 2020) were analyzed.ResultsA total of 550 and 1191 stroke patients were admitted during the first and second COVID-19 waves, respectively, with an average daily admission rate of nine patients in both waves. During the second wave, there was a decrease in stroke severity (median National Institutes of Health Stroke Scale 5 vs. 6; p = 0.000), in-hospital strokes (3% vs. 8.1%) and in-hospital mortality (9.9% vs. 15.9%). Furthermore, fewer patients experienced concurrent COVID-19 (6.8% vs. 19.1%), and they presented milder COVID-19 and less severe strokes. Fewer hospitals reported a reduction in the number of SU beds or deployment of SU personnel to COVID-19 dedicated wards during the second wave.ConclusionsDuring the second COVID-19 wave, fewer stroke patients were diagnosed with COVID-19, and they had less stroke severity and milder COVID-19.
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