COVID-19: how much unemployment was caused by the shutdown in Germany?

Bauer, Anja; Weber, Enzo

doi:10.1080/13504851.2020.1789544

Cited by 112 publications

(79 citation statements)

References 3 publications

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“…The social vulnerabilities in the COVID-19 context and its mitigation measures relate to social problems, such as unemployment, gender violence, anxiety and depression, or homelessness. In Germany, the impact of the shutdown measures in April 2020 increased unemployment in the short-term [24] and the report "The COVID confinement measures and EU labour markets", which analyzes the restriction on activities in three EU member states (Spain, Italy, and Germany) [25], shows asymmetric effects of the COVID lockdown measures across different groups of workers. In particular, the report states that restrictions on economic activity are mainly affecting workers with lower wages and worse employment conditions, with impact significantly appearing in women and young workers.…”

Section: Social Vulnerabilities and Social Problems Raised In The Covmentioning

confidence: 99%

Social Work during the COVID-19 Crisis: Responding to Urgent Social Needs

et al. 2020

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Social work during the COVID-19 crisis has faced one of the most challenging times to cover urgent social needs in an uncertain scenario. This study analyzes the immediate responses in social work to vulnerable groups in the first 15 days of the pandemic in Barcelona, one of the most affected areas worldwide by COVID-19. The sample for this qualitative study includes 23 semi-structured interviews with social workers from different fields of intervention, from general approaches (primary care) to specific ones (health, ageing, homeless, and justice). The data analysis followed the communicative methodology, including transformative and exclusionary dimensions, and the analytical categories focused on the impact of the COVID-19 pandemic on social services users, the organizational responses of social workers, and the impact of the interventions to cover urgent social needs of attendees. The interventions have been accompanied by an improvement in communication channels with vulnerable groups, ensuring an understanding of the situation of families and individuals, and covering the most urgent social needs. The study shows the key role of social workers from diverse social attention tools and their contribution to the sustainability of social services with a long-term impact.

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Section: Social Vulnerabilities and Social Problems Raised In The Covmentioning

confidence: 99%

Social Work during the COVID-19 Crisis: Responding to Urgent Social Needs

et al. 2020

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“…We assume that people believe the disease is 'coming closer' and thus perceive it as a higher health risk when the case numbers in their county increase. Rising case numbers are also related to restrictions in private and public life (Bauer and Weber 2020a), which in turn have negative economic consequences (Bauer and Weber 2020b). Therefore, we further assume that rising case numbers in a county increase the perception of financial risks as well.…”

Section: Experiential Factorsmentioning

confidence: 99%

The impact of the COVID-19 pandemic on risk perceptions: differences between ethnic groups in Germany

2020

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Since the start of the COVID-19 pandemic, people perceive their health and finances to be at greater risk than before. Using data from CILS4COVID, an add-on study to the German long-term project CILS4EU-DE, surveying young adults aged 24-26, we show that these risk perceptions are prevalent in populations with a former Yugoslavian, Turkish, and Asian background, although we find only few differences between the German majority and the ethnic minority groups overall. Contrary to expectations, we were not able to fully explain these systematic differences with sociodemographic, experiential and sociocultural factors. Nevertheless, our analysis provides important insights into mechanisms underlying the increased perception of risk during the pandemic irrespective of respondents' ethnic origin.

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“…While it is difficult to isolate effects of singular NPIs on the pandemic, there is by now ample evidence that NPIs are generally an adequate strategy to reduce the reproduction number R t , and thus the daily numbers of new infections, hospitalizations, and deaths related to COVID-19 (Flaxman et al 2020; Li et al 2020a; Dehning et al 2020). Whereas NPIs are effective in reducing the transmission of COVID-19, interventions such as stay-at-home orders and school closures can have profound negative effects on the economy, social life and individual well-being (Fuchs-Schündeln, Kuhn, & Tertilt 2020; Naumann et al 2020; Bäuerle et al 2020; Bauer & Weber 2020). Therefore, policy makers and other stakeholders aim to use NPIs with caution, while reducing the spread of COVID-19 sufficiently in order not to overburden health care systems.…”

Section: Introductionmentioning

confidence: 99%

Forecasting intensive care unit demand during the COVID-19 pandemic: A spatial age-structured microsimulation model

Klüsener

Schneider²,

Rosenbaum‐Feldbrügge

et al. 2020

Preprint

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BackgroundThe COVID-19 pandemic poses the risk of overburdening health care systems, and in particular intensive care units (ICUs). Non-pharmaceutical interventions (NPIs), ranging from wearing masks to (partial) lockdowns have been implemented as mitigation measures around the globe. However, especially severe NPIs are used with great caution due to their negative effects on the economy, social life and mental well-being. Thus, understanding the impact of the pandemic on ICU demand under alternative scenarios reflecting different levels of NPIs is vital for political decision-making on NPIs.ObjectiveThe aim is to support political decision-making by forecasting COVID-19-related ICU demand under alternative scenarios of COVID-19 progression reflecting different levels of NPIs. Substantial sub-national variation in COVID-19-related ICU demand requires a spatially disaggregated approach. This should not only take sub-national variation in ICU-relevant disease dynamics into account, but also variation in the population at risk including COVID-19-relevant risk characteristics (e.g. age), and factors mitigating the pandemic. The forecast provides indications for policy makers and health care stakeholders as to whether mitigation measures have to be maintained or even strengthened to prevent ICU demand from exceeding supply, or whether there is leeway to relax them.MethodsWe implement a spatial age-structured microsimulation model of the COVID-19 pandemic by extending the Susceptible-Exposed-Infectious-Recovered (SEIR) framework. The model accounts for regional variation in population age structure and in spatial diffusion pathways. In a first step, we calibrate the model by applying a genetic optimization algorithm against hospital data on ICU patients with COVID-19. In a second step, we forecast COVID-19-related ICU demand under alternative scenarios of COVID 19 progression reflecting different levels of NPIs. We apply the model to Germany and provide state-level forecasts over a 2-month period, which can be updated daily based on latest data on the progression of the pandemic.ResultsTo illustrate the merits of our model, we present here “forecasts” of ICU demand for different stages of the pandemic during 2020. Our forecasts for a quiet summer phase with low infection rates identified quite some variation in potential for relaxing NPIs across the federal states. By contrast, our forecasts during a phase of quickly rising infection numbers in autumn (second wave) suggested that all federal states should implement additional NPIs. However, the identified needs for additional NPIs varied again across federal states. In addition, our model suggests that during large infection waves ICU demand would quickly exceed supply, if there were no NPIs in place to contain the virus.ConclusionOur results provide evidence for substantial spatial variation in (1) the effect of the pandemic on ICU demand, and (2) the potential and need for NPI adjustments at different stages of the pandemic. Forecasts with our spatial age-structured microsimulation model allow to take this spatial variation into account. The model is programmed in R and can be applied to other countries, provided that reliable data on the number of ICU patients infected with COVID-19 are available at sub-national level.

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COVID-19: how much unemployment was caused by the shutdown in Germany?

Cited by 112 publications

References 3 publications

Social Work during the COVID-19 Crisis: Responding to Urgent Social Needs

Social Work during the COVID-19 Crisis: Responding to Urgent Social Needs

The impact of the COVID-19 pandemic on risk perceptions: differences between ethnic groups in Germany

Forecasting intensive care unit demand during the COVID-19 pandemic: A spatial age-structured microsimulation model

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