Abstract Background: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. Conclusions: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.
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Social media play an important role in the rapidly changing dynamics of government organizations and their interaction with the public. Governments are facing changing demands at organizational level due to the exponential growth of connections, networks involved in social issues and collaboration within and across organizational boundaries (Van Berlo, 2012). Vocal citizens are increasingly expressing their opinions openly and clearly, anywhere and any time. Sometimes they even get involved in (the creation of) government policy. Social media have the capacity to strengthen and facilitate online dialogue in society.
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In this chapter the autor explores the contours and possible effects of the WMO for the local government level. First she gives a short overview of the main features of the WMO (PAR. 2.5). Then she describes the challenges that local government is confronted with, especially the political decisions that have to be taken at the local level (PAR. 2.3). The question to be answered is whether or not the WMO means an impulse for local democracy in the Netherlands. To that purpose, two quick comparisons are made (PAR. 2.4): with other decentralisation operations in the Netherlands, and with the decentralisation of social care and welfare in Sweden. These comparisons make it possible to determine two main conditions for creating an impulse for local politics, which are presented in PAR. 2.5. The article ends by some concluding remarks on the effect of the WMO on the local democracy in the Netherlands.
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