Suchergebnisse

We rely on data from India and the United States to show that political and economic centralization can influence the number of national parties in single-member simple-plurality electoral systems. Historically, in both countries the number of parties in local electoral districts has been near two, but the number of national parties has fluctuated. Periods of a small number of national parties in both countries correspond to periods of centralization. We argue that, as national governments centralize power and make policies that affect local areas, candidates have greater incentives to associate with national organizations, and voters have greater incentives to abandon locally competitive but nationally noncompetitive parties.

ObjectivesThe objective of this study is to explore how party systems can affect turnout by exploring the conditional effect of number of parties and party polarization on democracies.MethodsUsing Comparative Manifesto Project data from 26 democracies, this study develops a measure of party systems that interacts party polarization and number of parties to explain turnout.ResultsFindings show that the composition of the party system as a whole is a key determinate of a voter's propensity to vote. Highly polarized systems with few parties spur individuals to vote, while low levels of polarization and many parties reduce incentives to vote.ConclusionsResults have important implications for theories of turnout, resolving the confusion surrounding how party systems affect political participation.

We rely on data from India and the United States to show that political and economic centralization can influence the number of national parties in single-member simple-plurality electoral systems. Historically, in both countries the number of parties in local electoral districts has been near two, but the number of national parties has fluctuated. Periods of a small number of national parties in both countries correspond to periods of centralization. We argue that, as national governments centralize power and make policies that affect local areas, candidates have greater incentives to associate with national organizations, and voters have greater incentives to abandon locally competitive but nationally noncompetitive parties. (American Political Science Review / FUB)

A comprehensive investigation on the variation of the Covid-19 pandemic adequate reproduction number (R) in four critical European countries is provided in this study as a function of the variations related to the weather, mobility, government responses, and epidemiology. In this study, an open data set about the Covid-19 pandemic is used for the analysis. The data contains newly recorded components with different metrics since the appearance of the COVID19 pandemic. The aim is to investigate the impact of the weather, mobility, and government restriction on the R values and pandemic. The appropriate statistical analyses are used to reveal the association and the effect of the various attributes to the R. In addition, multiple predictive models were applied. The results show significant differences between studied countries and have different factors that influence the virus's spread.

Due to the particularities of SARS-CoV-2, public health policies have played a crucial role in the control of the COVID-19 pandemic. Epidemiological parameters for assessing the stage of the outbreak, such as the Effective Reproduction Number (R-t), are not always straightforward to calculate, raising barriers between the scientific community and non-scientific decision-making actors. The combination of estimators ofR(t)with elaborated Machine Learning-based forecasting techniques provides a way to support decision-making when assessing governmental plans of action. In this work, we develop forecast models applying logistic growth strategies and auto-regression techniques based on Auto-Regressive Integrated Moving Average (ARIMA) models for each country that records information about the COVID-19 outbreak. Using the forecast for the main variables of the outbreak, namely the number of infected (I), recovered (R), and dead (D) individuals, we provide a real-time estimation ofR(t)and its temporal evolution within a timeframe. With such models, we evaluateR(t)trends at the continental and country levels, providing a clear picture of the effect governmental actions have had on the spread. We expect this methodology of combining forecast models for raw data to calculateR(t)to serve as valuable input to support decision-making related to controlling the spread of SARS-CoV-2. ; Centre for Biotechnology and Bioengineering-CeBiB (PIA project, Conicyt, Chile) FB0001 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) 21181435

Due to the particularities of SARS-CoV-2, public health policies have played a crucial role in the control of the COVID-19 pandemic. Epidemiological parameters for assessing the stage of the outbreak, such as the Effective Reproduction Number (R_t), are not always straightforward to calculate, raising barriers between the scientific community and non-scientific decision-making actors. The combination of estimators of R_t with elaborated Machine Learning-based forecasting techniques provides a way to support decision-making when assessing governmental plans of action. In this work, we develop forecast models applying logistic growth strategies and auto-regression techniques based on Auto-Regressive Integrated Moving Average (ARIMA) models for each country that records information about the COVID-19 outbreak. Using the forecast for the main variables of the outbreak, namely the number of infected (I), recovered (R), and dead (D) individuals, we provide a real-time estimation of R_t and its temporal evolution within a timeframe. With such models, we evaluate R_t trends at the continental and country levels, providing a clear picture of the effect governmental actions have had on the spread. We expect this methodology of combining forecast models for raw data to calculate R_t to serve as valuable input to support decision-making related to controlling the spread of SARS-CoV-2.