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Pandemic and Partisan Polarisation: Voter Evaluation of UK Government Handling During Covid‐19

ABSTRACT: Abstract What is the effect of pandemics on partisan perceptions of government competence? Taking the case of Covid‐19 in the UK, we explore how voters’ assessments of the government’s handling of the economy and health were affected by four events: the first UK Covid‐19 death; the national lockdown; Boris Johnson’s hospitalisation; and Cummings’ scandal. Using a large representative weekly survey in the last year totalling over around 30’000 respondents, our results show that Labour voters had the worst assessments of government handling. The first death deteriorated perceptions of government handling of health among both Labour and Conservative voters, while Boris Johnson’s hospitalisation improved perceptions among most voters. Lockdown improved the perception of health handling but at the cost of more negative perceptions of its handling of the economy. The Cummings scandal had a negative effect on perceptions of government handling of economy but surprisingly improved perceptions of its handling of health.

SUBMITTER: Vlandas T

PROVIDER: S-EPMC8242772 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed. Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching-learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic methods on the experiment problems. To validate the current model, a set of sensitivity analysis on important parameters and a real case study in the United States are provided. Based on the empirical experimental results, computational time and eight assessment metrics proposed methodology seems to work well for the considered problems. The results show that by raising the transportation costs, the total cost and the environmental impacts of sustainability increased steadily and the trend of the social responsibility of staff rose gradually between - 20 and 0%, but, dropped suddenly from 0 to + 20%. Also in terms of the on-resiliency of the proposed network, the trends climbed slightly and steadily. Applications of this paper can be useful for hospitals, pharmacies, distributors, medicine manufacturers and the Ministry of Health.Supplementary informationThe online version contains supplementary material available at 10.1007/s10479-021-04238-2.

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Pandemic and Partisan Polarisation: Voter Evaluation of UK Government Handling During Covid‐19

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