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This paper analyses the use of Twitter as a presidential communication channel during the first few months of the COVID-19 crisis. The aim is to determine how four recently elected presidents (those of Spain, Argentina, Mexico and Brazil) managed their political communication, and to explore the thesis that they resorted to populist messages during the first months of their terms in office. Using a qualitative methodology and the XL Node tool to capture data, a comparative analysis was performed on the messages posted on their personal Twitter accounts during the first 20 weeks of 2020, classified in six categories: polarization; conspiracy; exaltation and leadership; personalisation and privacy; emotions and feelings; and media publicity. The results indicate that the four presidents share populist traits, but to a different extent. López Obrador and Bolsonaro display a more populist profile, with emotional appeals to the people and to their saving action as regards the implementation of health policies. Conversely, Alberto Fernández and Pedro Sánchez are more akin to the pop politician profile, posting photographs and media messages with a view to receiving press coverage. Both post tweets, based on values and historical events, aimed at their grassroots supporters. The main conclusion is that the pandemic has enhanced the presidential and personalist profiles of the four leaders, although their actions during the COVID-19 crisis were not necessarily in keeping with the populist paradigm. Thus, Sánchez and Bolsonaro implemented a health management communication strategy, while López Obrador and Fernández paid scant attention to health policy.

Populism has been at the center of recent debates in political science and international relations scholarship. Recognized as a contested concept and framed as a new global phenomenon, populism emerged in the context of liberal democracies, where political actors inflate social antagonisms by putting the people against the elite. Facing a global health crisis where a sense of threat, uncertainty, and emergency has pushed normal politics into the realm of politics of crisis, populists have actively engaged in creating a spectacularization of failure—of science, institutions, experts, governments—vis-à-vis the new Coronavirus, and in creating doubts about and devaluing scientists, experts and governments. Issues such as mask mandates, lockdown measures, compulsory vaccination, medicine effectiveness, and vaccine certificates became politicized. That is, they have been taken from normal politics and made contingent and controversial in order to deepen already existing political divisions and polarization. Exploring the case of Germany and Brazil, we will show how populists tried to use the pandemic to forge divisions between the people and the elite (represented by scientists, health experts, and the press). This conceptual-empirical paper wishes to make a contribution to the debate on how populists brought scientific public health issues into their black-and-white, antagonistic vision of society and hence instrumentalized COVID-19 for their own political gain. [ABSTRACT FROM AUTHOR]

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A SIRU-type epidemic model is employed for the prediction of the COVID-19 epidemy evolution in Brazil, and analyze the influence of public health measures on simulating the control of this infectious disease. The proposed model allows for a time variable functional form of both the transmission rate and the fraction of asymptomatic infectious individuals that become reported symptomatic individuals, to reflect public health interventions, towards the epidemy control. An exponential analytical behavior for the accumulated reported cases evolution is assumed at the onset of the epidemy, for explicitly estimating initial conditions, while a Bayesian inference approach is adopted for the estimation of parameters by employing the direct problem model with the data from the first phase of the epidemy evolution, represented by the time series for the reported cases of infected individuals. The evolution of the COVID-19 epidemy in China is considered for validation purposes, by taking the first part of the dataset of accumulated reported infectious individuals to estimate the related parameters, and retaining the rest of the evolution data for direct comparison with the predicted results. Then, the available data on reported cases in Brazil from 15 February until 29 March, is used for estimating parameters and then predicting the first phase of the epidemy evolution from these initial conditions. The data for the reported cases in Brazil from 30 March until 23 April are reserved for validation of the model. Then, public health interventions are simulated, aimed at evaluating the effects on the disease spreading, by acting on both the transmission rate and the fraction of the total number of the symptomatic infectious individuals, considering time variable exponential behaviors for these two parameters. This first constructed model provides fairly accurate predictions up to day 65 below 5% relative deviation, when the data starts detaching from the theoretical curve. From the simulated public health intervention measures through five different scenarios, it was observed that a combination of careful control of the social distancing relaxation and improved sanitary habits, together with more intensive testing for isolation of symptomatic cases, is essential to achieve the overall control of the disease and avoid a second more strict social distancing intervention. Finally, the full dataset available by the completion of the present work is employed in redefining the model to yield updated epidemy evolution estimates. [ABSTRACT FROM AUTHOR]