Analysis of lockdown perception in the United States during the COVID-19 pandemic

Abstract

Containment measures have been applied throughout the world to halt the COVID-19 pandemic. In the United States, several forms of lockdown have been adopted in different parts of the country, leading to heterogeneous epidemiological, social, and economic effects. Here, we present a spatio-temporal analysis of a Twitter dataset comprising 1.3 million geo-localized Tweets about lockdown, from January to May 2020. Through sentiment analysis, we classified Tweets as expressing positive or negative emotions about lockdown, demonstrating a change in perception during the course of the pandemic modulated by socio-economic factors. A transfer entropy analysis of the time series of Tweets unveiled that the emotions in different parts of the country did not evolve independently. Rather, they were mediated by spatial interactions, which were also related to socio-ecomomic factors and, arguably, to political orientations. This study constitutes a first, necessary step toward isolating the mechanisms underlying the acceptance of public health interventions from highly resolved online datasets.

A mechanistic model for airborne and direct human-to-human transmission of COVID-19: effect of mitigation strategies and immigration of infectious persons

“…After that, various mathematical models were proposed by considering several stages of susceptible and infected populations [19,36]. Many researchers studied the dynamics of COVID-19 using real incidence data of the affected countries and examined different characteristics of the outbreak as well as evaluated the effect of intervention strategies implemented to suppress the outbreak in respective countries [14,27,41]. In addition, a comparative analysis of the most affected countries during the second wave of COVID-19 was done by Easwaramoorthy et al [10] using fractal-based prognostic model.…”

A proposed fractional dynamic system and Monte Carlo-based back analysis for simulating the spreading profile of COVID-19

“…As of October 01, 2021, there have been more than 230 million confirmed cases, together with nearly 4.8 million deaths reports in the world [ 12 ]. Throughout anti-epidemic struggles, apart from medical, biological, and scientific research, theoretical studies based on mathematical and statistical modeling may thus significantly contribute to the conception of outbreak attributes [ 13 – 15 ]. Forecasting the trajectory of the spread accordingly helps countries make informed decisions on the required actions to mitigate the spread of the disease.…”

An ANFIS model-based approach to investigate the effect of lockdown due to COVID-19 on public health

“…A study on mental health due to the lockdown has been done by Hirematha et al [ 20 ]. Analysis of lockdown perception in the United States during the COVID-19 pandemic has also been done by Surano et al [ 21 ]. But, to date, there are not so many significant works that have analyzed the effect of lockdown on public health.…”