COVID-19 Misinformation and Polarization on Twitter

2021 ◽  
Vol 13 (1) ◽  
pp. 19-36
Author(s):  
Rebecca Godard ◽  
Susan Holtzman
Keyword(s):  
Public Health ◽  
Social Network ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Conspiracy Theories ◽  
Connected Network ◽  
Network Analyses ◽  
The Stability ◽  
Social Network Analyses ◽  
Over Time

This study investigated polarization on Twitter related to the COVID-19 pandemic by examining tweets containing #Plandemic (suggests the pandemic is a hoax) or #StayHome (encourages compliance with health recommendations). Over 35,000 tweets from over 25,000 users were collected in April 2020 and examined using sentiment and social network analyses. Compared to #StayHome tweets, #Plandemic tweets came from a more tightly connected network, were higher in negative emotional content, and could be sub-divided into specific categories of misinformation and conspiracy theories. To evaluate the stability of users' COVID-related perspectives, the prevalence of pro- and anti-mask sentiment was measured in same users' tweets approximately four months later. Results revealed substantial stability over time, with 40% of #Plandemic users tweeting anti-mask hashtags compared to just 2% of #StayHome users. Findings demonstrate COVID-related polarization on Twitter and have implications for public health interventions to quell the propagation of misinformation.

scholarly journals SARS-CoV-2 shifting transmission dynamics and hidden reservoirs potentially limit efficacy of public health interventions in Italy

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Marta Giovanetti ◽  
Eleonora Cella ◽  
Francesca Benedetti ◽  
Brittany Rife Magalis ◽  
Vagner Fonseca ◽  
...  
Keyword(s):  
Public Health ◽  
Epidemiological Data ◽  
Health Interventions ◽  
Transmission Dynamics ◽  
Public Health Interventions ◽  
Viral Spread ◽  
Three Phase ◽  
Phylodynamic Analysis ◽  
Epidemic Wave ◽  
Over Time

AbstractWe investigated SARS-CoV-2 transmission dynamics in Italy, one of the countries hit hardest by the pandemic, using phylodynamic analysis of viral genetic and epidemiological data. We observed the co-circulation of multiple SARS-CoV-2 lineages over time, which were linked to multiple importations and characterized by large transmission clusters concomitant with a high number of infections. Subsequent implementation of a three-phase nationwide lockdown strategy greatly reduced infection numbers and hospitalizations. Yet we present evidence of sustained viral spread among sporadic clusters acting as “hidden reservoirs” during summer 2020. Mathematical modelling shows that increased mobility among residents eventually catalyzed the coalescence of such clusters, thus driving up the number of infections and initiating a new epidemic wave. Our results suggest that the efficacy of public health interventions is, ultimately, limited by the size and structure of epidemic reservoirs, which may warrant prioritization during vaccine deployment.

scholarly journals SARS-CoV-2 shifting transmission dynamics and hidden reservoirs limited the efficacy of public health interventions in Italy

2020 ◽  
Author(s):  
Marta Giovanetti ◽  
Eleonora Cella ◽  
Francesca Benedetti ◽  
Brittany Rife Magalis ◽  
Vagner Fonseca ◽  
...  
Keyword(s):  
Public Health ◽  
Epidemiological Data ◽  
Health Interventions ◽  
Transmission Dynamics ◽  
Public Health Interventions ◽  
Viral Spread ◽  
Three Phase ◽  
Phylodynamic Analysis ◽  
Epidemic Wave ◽  
Over Time

AbstractWe investigated SARS-CoV-2 transmission dynamics in Italy, one of the countries hit hardest by the pandemic, using phylodynamic analysis of viral genetic and epidemiological data. We observed the co-circulation of at least 13 different SARS-CoV-2 lineages over time, which were linked to multiple importations and characterized by large transmission clusters concomitant with a high number of infections. Subsequent implementation of a three-phase nationwide lockdown strategy greatly reduced infection numbers and hospitalizations. Yet we present evidence of sustained viral spread among sporadic clusters acting as “hidden reservoirs” during summer 2020. Mathematical modelling shows that increased mobility among residents eventually catalyzed the coalescence of such clusters, thus driving up the number of infections and initiating a new epidemic wave. Our results suggest that the efficacy of public health interventions is, ultimately, limited by the size and structure of epidemic reservoirs, which may warrant prioritization during vaccine deployment.

scholarly journals COVID-19 Pandemic: Questioning Conspiracy Theories, Beliefs or Claims that Have Potential Negative Impact on Public Health Interventions and Proposal for Integrated Communication and Information Dissemination Strategies (ICIDS)

2021 ◽  
Vol 8 (1) ◽  
pp. 1-21
Author(s):  
Aceme Nyika ◽  
Geraldine Taponeswa Nyika ◽  
Jeffrey Tonderai Nyika ◽  
Jeremy Tashinga Nyika ◽  
Trenah Nyika
Keyword(s):  
Public Health ◽  
Information Dissemination ◽  
Viral Infections ◽  
Negative Impact ◽  
Care Delivery ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Conspiracy Theories ◽  
The World ◽  
Integrated Communication

The COVID-19 outbreak that started in Wuhan, China, in December 2019 spread across the world causing a pandemic that infected and killed thousands of people globally. Countries made frantic efforts to put in place measures to curb the spread of the viral infections. The measures included social distancing, regular washing of hands with soap, applying sanitizers to hands and surfaces, use of personal protective equipment, screening, testing, isolation of suspected cases, quarantine of cases, lockdowns, treatment of cases and controlled burial of deceased cases.Almost all affected countries experienced four main hindrances to their efforts to control the COVID-19 pandemic; (i) challenges in implementing preventative measures effectively, (ii) health care delivery systems that could not cope with the pandemic, (iii) limited resources, and (iv) negative socio-economic impact caused by the pandemic. One of the challenges that hindered efforts to prevent the spread of the pandemic or to manage it are various conspiracy theories, beliefs, and or unproven claims, some of which are contradictory, that were circulated across the world.2This article gives an overview of the covid-19 pandemic, some conspiracy theories, beliefs and claims that were circulated as unofficial information, and questions the unofficial information. The article ends with an outline of some potential negative impact of conspiracy theories, beliefs and claims on public health interventions aimed at controlling the pandemic. In order to counter disinformation and misinformation, the article recommends the establishment of well-coordinated Integrated Communication and Information Dissemination Strategies (ICIDS) at global, continental, regional and national levels.

scholarly journals An analysis of COVID-19 clusters in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Sengupta ◽  
Bhaswati Ganguli ◽  
Sugata SenRoy ◽  
Aditya Chatterjee
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Population Density ◽  
Case Studies ◽  
Compartment Model ◽  
Health Interventions ◽  
Disease Spread ◽  
Second Phase ◽  
Public Health Interventions ◽  
Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

scholarly journals Impact of reduction of susceptibility to SARS-CoV-2 on epidemic dynamics in four early-seeded metropolitan regions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thomas J. Barrett ◽  
Karen C. Patterson ◽  
Timothy M. James ◽  
Peter Krüger
Keyword(s):  
Public Health ◽  
Growth Phase ◽  
Chronic Phase ◽  
Health Interventions ◽  
Resource Utilisation ◽  
Policy Planning ◽  
Infection Rates ◽  
Public Health Interventions ◽  
Metropolitan Regions ◽  
Epidemic Dynamics

AbstractAs we enter a chronic phase of the SARS-CoV-2 pandemic, with uncontrolled infection rates in many places, relative regional susceptibilities are a critical unknown for policy planning. Tests for SARS-CoV-2 infection or antibodies are indicative but unreliable measures of exposure. Here instead, for four highly-affected countries, we determine population susceptibilities by directly comparing country-wide observed epidemic dynamics data with that of their main metropolitan regions. We find significant susceptibility reductions in the metropolitan regions as a result of earlier seeding, with a relatively longer phase of exponential growth before the introduction of public health interventions. During the post-growth phase, the lower susceptibility of these regions contributed to the decline in cases, independent of intervention effects. Forward projections indicate that non-metropolitan regions will be more affected during recurrent epidemic waves compared with the initially heavier-hit metropolitan regions. Our findings have consequences for disease forecasts and resource utilisation.

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