Datasets on how misinformation promotes immune perception of COVID-19 pandemic in Africa

Abstract

The dataset investigates the magnitude of the misinformation content influencing scepticisms about the novel COVID-19 pandemic in Africa. The data is collected via an electronic questionnaire method and twenty-one Africa countries randomly participated. Responses were received from all the five regions of Africa. The data is structured to identify some leading misinformation been propagated in the media. For data, in brief, we performed a descriptive analysis of the data and also examine the degree of each selected misinformation contents on the immune perception of respondents using Confirmatory Factor Analysis. Another research can use the dataset to investigate how misinformation and religion misconception promote ignorance about disease or pandemic in Africa or the dataset could serve as supplementary material for further investigation of COVID-19 pandemic in Africa.

Energy pricing during the COVID-19 pandemic: Predictive information-based uncertainty indexes with machine learning algorithm

“…The GFI (health news-related index) from Salisu & Akanni (2020) and CIU (based on Google trends search volume) from the procedure described in Olubusoye et al (2021) . It is informed by the perceived connectedness and possible bidirectional causality of uncertainty and misinformation, as the latter is likely to spur the former, while the uncertainty could breed an opportunity for misinformation (see Akintande & Olubusoye, 2020 ). Hence, we consider search volumes on the Google Trends database relating to misinformation around the COVID-19 pandemic, using keywords such as "COVID-19 Fake news", "Fake news", "COVID-19 Myth", "COVID and Age", "COVID and Race" and "COVID and Bleach".…”

“…These indexes are limited. Since access to quality information ( Akintande & Olubusoye, 2020 ) is most likely to affect an individual's perception and decision more than the infection and mortality figures. Thus, misinformation may catalyze the understanding of investment risks ( Norouzi et al, 2020 ).…”

“…Fortunately, this study is not in the vacuum of this procedure. Several studies have researched the impact of COVID-19 on the commodity market, (see for instance; Salisu et al, 2020 a, and Olubusoye et al, (2021) among others) and other related areas such as misinformation (e.g., Akintande & Olubusoye (2020) , Galvão (2020) , among others). Therefore, several extant pieces of literature are detailing different methodological procedures on the subject and COVID-19 related impact on the commodity prices (see Sharif et al, 2020; Salisu et al, 2020 ; Salisu & Ogbonna, 2019 ; Yaya et al, 2017 ; among many others).…”

Investigating the transmission dynamics of SARS-CoV-2 in Nigeria: A SEIR modelling approach

“…This observation was in tandem with the average R 0 of 3.28 reported in another study from China [34] , which was obtained later when the population might have acquired some level of immune resistance. Control measures and non-pharmaceutical interventions (such as the rational use of face masks, rigorous public awareness, contact tracing, immediate isolation and prompt treatment 35 , 36 , 37 , 38 ) likely promoted a significant increase in risk perception of the virus and behaviour change [39] , but not sufficient to significantly reduce the community transmission of SARS-CoV-2. It is also worth noting that R 0 estimates can be affected by several factors such as; genetic, behavioural, environmental, etc.…”

Dataset of Coronavirus Content From Instagram With an Exploratory Analysis

“…In another study [18], to help the evaluation of the determinants and impact of the COVID-19 at a large scale, the authors present a new dataset with socio-demographic, economic, public policy, health, pollution and environmental factors for the European Union. Akindtande et al [19] present a dataset that investigates the magnitude of the misinformation content influencing scepticisms about the novel COVID-19 pandemic in Africa and the data is collected via an electronic questionnaire method from twenty-one Africa countries. In medicine, Sass et al introduce the ''German Corona Consensus Dataset'' (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data [20].…”