Choosing the right COVID-19 indicator: crude mortality, case fatality, and infection fatality rates influence policy preferences, behaviour, and understanding

Abstract

Individuals worldwide are overwhelmed with news about COVID-19. In times of pandemic, media alternate the usage of different COVID-19 indicators, ranging from the more typical crude mortality rate to the case fatality rate, and the infection fatality rate continuously. In this article, we used experimental methods to test whether and how the treatment of individuals with different types of information on COVID-19 is able to change policy preferences, individual and social behaviours, and the understanding of COVID-19 indicators. Results show that while the usage of the crude mortality rate proves to be more efficient in terms of supporting policy preferences and behaviours to contain the virus, all indicators suffer from a significant misunderstanding on behalf of the population.

Using correlation analysis to examine the impact of Covid-19 pandemics on various socioeconomic aspects: Case study of Indonesia

“…Collectively, the hypothesis offering frames the connections between variables whose outcome is the hypothesis accepted or otherwise. Gaucher et al, 2022;Musikanski et al, 2017;Soylu et al, 2018;Streeten, 1994;Yulia dan Irina, 2020;Salehi et al, 2012;Muñoz-Mora et al, 2022;Melde, 2012;Akin dan Banfi, 2019;Choi et al, 2019;Bhaskaran et al, 2021;Walby et al, 2017;Robinson, 2003;Rakovec-Felser, 2014;Merkin, 2012;Bondestam dan Lundqvist, 2020;Focacci et al, 2022;Patel et al, 2020). Source: modified from Schmidt and Osebold, 2017;Singh, 2013 The operationalization of the above variables decorates nine hypothetical targets based on the premise.…”

“…Figure 1. Conceptual framework (Source: compiled from Varlamovaa danLarionova , 2015; Tapsin dan Hepsag, 2014;Gaucher et al, 2022;Musikanski et al, 2017;Soylu et al, 2018;Streeten, 1994; Yulia dan Irina, 2020;Salehi et al, 2012;Muñoz-Mora et al, 2022;Melde, 2012; Akin dan Banfi, 2019;Choi et al, 2019;Bhaskaran et al, 2021;Walby et al, 2017;Robinson, 2003;Rakovec-Felser, 2014;Merkin, 2012; Bondestam dan Lundqvist, 2020;Focacci et al, 2022;Patel et al, 2020).…”

Aggressive Measures, Rising Inequalities and Mass Formation During the COVID-19 Crisis: An Overview and Proposed Way Forward

“…Governments and public health authorities around the world have felt the urgency to impose decisions on people, while having trouble using evidence-based decision and policy making (Eden & Wagstaff, 2021;Focacci et al, 2022;Schippers & Rus, 2021). This has proven to be quite harmful to many groups in society (Abbasi, 2020;Schippers, 2020).…”

“…This has proven to be quite harmful to many groups in society (Abbasi, 2020;Schippers, 2020). Many scientists also went along with the narrative that the most aggressive NPIs were necessary for the greater good, for instance experts giving advice on how to modify behavior (e.g., Bavel et al, 2020b;Focacci et al, 2022). Others have pointed out that the debate has been highly polarized and should ideally be more open-minded and nuanced (Escandón et al, 2021).…”

The Pattern of COVID-19 Deaths per Million in Relation to Population Growth Rates

“…We look through this study to nd a correlation of the PGR to the COVID-19 deaths per one million populations as crude mortality rate which is a ratio of the number of people that die from a disease divided by the total number of individuals in a population. 15 This study can look for age structure and age distribution of the disease by other prospective i.e. the growth rate.…”