Sociocultural behavioral traits in modelling the prediction of COVID-19 infection rates

Charles Alba,Manasvi M Mittal

doi:10.1108/jhass-07-2021-0128

Abstract

PurposeOver the past decades, many health authorities and public policy experts have traditionally relied on indicators that are dependent on a nation's economy, its health-care infrastructure advancements, and superiority in biomedical sciences and technology to predict potential infection rates should a health pandemic occur. One such commonly relied-upon indicator was that of the Global Health Security (GHS) Index. However, the coronavirus disease 2019 (COVID-19) pandemic has shown how such variables prove to be inaccurate in predicting the infection rates during a global health pandemic. Hence, this paper proposes the utilization of socio-cultural behavioral traits to predict a country's COVID-19 infection rates.Design/methodology/approachThis is achieved by proposing a model involving the classification and regression tree (CART) algorithm and a Poisson regression against the six selected cultural behavioral predictors consisting of individualism, power distance, masculinity, uncertainty avoidance, long-term orientation, and indulgence.FindingsThe results show that all the selected cultural behavioral predictors are significant in impacting COVID-19 infection rates. Furthermore, the model outperforms the conventional GHS Index model based on a means squared error comparison.Research limitations/implicationsThe authors hope that this study would continue promoting the use of cultures and behaviors in modeling the spread of health diseases.Practical implicationsThe authors hope that their works could prove beneficial to public office holders, as well as health experts working in health facilities, in better predicting potential outcomes during a health pandemic, thus allowing them to plan and allocate resources efficiently.Originality/valueThe results are a testament to the fact that sociocultural behavioral traits are more reliant predictors in modeling cross-national infection rates of global health pandemics, like that of COVID-19, as compared to economic-centric indicators.

Highlights

For the longest time, authorities, researchers, and planners at a local, national, and global level have heavily relied on indicators that are a function of a nation’s economic stability and its advances in health-care infrastructure and technology to predict the spread of viruses during a global health pandemic
It is worth noting that the Global Health Security (GHS) model has predicted a country’s preparedness and ability to handle the pandemic in the opposite direction to the actual happenings of the COVID-19 pandemic in terms of the number of COVID infection rates per million population
5.1 Model accuracy and reliability Our results show that our selected sociocultural behavioral traits are more accurate predictors in modeling the spread of viruses, like COVID-19, as compared to indicators that are dependent on a country’s economic strength and advancements in biotechnology, like the GHS Index

Summary

Introduction

Authorities, researchers, and planners at a local, national, and global level have heavily relied on indicators that are a function of a nation’s economic stability and its advances in health-care infrastructure and technology to predict the spread of viruses during a global health pandemic. As shown in the figures in relation to the coronavirus disease 2019 (COVID-19) pandemic (like that of Figure 1), indexes like that of the GHS Index have come under scrutiny from medical or health-care experts as well as public policy experts alike for its failure toward coming remotely close to predicting or forecasting the real figures of the COVID-19 pandemic (Aitken et al, 2020; Abbey et al, 2020) This has led many to question the viability of such metrics for future uses (Aitken et al, 2020; Abbey et al, 2020; Dalglish, 2020)

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