Sociocultural behavioral traits in modelling the prediction of COVID-19 infection rates
Purpose – Over 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...
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Format: | Article |
Language: | English |
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Emerald Publishing
2021-09-01
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Series: | Journal of Humanities and Applied Social Sciences |
Subjects: | |
Online Access: | https://www.emerald.com/insight/content/doi/10.1108/JHASS-07-2021-0128/full/pdf?title=sociocultural-behavioral-traits-in-modelling-the-prediction-of-covid-19-infection-rates |
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author | Charles Alba Manasvi M. Mittal |
author_facet | Charles Alba Manasvi M. Mittal |
author_sort | Charles Alba |
collection | DOAJ |
description | Purpose – Over 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/approach – This 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. Findings – The 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/implications – The authors hope that this study would continue promoting the use of cultures and behaviors in modeling the spread of health diseases. Practical implications – The 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/value – The 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. |
first_indexed | 2024-04-11T20:34:15Z |
format | Article |
id | doaj.art-69f1317d44ff4b3292c2cc82376ebd51 |
institution | Directory Open Access Journal |
issn | 2632-279X |
language | English |
last_indexed | 2024-04-11T20:34:15Z |
publishDate | 2021-09-01 |
publisher | Emerald Publishing |
record_format | Article |
series | Journal of Humanities and Applied Social Sciences |
spelling | doaj.art-69f1317d44ff4b3292c2cc82376ebd512022-12-22T04:04:25ZengEmerald PublishingJournal of Humanities and Applied Social Sciences2632-279X2021-09-013533935510.1108/JHASS-07-2021-0128673308Sociocultural behavioral traits in modelling the prediction of COVID-19 infection ratesCharles Alba0Manasvi M. Mittal1Student Engagement Network, The Pennsylvania State University, University Park, PA, USACollege of Information, Sciences and Technology, The Pennsylvania State University, University Park, PA, USAPurpose – Over 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/approach – This 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. Findings – The 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/implications – The authors hope that this study would continue promoting the use of cultures and behaviors in modeling the spread of health diseases. Practical implications – The 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/value – The 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.https://www.emerald.com/insight/content/doi/10.1108/JHASS-07-2021-0128/full/pdf?title=sociocultural-behavioral-traits-in-modelling-the-prediction-of-covid-19-infection-rateshealth policycovid-19culturessociocultural behaviors |
spellingShingle | Charles Alba Manasvi M. Mittal Sociocultural behavioral traits in modelling the prediction of COVID-19 infection rates Journal of Humanities and Applied Social Sciences health policy covid-19 cultures sociocultural behaviors |
title | Sociocultural behavioral traits in modelling the prediction of COVID-19 infection rates |
title_full | Sociocultural behavioral traits in modelling the prediction of COVID-19 infection rates |
title_fullStr | Sociocultural behavioral traits in modelling the prediction of COVID-19 infection rates |
title_full_unstemmed | Sociocultural behavioral traits in modelling the prediction of COVID-19 infection rates |
title_short | Sociocultural behavioral traits in modelling the prediction of COVID-19 infection rates |
title_sort | sociocultural behavioral traits in modelling the prediction of covid 19 infection rates |
topic | health policy covid-19 cultures sociocultural behaviors |
url | https://www.emerald.com/insight/content/doi/10.1108/JHASS-07-2021-0128/full/pdf?title=sociocultural-behavioral-traits-in-modelling-the-prediction-of-covid-19-infection-rates |
work_keys_str_mv | AT charlesalba socioculturalbehavioraltraitsinmodellingthepredictionofcovid19infectionrates AT manasvimmittal socioculturalbehavioraltraitsinmodellingthepredictionofcovid19infectionrates |