Debate: Facing uncertainty with(out) a sense of control – cultural influence on adolescents’ response to the COVID‐19 pandemic

Zhu, Nan; Jiaqing, O; Lu, Hui; Chang, Lei

doi:10.1111/camh.12408

Cited by 32 publications

(35 citation statements)

References 4 publications

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“…We obtain daily metrics of the implementation of NPIs at the national level from the Oxford COVID-19 Government Response Tracker (OxCGRT) dataset [1]. Although different countries may implement similar NPIs, some have suggested that cross-cultural variations across populations may lead to different perception and responses towards such NPIs [31][32][33]. We intend to capture any effects due to national cross-cultural differences by complementing the OxGCRT dataset with national cultural norm values from Hofstede's cultural dimensions [34].…”

Section: Description Of Studymentioning

confidence: 99%

Machine Learning Forecast of Growth in COVID-19 Confirmed Infection Cases with Non-Pharmaceutical Interventions and Cultural Dimensions: Algorithm Development and Validation

Yeung

Roewer-Després

Rosella

et al. 2021

Preprint

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BackgroundNational governments have implemented non-pharmaceutical interventions to control and mitigate against the COVID-19 pandemic. A deep understanding of these interventions is required.ObjectiveWe investigate the prediction of future daily national Confirmed Infection Growths – the percentage change in total cumulative cases across 14 days – using metrics representative of non-pharmaceutical interventions and cultural dimensions of each country.MethodsWe combine the OxCGRT dataset, Hofstede’s cultural dimensions, and COVID-19 daily reported infection case numbers to train and evaluate five non-time series machine learning models in predicting Confirmed Infection Growth. We use three validation methods – in-distribution, out-of-distribution, and country-based cross-validation – for evaluation, each applicable to a different use case of the models.ResultsOur results demonstrate high R2 values between the labels and predictions for the in-distribution, out-of-distribution, and country-based cross-validation methods (0.959, 0.513, and 0.574 respectively) using random forest and AdaBoost regression. While these models may be used to predict the Confirmed Infection Growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case.ConclusionsThis work provides new considerations in using machine learning techniques with non-pharmaceutical interventions and cultural dimensions data for predicting the national growth of confirmed infections of COVID-19.

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Section: Description Of Studymentioning

confidence: 99%

Machine Learning Forecast of Growth in COVID-19 Confirmed Infection Cases with Non-Pharmaceutical Interventions and Cultural Dimensions: Algorithm Development and Validation

Yeung

Roewer-Després

Rosella

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…We obtained daily metrics for the implementation of NPIs at the national level from the OxCGRT data set [1]. Although different countries may implement similar NPIs, researchers have suggested that cross-cultural variations across populations lead to different perceptions and responses toward these NPIs [53,54,68]. We intended to capture any effects due to national cross-cultural differences by complementing the OxCGRT data set with national cultural norm values from the Hofstede cultural dimensions [58].…”

Section: Description Of the Studymentioning

confidence: 99%

Machine Learning–Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation (Preprint)

Yeung¹,

Roewer-Després²,

Rosella³

et al. 2020

Preprint

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BACKGROUND National governments worldwide have implemented nonpharmaceutical interventions to control the COVID-19 pandemic and mitigate its effects. OBJECTIVE The aim of this study was to investigate the prediction of future daily national confirmed COVID-19 infection growth—the percentage change in total cumulative cases—across 14 days for 114 countries using nonpharmaceutical intervention metrics and cultural dimension metrics, which are indicative of specific national sociocultural norms. METHODS We combined the Oxford COVID-19 Government Response Tracker data set, Hofstede cultural dimensions, and daily reported COVID-19 infection case numbers to train and evaluate five non–time series machine learning models in predicting confirmed infection growth. We used three validation methods—in-distribution, out-of-distribution, and country-based cross-validation—for the evaluation, each of which was applicable to a different use case of the models. RESULTS Our results demonstrate high R2 values between the labels and predictions for the in-distribution method (0.959) and moderate R2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and adaptive boosting (AdaBoost) regression. Although these models may be used to predict confirmed infection growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case. CONCLUSIONS This work provides new considerations in using machine learning techniques with nonpharmaceutical interventions and cultural dimensions as metrics to predict the national growth of confirmed COVID-19 infections.

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“…Knowing others are following the same rules and rituals that serve collective interests might relieve worries about communicable diseases, thus causing people to be less prone to resort to outgroup prejudice as a behavioral immune response in collectivistic societies. It is conceivable that disease-prevention interventions and norms that necessitate temporary sacrifices of personal choice and freedoms for the greater good of public health (e.g., quarantines, community closures) might face more psychological backlash in individualistic societies (Zhu, O, Lu, & Chang, 2020), which historically faced lower pathogen stress (Murray & Schaller, 2010).…”

Section: Collectivistic Norms Reduces Divisiveness In the Face Of Dismentioning

confidence: 99%

Collectivistic Norms Facilitate Cooperation but Not Prejudice during a Pandemic

Zhu¹,

Lu²,

Chang³

2020

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Debate: Facing uncertainty with(out) a sense of control – cultural influence on adolescents’ response to the COVID‐19 pandemic

Cited by 32 publications

References 4 publications

Machine Learning Forecast of Growth in COVID-19 Confirmed Infection Cases with Non-Pharmaceutical Interventions and Cultural Dimensions: Algorithm Development and Validation

Machine Learning Forecast of Growth in COVID-19 Confirmed Infection Cases with Non-Pharmaceutical Interventions and Cultural Dimensions: Algorithm Development and Validation

Machine Learning–Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation (Preprint)

Collectivistic Norms Facilitate Cooperation but Not Prejudice during a Pandemic

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