Early Indicators of COVID-19 Infection Prevention Behaviors:Machine Learning Identifies Personal and Country-Level Factors

The Coronavirus is highly infectious and potentially deadly. In the absence of a cure or a vaccine, the infection prevention behaviors recommended by the World Health Organization constitute the only measure that is presently available to combat the pandemic. The unprecedented impact of this pandemic calls for swift identification of factors most important for predicting infection prevention behavior. In this paper, we used a machine learning approach to assess the relative importance of potential indicators of personal infection prevention behavior in a global psychological survey we conducted between March-May 2020 (N = 56,072 across 28 countries). The survey data were enriched with society-level variables relevant to the pandemic. Results indicated that the two most important indicators of self-reported infection prevention behavior were individual-level injunctive norms—beliefs that people in the community should engage in social distancing and self-isolation, followed by endorsement of restrictive containment measures (e.g., mandatory vaccination). Society-level factors (e.g., national healthcare infrastructure, confirmed infections) also emerged as important indicators. Social attitudes and norms were more important than personal factors considered most important by theories of health behavior. The model accounted for 52% of the variance in infection prevention behavior in a separate test sample—above the performance of psychological models of health behavior. These results suggest that individuals are intuitively aware that this pandemic constitutes a social dilemma situation, where their own infection risk is partly dependent on the behaviors of others. If everybody engaged in infection prevention behavior, the virus could be defeated even without a vaccine

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant

.Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individuallevel injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant

Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic

Before vaccines for COVID-19 became available, a set of infection prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection prevention behavior in 56,072 participants across 28 countries, administered in March-May 2020. The machine- learning model predicted 52% of the variance in infection prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual- level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically-derived predictors were relatively unimportant

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psycho-logical models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant.Web Information System

Early Indicators of COVID-19 Infection Prevention Behaviors: Machine Learning Identifies Personal and Country-Level Factors

The Coronavirus is highly infectious and potentially deadly. In the absence of a cure or a vaccine, the infection prevention behaviors recommended by the World Health Organization constitute the only measure that is presently available to combat the pandemic. The unprecedented impact of this pandemic calls for swift identification of factors most important for predicting infection prevention behavior. In this paper, we used a machine learning approach to assess the relative importance of potential indicators of personal infection prevention behavior in a global psychological survey we conducted between March-May 2020 (N = 56,072 across 28 countries). The survey data were enriched with society-level variables relevant to the pandemic. Results indicated that the two most important indicators of self-reported infection prevention behavior were individual-level injunctive norms—beliefs that people in the community should engage in social distancing and self-isolation, followed by endorsement of restrictive containment measures (e.g., mandatory vaccination). Society-level factors (e.g., national healthcare infrastructure, confirmed infections) also emerged as important indicators. Social attitudes and norms were more important than personal factors considered most important by theories of health behavior. The model accounted for 52above the performance of psychological models of health behavior. These results suggest that individuals are intuitively aware that this pandemic constitutes a social dilemma situation, where their own infection risk is partly dependent on the behaviors of others. If everybody engaged in infection prevention behavior, the virus could be defeated even without a vaccine

Early Indicators of COVID-19 Infection Prevention Behaviors: Machine Learning Identifies Personal and Country-Level Factors

The Coronavirus is highly infectious and potentially deadly. In the absence of a cure or a vaccine, the infection prevention behaviors recommended by the World Health Organization constitute the only measure that is presently available to combat the pandemic. The unprecedented impact of this pandemic calls for swift identification of factors most important for predicting infection prevention behavior. In this paper, we used a machine learning approach to assess the relative importance of potential indicators of personal infection prevention behavior in a global psychological survey we conducted between March-May 2020 (N = 56,072 across 28 countries). The survey data were enriched with society-level variables relevant to the pandemic. Results indicated that the two most important indicators of self-reported infection prevention behavior were individual-level injunctive norms—beliefs that people in the community should engage in social distancing and self-isolation, followed by endorsement of restrictive containment measures (e.g., mandatory vaccination). Society-level factors (e.g., national healthcare infrastructure, confirmed infections) also emerged as important indicators. Social attitudes and norms were more important than personal factors considered most important by theories of health behavior. The model accounted for 52above the performance of psychological models of health behavior. These results suggest that individuals are intuitively aware that this pandemic constitutes a social dilemma situation, where their own infection risk is partly dependent on the behaviors of others. If everybody engaged in infection prevention behavior, the virus could be defeated even without a vaccine