From prevention to response: A holistic exploration of factors shaping Global Health Security

Abstract

In the face of global challenges, ensuring robust health security is paramount for safeguarding populations against emerging threats. Using country-level data on the Global Health Security (GHS) index covering 195 countries, this study employs Bayesian Belief Networks (BBNs) to explore probabilistic dependencies among various indicators that can influence health security outcomes. The findings reveal distinct probabilities of low performance for certain components within main indicators such as ‘prevention’, ‘early detection and reporting’, and ‘sufficient and robust health sector’, significantly shaping overall health security outcomes. Particularly noteworthy is the identification of ‘early detection and reporting’ as the most critical indicator, showing an 87% probability improvement, followed closely by ‘prevention’ at 81%. The latter part of the study delves into the sub-indicators associated with ‘early detection and reporting’. This analysis uncovers varying probabilities of extreme performance states, with ‘laboratory supply chains’ emerging as the most crucial sub-indicator, presenting an 84% probability improvement. Conversely, the ‘epidemiology workforce’ is deemed less influential in impacting overall health security outcomes. Assessing the mutual value of information sheds light on the informative nature of ‘prevention’ and ‘sufficient and robust health sector’ within the main indicators, while in sub-indicators, ‘surveillance data accessibility and transparency’ take precedence.

Keywords

• Bayesian belief networks
• Global Health Security
• Early detection and reporting
• Mutual value of information
• Supply chains