Beyond crystal balls: Crosscutting solutions in global health to prepare for an unpredictable future

Wladimir Jimenez Alonso, Benjamin Joseph James McCormick*, Mark A. Miller, Cynthia Schuck-Paim, Ghassem R. Asrar

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)


Background: Efforts in global heath need to deal not only with current challenges, but also to anticipate new scenarios, which sometimes unfold at lightning speed. Predictive modeling is frequently used to assist planning, but outcomes depend heavily on a subset of critical assumptions, which are mostly hampered by our limited knowledge about the many factors, mechanisms and relationships that determine the dynamics of disease systems, by a lack of data to parameterize and validate models, and by uncertainties about future scenarios. Discussion: We propose a shift from a focus on the prediction of individual disease patterns to the identification and mitigation of broader fragilities in public health systems. Modeling capabilities should be used to perform "stress tests" on how interrelated fragilities respond when faced with a range of possible or plausible threats of different nature and intensity. This system should be able to reveal crosscutting solutions with the potential to address not only one threat, but multiple areas of vulnerability to future health risks. Summary: Actionable knowledge not based on a narrow subset of threats and conditions can better guide policy, build societal resilience and ensure effective prevention in an uncertain world.

Original languageEnglish
Article number955
JournalBMC Public Health
Issue number1
Publication statusPublished - 24 Sept 2015

Bibliographical note

Funding Information:
We would like to acknowledge the insight and experience of Drs FE McKenzie and JP Rosenthal in commenting on the proposed idea. This work was funded by the Fogarty International Centre, National Institutes of Health.

Publisher Copyright:
© 2015 Alonso et al.


  • Health risks
  • Models
  • Prediction
  • Public health
  • Resilience


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