Economic optimization of a global strategy to address the pandemic threat - PubMed (original) (raw)

Economic optimization of a global strategy to address the pandemic threat

Jamison Pike et al. Proc Natl Acad Sci U S A. 2014.

Abstract

Emerging pandemics threaten global health and economies and are increasing in frequency. Globally coordinated strategies to combat pandemics, similar to current strategies that address climate change, are largely adaptive, in that they attempt to reduce the impact of a pathogen after it has emerged. However, like climate change, mitigation strategies have been developed that include programs to reduce the underlying drivers of pandemics, particularly animal-to-human disease transmission. Here, we use real options economic modeling of current globally coordinated adaptation strategies for pandemic prevention. We show that they would be optimally implemented within 27 y to reduce the annual rise of emerging infectious disease events by 50% at an estimated one-time cost of approximately 343.7billion.WethenanalyzeWorldBankdataonmultilateral"OneHealth"pandemicmitigationprograms.Wefindthat,becausemostpandemicshaveanimalorigins,mitigationisamorecost−effectivepolicythanbusiness−as−usualadaptationprograms,savingbetween343.7 billion. We then analyze World Bank data on multilateral "One Health" pandemic mitigation programs. We find that, because most pandemics have animal origins, mitigation is a more cost-effective policy than business-as-usual adaptation programs, saving between 343.7billion.WethenanalyzeWorldBankdataonmultilateral"OneHealth"pandemicmitigationprograms.Wefindthat,becausemostpandemicshaveanimalorigins,mitigationisamorecost−effectivepolicythanbusiness−as−usualadaptationprograms,savingbetween344.0.7 billion and $360.3 billion over the next 100 y if implemented today. We conclude that globally coordinated pandemic prevention policies need to be enacted urgently to be optimally effective and that strategies to mitigate pandemics by reducing the impact of their underlying drivers are likely to be more effective than business as usual.

Keywords: One Health; adaptation; climate change; emerging infectious diseases; mitigation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Real options model. Shown is the structure of our real options model to enable optimal timing of business-as-usual global adaptive policy to reduce the rise in frequency of emerging infectious diseases (EIDs). The y axis represents the net present value of the expected damages of an EID outbreak plus the cost, K, of a policy if implemented. The x axis represents expected damages/time. The blue line represents expected damages following business as usual and the value of waiting is not considered. The green line represents the evolution of EID damages if a policy with cost, K, is implemented. If the value of waiting is ignored, _D_∼ is the threshold at which a policy should be implemented. The red line, known as the “continuation value,” illustrates the expected damages under business as usual, including the value of waiting. The decision model simply takes the currently experienced damage, a point on the x axis, and determines which of the three lines is lowest (has lowest expected present damages and costs). For damages less than D* it is optimal to “continue” to wait. For all damages above D* it is optimal to implement the policy. D* is the optimal threshold. Full model development and simulations from the parameterized model are in SI Text.

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Economic optimization of a global strategy to address the pandemic threat - PubMed (original) (raw)