Abstract
Public health related decisions often have to balance the cost of intervention strategies with the benefit of the reduction in disease burden. While the cost can often be inferred, forward modelling of the effect of different intervention options is complicated and disease specific. Here we introduce a package that is aimed to simplify this process. The package allows one to infer parameters using a Bayesian approach, perform forward modelling of the likely results of the proposed intervention and finally perform cost effectiveness analysis of the results. The package is based on a method previously used in the UK to inform vaccination strategies for influenza, with extensions to make it easily adaptable to other diseases and data sources.
| Original language | English |
|---|---|
| Article number | e1005838 |
| Journal | PLoS Computational Biology |
| Volume | 13 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - Nov 2017 |
Bibliographical note
Publisher Copyright:© 2017 van Leeuwen et al.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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