How can my organization ensure responsible data practice when developing or using models in the COVID-19 response?

Epidemic models are an essential tool in the hands of governments and policy makers for planning and responding to COVID-19. This crisis shows how predictive analytics can inform and maximise the impact of interventions, especially in resource-limited contexts. It also shows the importance of having models that are validated and ready to be deployed right before or at the beginning of a crisis.

Unfortunately, translating the outputs of predictive models into timely and appropriate responses in the humanitarian sector remains a challenge for several reasons:

  1. First, there is no common standard for documenting predictive models and their intended use which highlights the critical aspects for the application of models in the humanitarian sector.
  2. Second, there is no common standard or mechanism for assessing the technical rigor and operational readiness of predictive models in the sector.
  3. Third, the development of predictive models is often led by technical specialists who may not consider important ethical concerns that the application of models in humanitarian contexts may entail.

One approach for addressing these challenges is to submit models for peer review. The Centre for Humanitarian Data recently published an updated version of its Peer Review Framework for Predictive Analytics in Humanitarian Response. The Framework aims to create standards and processes for the use of models in our sector. It is based on research with experts and stakeholders across a range of organizations that design and use predictive models. The Framework also draws on best practices from academia and the private sector.