Learning from weather and climate science to prepare for a future pandemic
Established pandemic models have yielded mixed results to track and forecast the SARS-CoV-2 pandemic. To prepare for future outbreaks, the disease-modeling community can improve their modeling capabilities by learning from the methods and insights from another arena where accurate modeling is paramount: the weather and climate research field.
This paper argues that these improvements fall into four categories: model development, international comparisons, data exchange, and risk communication. A proper quantification of uncertainties in observations and models—including model assumptions, tail risks, and appropriate communication using probabilistic, Bayesian-based approaches—did not receive enough attention during the pandemic. Standardized testing and international comparison of model results is routine in climate mode-ling. No equivalent currently exists for pandemic models. Sharing of data is urgently needed. The homogenized real-time international data exchange, as organized by the World Meteorological Organization (WMO) since the 1960s, can serve as a role model for a global (privacy-preserving) data exchange by the World Health Organization. Lastly, researchers can look to climate change and high-impact weather forecasting to glean lessons about risk communication and the role of science in decision-making, in order to avoid common pitfalls and guide communication. Each of the four improvements is detailed here.