Tools for managing complex prediction modeling
The data that models rely upon may be imperfect due a range of factors, including a lack of widespread testing or inconsistent reporting. Thats why COVID-19 models need to account for uncertainty in order for their predictions to be reliable and useful. To help address this challenge, were providing researchers examples of how to implement bespoke epidemiological models using TensorFlow Probability (TFP), a library for building probabilistic models that can measure confidence in their own predictions. With TFP, researchers can use a range of data sources with different granularities, properties, or confidence levels, and factor that uncertainty into the overall prediction models. This could be particularly useful in fine-tuning the increasingly complex models that epidemiologists are using to understand the spread of COVID-19, particularly in gaining city or county-level insights when only state or national-level datasets exist.
While models can help predict what happens next, researchers and policymakers are also turning to simulations to better understand the potential impact of their interventions. Simulating these “what if” scenarios involve calculating highly variable social interactions at a massive scale. Simulators can help trial different social distancing techniques and gauge how changes to the movement of people may impact the spread of disease.
Google researchers have developed an open-source agent-based simulator that utilizes real-world data to simulate populations to help public health organizations fine tune their exposure notification parameters. For example, the simulator can consider different disease and transmission characteristics, the number of places people visit, as well as the time spent in those locations. We also contributed to Oxfords agent-based simulator by factoring in real world mobility and representative models of interactions within different workplace sectors to understand the effect of an exposure notification app on the COVID-19 pandemic.
The scientific and developer community are working on important work to understand and manage the pandemic. Whether its by contributing to open source initiatives or funding data science projects and providing Google.org Fellows, were committed to collaborating with researchers on efforts to build a more equitable and resilient future.read more
Making data useful for public health
