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COVID-19 Data Models: A Guide for Public Sector Officials

Updated: May 27, 2020

Written by Kevin Soo, PhD, a Senior Applied Data Scientist at Civis Analytics, where he helps government teams solve tough data-related problems.


Data science and predictive modeling are increasingly important in modern-day governments, and this is especially true as teams focus on responding to COVID-19.


Addressing this public health crisis requires collaboration among team members with varying levels of technical expertise. Because most government data science teams are small, the individuals who understand it become the go-to for questions about modeling, data and statistics. This data science knowledge gap can be tricky to navigate: experts may spend time explaining the nuances of technical concepts to colleagues who may not know the right questions to ask.


To help bridge this gap, Civis identified some important considerations for teams thinking about data and models related to COVID-19. Our hope is that this serves as a good high-level starting point for beginners, and can be a resource for data scientists to share with colleagues.


First of all: what is a model and why are there so many of them?


At the most general level: statistical models take data about what has already happened, and use them to make projections about what will happen using math. In the case of COVID-19, there are a number of models, and they may differ for several reasons.


First, they may be predicting different outcomes. For example, the IHME model projects the number of COVID-19-related deaths, while the CHIME model makes projections about hospital capacity. Both are important predictions that can help us navigate the crisis, but we can’t compare them apples-to-apples, so it’s important to know what models are being used by your state or city government.