Shelters have unique challenges due to the incredible resource constraints, scale and scope of problems, and criticality of mission to which they are subjected. In many ways, animal shelters are akin to battlefield medics, in which they must use what they have to save everyone possible. Similarly to battlefield medics, their success is often determined by the time, information, and tools at their disposal. Unfortunately, many shelters do not have access to more than basic IT resources. Austin Pets Alive!, a local rescue which has, in partnership with Austin Animal Center, brought the live release rate from 55% when it was founded in 2008 to 98% in 2019 so far, has been championing the use of more advanced technical volunteers and their skills in order to provide shelters around the world with time saving methods, information, and tools. In this talk, three of the most advanced instantiations of this effort will be shared in areas of health economics, disease spread, and data analytics.

First, we will discuss the use of python, including tools such as Pandas, Numpy, and SciPy, and Data Science to examine the health economics of the treatment of the canine parvovirus, a deadly virus which infects puppies resulting in a 10% survival rate without treatment and 90% survival rate with treatment.

Second, we will discuss the use of python, including tools such as Tensorflow, NetworkX, and PyGame to examine the spread of another deadly, airborne disease, Canine Distemper, through shelters and how strategic choices about animal placement may stem the tide of the disease spread during an outbreak.

Finally, we will discuss the high-level problem of evaluating the success of shelters in saving animals and how data standardization (using Pandas), KPIs, and dashboarding (using Plotly) can be used to bring animal sheltering into the 21st century.