Animal-borne diseases (e.g., Lyme, Malaria, etc.) are the #1 source of global infectious disease, and 60% of existing and 75% of emerging human infectious diseases have an animal origin. Each year, 2.5 billion people are infected by animal-borne disease and2.7M people die annually from animal-borne disease. For example: there are at least 18 known tickborne pathogens of zoonotic origin that cause over a dozen illnesses “Just one of those diseases, Lyme, infects over 500K people / year with U.S. costs of over $50B (DHHS 2018). While interventions exist for Lyme disease, it is a challenge to implement those interventions in a practical and targeted way that can be shown to actually reduce human infection risk. ZooHUB, a predictive analytic toolset using Bayesian spatial regression techniques, can be used to guide intervention efforts for vector-borne diseases. We are using historical records of tick abundance and infection rates from academic patterns. These data are used in combination with key environmental drives of tick ecology (temperature, precipitation, elevation, humidity, land coverage / forest density) to develop accurate predictions at a backyard spatial scale. These forecasts will alert and direct pest management specialists where to specifically focus countermeasure efforts. Additionally, through the use of an established anti-Lyme (OspA) wildlife vaccine, we are capable to greatly reducing the pathogen load in key animal and vector species. This targeted application of known anti-Lyme countermeasure can greatly reduce transmission risk to humans.
