Essex County Applies Geospatial AI to Improve Public Health Outcomes

A New Jersey public health department uses predictive modeling to proactively manage influenza and rodent-related risks.

Essex County, New Jersey, is setting a new standard for modern public health operations. By embedding geospatial artificial intelligence into its daily work, the Essex County Office of Public Health Management (ECOPHM) is equipping decision-makers with forward-looking insight to better protect residents from disease and environmental health threats.

Geospatial AI is the fusion of two powerful technologies: artificial intelligence (AI) and geographic information system (GIS) technology. In short, it’s AI that’s applied to maps, geospatial data, science, and technology to enhance spatial analysis for faster, better decisions. It helps public health teams not only see where problems are likely to happen but also act before they do.

“We understood the importance of having a proactive public health program before the COVID-19 pandemic hit and were in the process of reinvigorating our office,” said Joseph N. DiVincenzo Jr., Essex County executive. DiVincenzo explained that “artificial intelligence is another progressive tool we can use to promote wellness and develop defenses against health threats.”.

Established in 2020, ECOPHM is among the first public health departments in the United States to fully integrate geospatial AI into its core functions. From the outset, the department built an enterprise GIS program using ArcGIS Enterprise—a comprehensive GIS platform that enables users to manage, map, visualize, and analyze their data—that works alongside other health information systems to monitor risks, guide interventions, and advance community well-being.

Located in the second-most populous county in New Jersey—home to diverse urban centers like Newark and East Orange, along with large suburbs connected by commuter rail lines—ECOPHM is uniquely positioned to showcase location intelligence in dense, highly mobile populations.

“From day one, we’ve seen spatial data and analysis not just as useful tools, but as essential components of our public health intelligence,” said Maya Harlow, founding director and county health officer. “Geospatial AI is the future of public health. We are now equipped with tools that [help us respond] with a plan. We act earlier, target more effectively, and ensure equitable access to services.”

Initial GIS projects for the county included its Community Right to Know Facility Mapping dashboard and the Community Health Assessment dashboard. Both dashboards were powered by data collected through ArcGIS Survey123, a formcentric mobile and web app that allows mobile staff to easily capture structured, location-based data. 

Each flu season brings the risk of widespread illness and added pressure on health-care systems. To stay ahead of potential outbreaks, ECOPHM turned to predictive modeling. The goal was to identify where and when flu cases would likely rise—and which age groups would be most affected—so that the department could deploy outreach and vaccination campaigns before transmission intensified.

Working with Esri partner CyberTech, the county analyzed 10 years of historical flu case data, applying a suite of models to identify geographic and demographic patterns of flu incidence.

Spatial analysis models revealed three types of flu hot spots:

• Sporadic hot spots: areas where flu activity appeared inconsistently over time

• Oscillating hot spots: zones with fluctuating levels of flu activity

• Persistent hot spots: neighborhoods with consistently high flu incidence over consecutive months

Persistent hot spots were of particular concern, as they were primarily located near regional rail stations. This suggested that being in proximity to transit corridors—regardless of individual commuter behavior alone—may increase the risk of disease transmission.

With these insights, ECOPHM launched geographically targeted communications and flu vaccination clinics in high-risk areas. This led to a more efficient and equitable use of resources—helping prevent illness where it was most likely to occur.

In addition to tracking and responding to infectious disease, ECOPHM applied geospatial AI to a different kind of health threat: rodent activity. Rodents can carry dangerous pathogens, such as bubonic plague and hantavirus, as well as parasites. They can also destroy infrastructures ranging from homes to commercial businesses. The Township of Bloomfield, a community within Essex County, had seen periodic surges in rodent sightings and wanted to better understand the environmental factors involved.

“Public health challenges cross borders, so our solutions must too,” said Bloomfield Mayor Jenny Mundell. “By partnering with Essex County and using geospatial AI, we’ve improved how we respond to rodent activity—targeting resources where they’re needed most. It’s a smart, collaborative approach that strengthens our communities and ensures residents benefit from data-informed strategies.”

Rodent activity appeared to increase with rising temperatures and specific humidity levels. To explore this connection, ECOPHM and CyberTech applied density-based clustering and hot spot analysis to data from January 2021 through April 2024. Their analysis revealed both sporadic and persistent problem areas across space and time.

Equipped with these findings, the county was able to anticipate surges in rodent activity during warmer, more humid periods and focus sanitation and mitigation efforts in the neighborhoods most at risk. 

Essex County’s early investment in spatial thinking and geospatial AI is delivering more resilient, responsive, and equitable public health services. Whether forecasting flu outbreaks or targeting rodent control, the county is demonstrating how modern tools can anticipate risk and enable smarter, faster action. Essex County’s approach offers a model for other jurisdictions looking to modernize public health with location intelligence.