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Principles of Disease Surveillance

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...

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Updated: Aug 3, 2025

Enhanced Rabies Surveillance Using a Direct Rapid Immunohistochemical Test

Enhanced Rabies Surveillance Using a Direct Rapid Immunohistochemical Test

Published on: April 30, 2019

Defining County-Level Terrestrial Rabies Freedom Using the US National Rabies Surveillance System: Surveillance Data

Amber Kunkel^1,2, Gabriella Veytsel¹, Sarah Bonaparte¹

¹Division of High-Consequence Pathogens and Pathology, National Center for Emerging & Zoonotic Infectious Diseases, US Centers for Disease Control and Prevention, Atlanta, GA, United States.

JMIR Public Health and Surveillance

|April 7, 2023

Summary

This summary is machine-generated.

The National Rabies Surveillance System

Keywords:

animal disease spread infection spread model predict public health rabies surveillance zoonoses zoonosis

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Area of Science:

Veterinary Public Health
Zoonotic Disease Epidemiology
Wildlife Disease Surveillance

Background:

Rabies is a fatal zoonotic disease endemic in US wildlife reservoirs, posing risks to humans and domestic animals.
Accurate identification of rabies-free counties is crucial for public health decisions, including post-exposure prophylaxis.
Current surveillance methods struggle to differentiate between true absence of rabies and underreporting in counties.

Purpose of the Study:

To evaluate the historical definition of rabies-free counties used by the National Rabies Surveillance System (NRSS).
To explore improvements for defining rabies-free counties.
To develop a predictive model for estimating rabies freedom probability and reported cases at the county level.

Main Methods:

Analysis of NRSS data from 1995-2020, focusing on raccoon and skunk rabies virus transmission territories in the US.
Evaluation of the historical NRSS criteria for classifying counties as rabies-free.
Development of a zero-inflated negative binomial model to predict county-level rabies freedom and case counts.

Main Results:

The historical rabies-free definition demonstrated a high negative predictive value (99.2%) for both raccoon and skunk territories.
A small percentage of counties meeting historical criteria reported rabies cases the following year, some due to bat variants.
The developed model accurately predicted zero rabies cases and provided good estimates of reported cases.

Conclusions:

The historical NRSS definition is a reliable method for identifying counties free from terrestrial raccoon and skunk rabies.
A predictive model offers a way to quantify gradations of rabies risk at the county level.
Continued rabies testing capacity is essential, even in presumed rabies-free areas, due to potential animal translocations.