Related Experiment Video
Updated: Nov 14, 2025

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
Published on: April 6, 2021
Surveillance system for acute severe infections with epidemic potential based on a deterministic-stochastic model,
Jorge A Castañón-González1, Carlos Polanco-González2, Ricardo González-González3
1Health Sciences Research Center, Faculty of Health Sciences, Universidad Anahuac, Huixquilucan, Estado de Mexico; Department of Critical Care Medicine, Hospital Juarez de Mexico, Mexico City; Mexico.
Background:
The dynamic interactions of severe infectious diseases with epidemic potential and their hosts are complex. Therefore, it remains uncertain if a sporadic zoonosis restricted to a certain area will become a global pandemic or something in between.
Objective:
The objective of the study was to present a surveillance system for acute severe infections with epidemic potential based on a deterministic-stochastic model, the StochCum Method.
Design:
The StochCum Method is founded on clinical, administrative, and sociodemographic variables that provide a space/time map as a preventive warning of possible outbreaks of severe infections that can be complemented based on the sum of all the first accumulated cases. If the outbreak is happening in high-risk areas, an early warning can be elicited to activate the health response system and save time while waiting for the confirmation of symptomatic cases.
Results:
The surveillance system was tested virtually for 1 month on admissions to the emergency room of a public hospital located in Mexico City, Mexico. It promptly identified simulated cases of acute respiratory infections with epidemic potential.
Conclusions:
The StochCum Method proved to be a practical and useful system for conducting epidemic surveillance on a hospital network.
More Related Videos
Related Concept Videos
Steps in Outbreak Investigation
Principles of Disease Surveillance
Exponential Equations for Modeling Growth
Statistical Methods for Analyzing Epidemiological Data
Mechanistic Models: Compartment Models in Individual and Population Analysis
Statistical Software for Data Analysis and Clinical Trials

