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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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Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study.

Onicio Leal-Neto¹, Thomas Egger², Matthias Schlegel²

¹Department of Economics, University of Zurich, Zurich, Switzerland.

JMIR Public Health and Surveillance

|November 2, 2021

Summary

Participatory surveillance using SMS reporting by healthcare workers (HCWs) effectively identified COVID-19 trends. Loss of taste was a key indicator, with machine learning models showing high accuracy in predicting infections.

Keywords:

COVID-19 SARS-CoV-2 digital epidemiology health care workers

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Area of Science:

Epidemiology
Public Health
Infectious Disease Surveillance

Background:

Traditional disease surveillance can be augmented by novel techniques for rapid analysis.
Web-based participatory surveillance offers a complementary approach to monitor infectious diseases.

Purpose of the Study:

To describe a web-based participatory surveillance strategy for healthcare workers (HCWs) during the COVID-19 pandemic's first wave.
To assess the utility of SMS-based symptom reporting for tracking COVID-19 activity.

Main Methods:

Prospective cohort of 1004 HCWs recruited in two Swiss hospitals.
Data analysis included LOESS regression, Spearman correlation, anomaly detection, and random forest classification.
SMS text messages were used for weekly symptom reporting.

Main Results:

127,684 SMS messages generated 90,414 valid reports.
Loss of taste showed the strongest correlation with positive PCR tests.
Random forest classification achieved 89% accuracy, with high specificity (80%) but low sensitivity (10.6%).

Conclusions:

Loss of taste effectively indicated COVID-19 activity at the population level.
Individual-level prediction models identified loss of taste, limb/muscle pain, and absence of runny nose/red eyes as key predictors of COVID-19.