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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Principles of Disease Surveillance01:26

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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: Dec 7, 2025

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Leveraging Computational Modeling to Understand Infectious Diseases.

Adrianne L Jenner1,2, Rosemary A Aogo3, Courtney L Davis4

  • 1Department of Mathematics and Statistics, Pavillon André-Aisenstadt, Université de Montréal, Montréal, QC H3C 3J7 Canada.

Current Pathobiology Reports
|September 29, 2020
PubMed
Summary

Computational modeling advances infectious disease research by integrating biological mechanisms with mathematical and machine learning techniques. This interdisciplinary approach drives discoveries and improves treatments for diseases like malaria, HIV, and COVID-19.

Keywords:
BacteriaComputational modelingInfectious diseasesMathematicsParasitesViruses

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Area of Science:

  • Infectious disease dynamics
  • Computational biology
  • Mathematical modeling

Background:

  • Understanding in-host infectious disease dynamics is crucial for predicting effective treatments.
  • Biological mechanisms including etiology, pathogenesis, and cellular interactions are key to infectious diseases.

Purpose of the Study:

  • To review recent findings in computational and mathematical modeling of infectious diseases.
  • To highlight advances in modeling techniques and their impact on disease discovery and clinical translation.

Main Methods:

  • Review of recent literature on computational and mathematical modeling in infectious disease research.
  • Integration of mechanistic models with machine learning algorithms.
  • Analysis of within-host, between-host, and large-scale transmission models.

Main Results:

  • Combined modeling approaches have improved treatments for Shigella, tuberculosis, malaria, HIV, influenza, and SARS-CoV-2.
  • Modeling has led to novel compound development, effective vaccination and antimalarial therapies, and new HIV treatment modalities.
  • Large-scale SARS-CoV-2 models informed policy on travel restrictions and contact tracing.

Conclusions:

  • Computational modeling is central to infectious disease research, accelerated by the COVID-19 pandemic.
  • Interdisciplinary collaboration between medical/biological scientists and computer/mathematical scientists enhances understanding of infectious diseases.