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Opportunities and challenges in modeling emerging infectious diseases

C Jessica E Metcalf1,2, Justin Lessler3

  • 1Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. cmetcalf@princeton.edu.

Science (New York, N.Y.)
|July 15, 2017
PubMed

Insights

Mathematical models help forecast infectious disease spread and control. Despite data limitations, innovations in data and methods enhance their power for public health responses to emerging pathogens.

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • Pathogen emergence includes novel viruses, spread to new populations, and drug resistance.
  • Mathematical models are crucial for predicting disease cases, understanding transmission, and assessing control strategies.
  • Data scarcity presents significant limitations for the effective use of these models.

Purpose of the Study:

  • To highlight the capabilities and limitations of mathematical models in the context of pathogen emergence.
  • To emphasize the growing potential of these models due to data and methodological advancements.
  • To advocate for better integration of models with public health practices for improved response.

Main Methods:

  • Review of the current state and limitations of mathematical modeling for emergent pathogens.
  • Discussion of how increased data availability (genetics, ecology) and computational innovations enhance model utility.
  • Exploration of the need for integrating infectious disease models into public health practice.

Main Results:

  • Mathematical models offer valuable tools for forecasting and evaluating control of emergent pathogens.
  • Data scarcity remains a key challenge, but is being addressed by new data sources and methods.
  • Innovations are increasing the power of models to inform public health responses.

Conclusions:

  • Integrating infectious disease models more closely with public health practice is essential.
  • Developing readily available resources for model application can improve response timeliness and quality.
  • Enhanced modeling capabilities hold significant promise for managing future pathogen emergence events.

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