How decision makers can use quantitative approaches to guide outbreak responses.

Area of Science:

• Public Health
• Epidemiology
• Data Science

Background:

• Effective decision-making is crucial for managing public health emergencies.
• Decision makers synthesize diverse data for disease control strategies.
• Current challenges include data gaps and translating analysis into operational decisions.

Purpose of the Study:

• To highlight the role of data science and quantitative approaches in improving outbreak response.
• To outline analytic approaches for different response phases: investigation, scale-up, and control.
• To propose a structured framework for enhancing data analytics in disease outbreaks.

Main Methods:

• Review of analytic approaches for outbreak response phases.
• Discussion of quantitative methods for epidemiological descriptions and risk factor investigation.
• Identification of tools for assessing response needs and monitoring interventions.

Main Results:

• Quantitative methods can provide insightful epidemiological descriptions and identify causal agents.
• Tools exist to assess response needs, monitor interventions, and forecast for planning.
• Data science offers significant potential to improve outbreak response decision-making.

Conclusions:

• A structured approach is necessary to improve data quality and timeliness during outbreaks.
• Establishing dedicated analytic teams within response structures is recommended.
• A research agenda for data analytics in outbreak response needs definition to maximize benefits.