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A Novel Causal Risk-Based Decision-Making Methodology: The Case of Coronavirus.

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Area of Science:

  • Epidemiology
  • Public Health
  • Quantitative Risk Assessment

Background:

  • Societal catastrophes, including pandemics like COVID-19, necessitate urgent policy and decision-making.
  • Effective response requires evaluating interdependent parameters and potential scenarios.
  • Existing frameworks may lack the ability to dynamically identify causal relationships.

Purpose of the Study:

  • To propose a novel risk-based, decision-making methodology.
  • To unveil causal relationships between variables relevant to societal emergencies.
  • To assist policymakers in timely action during the COVID-19 pandemic using a quantitative framework.

Main Methods:

  • Development of a quantitative risk-based decision-making framework.
  • Application of the framework to county-level data from the United States.
  • Analysis of causal relationships, specifically examining weather variables' impact on daily coronavirus cases.

Main Results:

  • The methodology successfully identifies causal links between variables.
  • The framework provides a basis for scenario identification and consequence assessment.
  • Preliminary analysis suggests a potential causal impact of changing weather on COVID-19 case trends.

Conclusions:

  • The proposed methodology offers a powerful tool for navigating complex crises.
  • Understanding causal relationships, like weather's effect on disease spread, is crucial for effective public health policy.
  • This approach supports data-driven, timely interventions during emergencies.