https://doi.org/10.1140/epjs/s11734-023-00801-z
Regular Article
Modeling and forecasting age-specific drug overdose mortality in the United States
1
Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, 60322, Frankfurt am Main, Germany
2
Department of Computational Medicine, University of California, Los Angeles, 90095, Los Angeles, CA, USA
3
Department of Mathematics, University of California, Los Angeles, 90095, Los Angeles, CA, USA
4
Department of Mathematics, California State University at Northridge, 91330, Los Angeles, CA, USA
Received:
30
September
2022
Accepted:
24
February
2023
Published online:
26
April
2023
Drug overdose deaths continue to increase in the United States for all major drug categories. Over the past two decades the total number of overdose fatalities has increased more than fivefold; since 2013 the surge in overdose rates is primarily driven by fentanyl and methamphetamines. Different drug categories and factors such as age, gender, and ethnicity are associated with different overdose mortality characteristics that may also change in time. For example, the average age at death from a drug overdose has decreased from 1940 to 1990 while the overall mortality rate has steadily increased. To provide insight into the population-level dynamics of drug overdose mortality, we develop an age-structured model for drug addiction. Using an augmented ensemble Kalman filter (EnKF), we show through a simple example how our model can be combined with synthetic observation data to estimate mortality rate and an age-distribution parameter. Finally, we use an EnKF to combine our model with observation data on overdose fatalities in the United States from 1999 to 2020 to forecast the evolution of overdose trends and estimate model parameters.
© The Author(s) 2023
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