Intervention Time Series Analysis and Forecasting of Organ Donor Transplants in the US during the COVID-19 Era

Supraja Malladi and Qiqi Lu ()
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Supraja Malladi: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23220, USA
Qiqi Lu: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23220, USA

Forecasting, 2023, vol. 5, issue 1, 1-27

Abstract: The COVID-19 pandemic has had a catastrophic effect on the healthcare system including organ transplants worldwide. The number of living donor transplants performed in the US was affected more significantly by the pandemic with a 22.6% decrease in counts from 2019 to 2020 due to concerns of unnecessarily exposing potential living donors and living donor recipients to possible COVID-19 infection. This paper examines donor transplant counts obtained from the United Network for Organ Sharing from January 2002 to August 2021 using an intervention time series model with March 2020 as the intervention event. Specifically, donor transplant counts are analyzed across the different organs, donor types, and some major individual sociocultural factors, which are potential conditions contributing to disparities in achieving donor transplant equity such as age, ethnicity, and gender. In addition, the kidney allocation policy implemented in March 2021 is introduced as a second intervention event for kidney donor transplants. Overall, forecasts generated by our methods are more accurate than those using seasonal autoregressive integrated moving average models without interventions and seasonal naive methods. The intervention time series model provides a forecast accuracy comparable to the exponential smoothing method.

Keywords: time series; intervention; interrupted; forecast; organ donors; solid organ transplants (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2023
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