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Activity Number:
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90
- Novel Statistical Methods for COVID Pandemic and Other Current Health Policy Issues
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Type:
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Contributed
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Date/Time:
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Monday, August 9, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Health Policy Statistics Section
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Abstract #318886
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Title:
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Analysis of the Yearly Transition Function in Measles Disease Modeling
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Author(s):
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Carlo Stefan Davila-Payan* and Andrew Hill and Xi Li and Michael Lynch and Sarah W. Pallas
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Companies:
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Centers for Disease Control and Prevention and Centers for Disease Control and Prevention and Centers for Disease Control and Prevention and Centers for Disease Control and Prevention and Centers for Disease Control and Prevention
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Keywords:
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infectious disease modeling;
measles incidence;
vaccination;
time series;
particle filtering
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Abstract:
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Globally, there were an estimated 9.8 million measles cases and 207,500 measles deaths in 2019. This study presents a novel approach to the use of a yearly transition function to account for the effects of measles vaccination timing for different age groups and disease seasonality on the yearly number of measles cases in a given country. Our methodology adds to and expands on the existing modeling framework of Eilertson et al. (Stat. Med. 2019; 38: 4146-4158) by developing explicit functional expressions for each underlying component of the transition function in order to adjust for the temporal interaction between vaccination and exposure to disease. Assumption of specific distributional forms provides multipliers that can be applied to estimated yearly counts of cases and vaccine doses to estimate impacts more precisely on population immunity. These new model features provide the ability to forecast and compare the effects of different vaccination timing scenarios on the expected disease incidence. Although this application is to measles, the method has potential relevance to modeling other vaccine-preventable diseases.
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Authors who are presenting talks have a * after their name.
