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Abstract:
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Bronchiolitis (an acute lower respiratory tract viral infection in infants) is the most common cause of infant hospitalizations in the United States. The only preventative intervention currently available is monthly injections of immunoprophylaxis. However, this treatment is expensive and needs to be administered simultaneously with seasonal bronchiolitis cycles in order to be effective. To increase our understanding of bronchiolitis timing, this research focuses on identifying seasonal bronchiolitis cycles (start times, peaks, and declinations) throughout the continental United States using data on infant bronchiolitis cases from the US Military Health System. We develop novel a statistical change point model that estimates spatially and temporally varying seasonal bronchiolitis cycles. Additionally, we introduce a novel historical matching approach to future seasonal cycle prediction.
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