Document Type



Doctor of Philosophy (PhD)


Public Health

First Advisor's Name

Mary Jo Trepka

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Carina Blackmore

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Kristopher Fennie

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Gladys E. Ibañez

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Tan Li

Fifth Advisor's Committee Title

Committee Member

Sixth Advisor's Name

Weirui Wang

Sixth Advisor's Committee Title

Committee Member


Zika virus, arbovirus, real-time PCR, viral load, incubation period, testing algorithm

Date of Defense



Until recently, Zika virus (ZIKV) was an obscure virus that rarely caused infections and was unknown to most. In 2015 and 2016, ZIKV came into the public spotlight as Brazil and other countries began to report large increases in infections with ZIKV and reported potential complications with developing fetuses and neurologic manifestations. In 2016, the state of Florida identified and responded to an outbreak of locally acquired ZIKV infections in Miami-Dade County. This dramatic increase in infections demonstrated both its importance as an emerging infectious disease and the paucity of knowledge surrounding ZIKV. This study seeks to utilize the data collected during the ZIKV pandemic to further characterize the virus and examine the efficacy of current diagnostic algorithms.

First, a systematic review was conducted to pool data from the literature on existing cases of ZIKV infections. Markov chain Monte Carlo modeling was used to determine a median incubation time of 6.5 days for infections with ZIKV. Median time to viral RNA clearance varied significantly by specimen type. Vaginal specimens demonstrated the shortest time to viral RNA clearance (9.9 days); whereas blood specimens exhibited the longest (49.2 days).

Second, specimens from 934 symptomatic, non-congenitally acquired cases of ZIKV infection were analyzed to identify factors that contribute to the progression of viral load, as represented by the detection of ZIKV RNA. ZIKV RNA was detected most often in urine specimens and also was found to have higher viral loads than serum and whole blood specimens. Viral load was observed to be lower in non-pregnant women than pregnant women.

Last, an evaluation of the Centers for Disease Control and Prevention’s (CDC) 2017 and 2019 ZIKV testing algorithms was conducted using data from all confirmed and probable cases identified in Florida between 2016 and 2018 (n = 1,522). ZIKV RNA was detected most frequently in urine specimens. When testing required plaque reduction neutralization test (PRNT) to discern between ZIKV and dengue virus, the PRNT assay was only able to discriminate between viruses about half of the time. Reducing the specimen collection window in the 2019 CDC algorithm resulted in fewer conclusive results.






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