Researchers test mechanisms explaining differences in dengue serotype and disease severity by statistically fitting mathematical models to viral load data from dengue-infected individuals. They find a role for viral replication in explaining serotype-specific differences in viral load -- according to a new study published in PLOS Computational Biology.
Dengue is an important vector-borne virus that infects close to 400 million individuals annually. Though many risk factors of dengue are known, the mechanisms explaining why an individual will suffer severe symptoms are poorly understood. In efforts to combat this, clinical studies have been carried out and show that certain characteristics of viral load dynamics of dengue-infected individuals may be indicators of disease severity. In various empirical studies, researchers have uncovered possible mechanisms that may explain differences in disease severity between dengue-infected individuals.
In this study, Rotem Ben-Shachar, Scott Schmidler, and Katia Koelle at Duke University use statistical approaches to test if proposed mechanisms can explain variation in dengue viral load patterns.
They find statistical support for high viral infectivity rates of dengue serotypes 2 and 3 relative to dengue 1. In addition, they show that there is statistical support for antibodies contributing to disease severity during secondary dengue infections.
This research provides insight into how dengue viral load patterns can be useful for understanding severe dengue disease. Furthermore, since viral load been shown to be an important determinant of transmission, these finding have important implications for understanding differences in dengue transmission by strain and by disease severity.
In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://dx.plos.org/10.1371/journal.pcbi.1005194
Citation: Ben-Shachar R, Schmidler S, Koelle K (2016) Drivers of Inter-individual Variation in Dengue Viral Load Dynamics. PLoS Comput Biol 12(11): e1005194. doi:10.1371/journal.pcbi.1005194
Funding: RBS and KK received funding from MIDAS CIDID Center of Excellence (U54-GM111274). RBS received additional funding from NSF Mathbio RTG (NSF-DMS-0943760). SS was supported by NSF-DMS-1407622 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
PLoS Computational Biology