Research Assistant, University of Colombo
Develop a mathematical model to understand dengue virus dynamics inside human body.
Dengue virus (DENV) has emerged as the most prevalent arthropod-borne disease in humans worldwide, with an estimated 390 million individuals infected per year, leading to approximately 500,000 hospitalizations and 25,000 deaths. Currently there are no vaccines or anti virals available against dengue. Early detection and clinical management is the only care for severe dengue. By identifying within-host dengue viral dynamics, we can identify which parameters poses a greater risk for severe dengue and identify patients who are likely to progress severe dengue at an early stage. It would also help in allocating more resources, time, care and attention to these patients, reduce unwanted hospitalization, reduce government expenditure on dengue and ultimately reduce the dengue death rate
Abstract: Within-host competition between strains of a vector-borne pathogen can affect strain frequencies in both the host and vector, thereby affecting viral population dynamics. However little is known about inter-strain competition in one of the most genetically diverse and epidemiologically important mosquito-borne RNA virus: dengue virus (DENV). To assess the strength and symmetry of intra-host competition among different strains of DENV, the effect of mixed infection of two DENV serotypes, DENV2 and DENV4, on the replication of each in cultured mosquito cells was tested. The number of infectious particles produced by each DENV strain in mixed infections was compared to that in single infections to determine whether replication of each strain was decreased in the presence of the other strain (i.e., competition). The two DENV strains were added to cells either simultaneously (coinfection) or with a 1 or 6-hour time lag between first and second serotype (superinfection).DENV2 and DENV4 showed significantly reduced replication in mixed infection relative to single infection treatments. In superinfection treatments, replication was suppressed to a greater extent when the interval between addition of each strain was longer, and when a strain was added second. Additionally, competitive effects were asymmetric: although both strains replicated to similar peak population sizes in single infections, DENV2 was more suppressed than DENV4 in mixed infections. Superinfection treatments yielded significantly lower combined virus titers than coinfection or single infection treatments.Competition between DENV strains in cultured mosquito cells can cause a significant decrease in peak viral population sizes, which could translate to decreased transmission by the vector. Effects of competition were asymmetric between DENV2 and DENV4, probably reflecting significant variation in the competitive ability of DENV strains in nature. Competition was strongest in superinfection treatments, suggesting that colonization of new DENV strains could be impeded in areas where numerous mosquitoes are infected with endemic DENV strains.
Pub.: 12 Feb '08, Pinned: 27 Sep '17
Abstract: Dengue, the most common mosquito-borne viral infection of humans, is endemic across much of the world, including much of tropical Asia and is increasing in its geographical range. Here, we present a mathematical model of dengue virus dynamics within infected individuals, detailing the interaction between virus and a simple immune response. We fit this model to measurements of plasma viral titre from cases of primary and secondary DENV 1 infection in Vietnam. We show that variation in model parameters governing the immune response is sufficient to create the observed variation in virus dynamics between individuals. Estimating model parameter values, we find parameter differences between primary and secondary cases consistent with the theory of antibody-dependent enhancement (namely enhanced rates of viral entry to target cells in secondary cases). Finally, we use our model to examine the potential impact of an antiviral drug on the within-host dynamics of dengue. We conclude that the impact of antiviral therapy on virus dynamics is likely to be limited if therapy is only started at the onset of symptoms, owing to the typically late stage of viral pathogenesis reached by the time symptoms are manifested and thus treatment is started.
Pub.: 16 May '14, Pinned: 27 Sep '17
Abstract: In recent years, the within-host viral dynamics of dengue infections have been increasingly characterized, and the relationship between aspects of these dynamics and the manifestation of severe disease has been increasingly probed. Despite this progress, there are few mathematical models of within-host dengue dynamics, and the ones that exist focus primarily on the general role of immune cells in the clearance of infected cells, while neglecting other components of the immune response in limiting viraemia. Here, by considering a suite of mathematical within-host dengue models of increasing complexity, we aim to isolate the critical components of the innate and the adaptive immune response that suffice in the reproduction of several well-characterized features of primary and secondary dengue infections. By building up from a simple target cell limited model, we show that only the innate immune response is needed to recover the characteristic features of a primary symptomatic dengue infection, while a higher rate of viral infectivity (indicative of antibody-dependent enhancement) and infected cell clearance by T cells are further needed to recover the characteristic features of a secondary dengue infection. We show that these minimal models can reproduce the increased risk of disease associated with secondary heterologous infections that arises as a result of a cytokine storm, and, further, that they are consistent with virological indicators that predict the onset of severe disease, such as the magnitude of peak viraemia, time to peak viral load, and viral clearance rate. Finally, we show that the effectiveness of these virological indicators to predict the onset of severe disease depends on the contribution of T cells in fuelling the cytokine storm.
Pub.: 19 Dec '14, Pinned: 27 Sep '17
Abstract: Dengue is a vector-borne viral disease of humans that endemically circulates in many tropical and subtropical regions worldwide. Infection with dengue can result in a range of disease outcomes. A considerable amount of research has sought to improve our understanding of this variation in disease outcomes and to identify predictors of severe disease. Contributing to this research, patterns of viral load in dengue infected patients have been quantified, with analyses indicating that peak viral load levels, rates of viral load decline, and time to peak viremia are useful predictors of severe disease. Here, we take a complementary approach to understanding patterns of clinical manifestation and inter-individual variation in viral load dynamics. Specifically, we statistically fit mathematical within-host models of dengue to individual-level viral load data to test virological and immunological hypotheses explaining inter-individual variation in dengue viral load. We choose between alternative models using model selection criteria to determine which hypotheses are best supported by the data. We first show that the cellular immune response plays an important role in regulating viral load in secondary dengue infections. We then provide statistical support for the process of antibody-dependent enhancement (but not original antigenic sin) in the development of severe disease in secondary dengue infections. Finally, we show statistical support for serotype-specific differences in viral infectivity rates, with infectivity rates of dengue serotypes 2 and 3 exceeding those of serotype 1. These results contribute to our understanding of dengue viral load patterns and their relationship to the development of severe dengue disease. They further have implications for understanding how dengue transmissibility may depend on the immune status of infected individuals and the identity of the infecting serotype.
Pub.: 18 Nov '16, Pinned: 27 Sep '17
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