The effect of antibody-dependent enhancement, cross immunity, and vector population on the dynamics of dengue fever

Hu, Kongxin; Thoens, Christian; Bianco, Simone; Edlund, Stefan; Davis, Mark M.; Douglas, Judith V.; Kaufman, James H.

doi:10.1016/j.jtbi.2012.11.021

Cited by 27 publications

(21 citation statements)

References 42 publications

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“…concentrations (Katzelnick et al, 2017). Previous studies using in vitro monocytes that had received DENV pretreatment with DF/ DHF serum also indicated that preexisting antibodies promoted heterotypic DENV infection via ADE and that ADE infection correlated with the severity of dengue disease (Flipse et al, 2016;Hu et al, 2013;Katzelnick et al, 2017;Puerta-Guardo et al, 2013). We also noted that low diluted convalescent plasma from DF and DHF patients showed inhibitory effects on DENV-2 infection ( Figure 5; Supplementary Material Figure S1), but the reasons for this remain unclear.…”

Section: Discussionsupporting

confidence: 57%

A clinical and epidemiological survey of the largest dengue outbreak in Southern Taiwan in 2015

Wang

Lin

Chang

et al. 2019

International Journal of Infectious Diseases

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This study examined the epidemiological, clinical, and immunological characteristics of the 2015 dengue outbreak in Taiwan. Methods: Clinical data were collected from dengue fever (DF) and dengue hemorrhagic fever (DHF) patients. A phylogenetic tree was used to analyze the source of the outbreak strain. Paired plasma samples from DF/DHF patients were used for antibody-dependent enhancement (ADE) assay and cytokine multiplex biometric immunoassay to validate the immunological mechanism. Results: This outbreak mainly occurred in two of the southern cities of Taiwan: Tainan (n = 22 777; 52%) and Kaohsiung (n = 19 784; 45%). A high DHF death rate was noted (34.6%). The case (DHF) and control (DF) study indicated that older age (>60 years), type II diabetes, and hypertension were risk factors correlated with the development of DHF (p < 0.0001). The phylogenetic tree results suggested that the outbreak-associated strain was dengue virus serotype 2 and cosmopolitan genotype, forming a stable cluster with the isolates from Thailand and Indonesia (bootstrap value of 99%). Cytokine analyses demonstrated that levels of interleukin (IL)-6, IL-4, IL-13, IL-1β, interferon gamma (IFN-g), and granulocyte-macrophage colony-stimulating factor (GM-CSF) were significantly higher in DHF patients compared to DF patients (p < 0.001). The ADE assay showed that diluted plasma containing preexisting dengue antibodies from DHF patients significantly enhanced dengue infection (p < 0.05). Conclusion: The results suggest that older age, type II diabetes, hypertension, immunological cytokine dysregulation, and preexisting dengue antibodies inducing ADE infection are correlated with dengue severity. This study also indicates that the largest dengue outbreak in Taiwan might have been a result of imported DF from dengue epidemic regions.

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Section: Discussionsupporting

confidence: 57%

A clinical and epidemiological survey of the largest dengue outbreak in Southern Taiwan in 2015

Wang

Lin

Chang

et al. 2019

International Journal of Infectious Diseases

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“…In addition to weather, the impact of the dynamics of circulation of dengue virus serotypes on dengue epidemiology has been well documented [28]. Infection with one serotype confers life-long immunity to that particular serotype [29], [30]. Some studies have also reported a time-lagged correlation between dengue virus serotype dynamics and disease incidence rates [31].…”

Section: Introductionmentioning

confidence: 99%

Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore

Lee

et al. 2014

PLoS Negl Trop Dis

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Weather factors are widely studied for their effects on indicating dengue incidence trends. However, these studies have been limited due to the complex epidemiology of dengue, which involves dynamic interplay of multiple factors such as herd immunity within a population, distinct serotypes of the virus, environmental factors and intervention programs. In this study, we investigate the impact of weather factors on dengue in Singapore, considering the disease epidemiology and profile of virus serotypes. A Poisson regression combined with Distributed Lag Non-linear Model (DLNM) was used to evaluate and compare the impact of weekly Absolute Humidity (AH) and other weather factors (mean temperature, minimum temperature, maximum temperature, rainfall, relative humidity and wind speed) on dengue incidence from 2001 to 2009. The same analysis was also performed on three sub-periods, defined by predominant circulating serotypes. The performance of DLNM regression models were then evaluated through the Akaike's Information Criterion. From the correlation and DLNM regression modeling analyses of the studied period, AH was found to be a better predictor for modeling dengue incidence than the other unique weather variables. Whilst mean temperature (MeanT) also showed significant correlation with dengue incidence, the relationship between AH or MeanT and dengue incidence, however, varied in the three sub-periods. Our results showed that AH had a more stable impact on dengue incidence than temperature when virological factors were taken into consideration. AH appeared to be the most consistent factor in modeling dengue incidence in Singapore. Considering the changes in dominant serotypes, the improvements in vector control programs and the inconsistent weather patterns observed in the sub-periods, the impact of weather on dengue is modulated by these other factors. Future studies on the impact of climate change on dengue need to take all the other contributing factors into consideration in order to make meaningful public policy recommendations.

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“…Notably, there are also some cases where the size of the second outbreak is smaller than the first one. For example, the eight Dengue outbreaks in Thailand over 30 years from 1973 to 1999 [60] , and there are also some cases that the second outbreak is larger than the previous one, as in the case of SARS in 2003 [24] and dengue in Taiwan in 20 01-20 02 [59] . In order to better understand the underlying mechanism that drives the multi-outbreak phenomenon of the coupled dynamics, we set two thresholds, I high and I low , to represent different infected levels.…”

Section: Model Analysismentioning

confidence: 99%

Coupling dynamics of epidemic spreading and information diffusion on complex networks

Zhan

Liu

Zhou

et al. 2018

Applied Mathematics and Computation

146

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a b s t r a c tThe interaction between disease and disease information on complex networks has facilitated an interdisciplinary research area. When a disease begins to spread in the population, the corresponding information would also be transmitted among individuals, which in turn influence the spreading pattern of the disease. In this paper, firstly, we analyze the propagation of two representative diseases ( H7N9 and Dengue fever ) in the real-world population and their corresponding information on Internet, suggesting the high correlation of the two-type dynamical processes. Secondly, inspired by empirical analyses, we propose a nonlinear model to further interpret the coupling effect based on the SIS (Susceptible-Infected-Susceptible) model. Both simulation results and theoretical analysis show that a high prevalence of epidemic will lead to a slow information decay, consequently resulting in a high infected level, which shall in turn prevent the epidemic spreading. Finally, further theoretical analysis demonstrates that a multi-outbreak phenomenon emerges via the effect of coupling dynamics, which finds good agreement with empirical results. This work may shed light on the in-depth understanding of the interplay between the dynamics of epidemic spreading and information diffusion.

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The effect of antibody-dependent enhancement, cross immunity, and vector population on the dynamics of dengue fever

Cited by 27 publications

References 42 publications

A clinical and epidemiological survey of the largest dengue outbreak in Southern Taiwan in 2015

A clinical and epidemiological survey of the largest dengue outbreak in Southern Taiwan in 2015

Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore

Coupling dynamics of epidemic spreading and information diffusion on complex networks

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