Vector-borne Diseases: Data, Modeling, and Analysis

Tuesday, June 15 at 04:15am (PDT)
Tuesday, June 15 at 12:15pm (BST)
Tuesday, June 15 08:15pm (KST)

SMB2021 SMB2021 Follow Monday (Tuesday) during the "MS06" time block.
Note: this minisymposia has multiple sessions. The second session is MS13-MEPI (click here).

Share this


Jing Chen (Nova Southeastern University, United States), Shigui Ruan (University of Miami, United States), Xi Huo (University of Miami, United States)


Global change in the 21st century poses a significant threat to human health and vector-borne diseases will change in distribution and intensity as a result of global warming. Understanding the impacts of temperature, precipitation, vector spread, and human behavior remains extremely challenging and is essential in predicting future vector-borne disease outbreaks. Mathematical models are important tools to provide comprehensive explanations and quantitative simulations on natural phenomenon via analysis and data fitting methods. In this mini-symposium, we gather researchers with expertise in modeling vector-borne diseases to share their recent advances in either mathematical analysis or data fitting techniques.

Yijun Lou

(The Hong Kong Polytechnic University, China)
"Dynamics of a periodic tick-borne disease model with co-feeding and multiple patches"
This talk presents a mechanistic model for describing the spatial dynamics of tick-borne diseases by considering systemic transmission, seasonal climate impacts, host movement as well as the co-feeding transmission route. The net reproduction number for tick growth and basic reproduction number for disease transmission are derived, which predict the global dynamics of tick population growth and disease transmission. Further numerical simulations will also be presented in the talk to not only verify the analytical results, but also characterize the contribution of co-feeding transmission route on disease prevalence in a habitat and the effect of host movement on the spatial spreading of the pathogen.

Kaniz Fatema Nipa

(University of Georgia, United States)
"The Effect of Demographic and Environmental Variability on Disease Outbreak for a Dengue Model with a Seasonally Varying Vector Population"
Seasonal changes in temperature, humidity, and rainfall affect vector survival and emergence of mosquitoes and thus impact the dynamics of vector-borne disease outbreaks. Recent studies of deterministic and stochastic epidemic models with periodic environments have shown that the average basic reproduction number is not sufficient to predict an outbreak. We extend these studies to time-nonhomogeneous stochastic dengue models with demographic variability wherein the adult vectors emerge from the larval stage vary periodically. The combined effects of variability and periodicity provide a better understanding of the risk of dengue outbreaks. A multitype branching process approximation of the stochastic dengue model near the disease-free periodic solution is used to calculate the probability of a disease outbreak. The approximation follows from the solution of a system of differential equations derived from the backward Kolmogorov differential equation. This approximation shows that the risk of a disease outbreak is also periodic and depends on the particular time and the number of the initial infected individuals. Numerical examples are explored to demonstrate that the estimates of the probability of an outbreak from that of branching process approximations agree well with that of the continuous-time Markov chain. In addition, we propose a simple stochastic model to account for the effects of environmental variability on the emergence of adult vectors from the larval stage.

Necibe Tuncer

(Florida Atlantic University, United States)
"Determining Reliable Parameter Estimates for Within-host and Within-vector models of Zika Virus of Zika Epidemiological Models"
In this study, we introduce three within-host and one within-vector models of Zika virus. The within-host models are (i) the target cell limited model, (ii) the target cell limited model with natural killer cells class and (iii) a within-host-within-fetus model of a pregnant individual. The within-vector model includes the Zika virus dynamics in the midgut and the salivary glands. The within-host models are not structurally identifiable with respect to data on viral load and natural killer cell counts. After rescaling, the scaled within-host models are locally structurally identifiable. The within-vector model is structurally identifiable with respect to viremia data in the midgut and salivary glands. Using Monte Carlo Simulations we find that target cell limited model is practically identifiable from data on viremia; the target cell limited model with natural killer cell class is practically identifiable, except for the rescaled half saturation constant. The within-host-within-fetus model has all fetus related parameters not practically identifiable without data on the fetus, as well as the rescaled half saturation constant is also not practically identifiable. The remaining parameters are practically identifiable. Finally we find that none of the parameters of the within-vector model is practically identifiable.

Jianhong Wu

(York University, Canada)
"Multi-scale dynamic models for vector-borne disease transmission dynamics: infestation, co-feeding and systemic infection"
We present a series of collaborative studies on using structured models to understand the intertwined infestation (vector feeding on host), systemic and co-feeding transmission processes. We show how this multi-scale approach leads to a new class of nonlinearity, novel classes of dynamical systems, and interesting threshold dynamics including bistability and oscillation.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.