Agent-based mathematical modeling as a tool for estimating Trypanosoma cruzi vector–host contact rates

Abstract

The parasite Trypanosoma cruzi, spread by triatomine vectors, affects over 100 mammalian species throughout the Americas, including humans, in whom it causes Chagas' disease. In the U.S., only a few autochthonous cases have been documented in humans, but prevalence is high in sylvatic hosts (primarily raccoons in the southeast and woodrats in Texas). The sylvatic transmission of T. cruzi is spread by the vector species Triatoma sanguisuga and Triatoma gerstaeckeri biting their preferred hosts and thus creating multiple interacting vector-host cycles. The goal of this study is to quantify the rate of contacts between different host and vector species native to Texas using an agent-based model framework. The contact rates, which represent bites, are required to estimate transmission coefficients, which can be applied to models of infection dynamics. In addition to quantitative estimates, results confirm host irritability (in conjunction with host density) and vector starvation thresholds and dispersal as determining factors for vector density as well as host-vector contact rates.

On Parameter Estimation Approaches for Predicting Disease Transmission Through Optimization, Deep Learning and Statistical Inference Methods

“…However, there are also methods in the literature that can help to estimate the parameters including S 0 , α and β in system (2) by minimizing the deviation between the SIR model out and a given data set. One such method is the Berkeley Madonna method (Macey, Oster, & Zahnley, 2000) which has been shown to fit the data using the fitting parameter as α which was estimated to be 0.4421 when N = 763 and β = 0.00218 (Yong et al, 2015). The dynamics corresponding to these parameters is illustrated in Figure 7.…”

“…Often some of the parameters such as the recovery rate maybe estimated from patterns in the data but transmission rates have to be computed using heuristic algorithms that are computationally or statistically motivated. Some of these computational algorithms include inverse methods, least-squares approach, agent-based modelling, using final size calculations (Martcheva, 2015;Murray, 1989;Pollicott, Wang, & Weiss, 2012;Yong, Mubayi, & Kribs, 2015). Also, researchers have employed a variety of statistical approaches including maximum-likelihood, Bayesian inference and Poisson regression methods (Capaldi et al, 2012;Hadeler, 2011;Huang, Liu, & Wu, 2006;Longini, Koopman, Haber, & Cotsonis, 1988;O'Dea, Pepin, Lopman, & Wilke, 2014).…”

“…We then introduce one of the most commonly used benchmark examples with real-world data and discuss how these formulas can be used along with estimated values of transmission and recovery rates to predict and visualize the observed data. We illustrate how the work of a variety of researchers (Martcheva, 2015;Murray, 1989;Yong et al, 2015) applied to the same real-world data end up predicting different transmission and recovery rates. Following this we also illustrate how one can predict these rates using an optimization algorithm that yields a better comparison between the real-world data and the computed data.…”

Effectiveness of fluralaner treatment regimens for the control of canine Chagas disease: A mathematical modeling study

“…Models investigating sylvatic transmission cycles have been developed to incorporate the multiple sylvatic hosts available, advancing our understanding of interactions between hosts, vectors, and T. cruzi and highlighting both vector-fecal and oral transmission pathways [42,51,52,53,54,55,56,57]. In peridomestic settings where dogs are the main hosts, host-targeted interventions may provide a valuable tool to reduce triatomine populations and canine exposure to T. cruzi.…”

Identification of Triatomines and Their Habitats in a Highly Developed Urban Environment

“…Domestic vectors are a leading public health concern, as species that have adapted to live in human dwellings pose a nightly risk for disease transmission. While individual exposures are not associated with a significant risk for transmission (Yong et al 2015), those living in triatomine-infested houses can have consistent exposure to fecal material from T. cruzi-positive vectors, thereby increasing their risk of acquiring disease.…”