An agent-based model of tsetse fly response to seasonal climatic drivers: Assessing the impact on sleeping sickness transmission rates

Abstract

BackgroundThis paper presents the development of an agent-based model (ABM) to incorporate climatic drivers which affect tsetse fly (G. m. morsitans) population dynamics, and ultimately disease transmission. The model was used to gain a greater understanding of how tsetse populations fluctuate seasonally, and investigate any response observed in Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) disease transmission, with a view to gaining a greater understanding of disease dynamics. Such an understanding is essential for the development of appropriate, well-targeted mitigation strategies in the future.MethodsThe ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The model incorporates climatic factors that affect pupal mortality, pupal development, birth rate, and death rate. In combination with fine scale demographic data such as ethnicity, age and gender for the human population in the region, as well as an animal census and a sample of daily routines, we create a detailed, plausible simulation model to explore tsetse population and disease transmission dynamics.ResultsThe seasonally-driven model suggests that the number of infections reported annually in the simulation is likely to be a reasonable representation of reality, taking into account the high levels of under-detection observed. Similar infection rates were observed in human (0.355 per 1000 person-years (SE = 0.013)), and cattle (0.281 per 1000 cattle-years (SE = 0.025)) populations, likely due to the sparsity of cattle close to the tsetse interface. The model suggests that immigrant tribes and school children are at greatest risk of infection, a result that derives from the bottom-up nature of the ABM and conditioning on multiple constraints. This result could not be inferred using alternative population-level modelling approaches.ConclusionsIn producing a model which models the tsetse population at a very fine resolution, we were able to analyse and evaluate specific elements of the output, such as pupal development and the progression of the teneral population, allowing the development of our understanding of the tsetse population as a whole. This is an important step in the production of a more accurate transmission model for rHAT which can, in turn, help us to gain a greater understanding of the transmission system as a whole.

Models for the rates of pupal development, fat consumption and mortality in tsetse (Glossinaspp)

“…These rates provide important inputs for an increasing number of models of tsetse population dynamics (Vale & Torr, 2005;Torr & Vale, 2011;Hargrove et al, 2012;Moore et al, 2012;Ackley & Hargrove, 2017;Lord et al, 2018), using techniques varying from spreadsheet models to differential equations. Recently, there has also been a growing interest in using agent-based modelswhere the lives of individual tsetse are followed at short discrete time intervals (Alderton et al, 2013(Alderton et al, , 2016(Alderton et al, , 2018Lin et al, 2015;Grébaut et al, 2016). All of the above modelling approaches can be useful for the prediction of future changes in the distribution and abundance of tsetse, and the diseases they transmit, under various intervention protocols, and in the face of possible climatic changes.…”

The weakest link: uncertainty and sensitivity analysis of extinction probability estimates for tsetse (Glossina spp) populations

“…The female tsetse fly generally mates only once in her lifetime, storing the sperm in spermathecae and using small amounts to fertilize her eggs one at a time [21, 22]. When sterile males are introduced into tsetse population, the probability ( ϵ ) that a female is inseminated by a fertile male falls below unity, by an amount that depends on the ratio of sterile to fertile males in the population.…”

Exploring the effect of human and animal population growth on vector-borne disease transmission with an agent-based model of Rhodesian human African trypanosomiasis in eastern province, Zambia

“…This paper describes the application of an ABM for rHAT/AAT to a real world question; the effect of population growth, land conversion and settlement on rHAT risk in Eastern Province Zambia. The ABM was constructed using data derived from detailed rHAT, AAT, and tsetse ecological surveys, undertaken in 2013, in Eastern Province, Zambia [ 38 , 39 ]. The ABM model is complex, being constructed at fine spatial and temporal resolutions and incorporating the representation of multiple mechanisms (e.g.…”

“…tsetse reproduction, tsetse feeding, human agent movements using real-world routines and pathfinding techniques [ 37 ]). A detailed description of the model framework, and the data used to construct it, are provided in [ 38 ], [ 39 ]. Therefore, only new data and modifications to the original model are described in detail here.…”

“…The ability to test “what-if?” scenarios that could be used to aid policy-making or management decisions [ 35 ], or increase understanding of a transmission system [ 36 ], is made possible with ABMs. This paper investigates the evolving epidemiological landscape as a result of potential changes to host density and distribution (by comparing to a plausible current ABM representation of rHAT transmission in the region), building on investigation of human movement [ 37 ], transmission in the absence of seasonality [ 38 ], and a seasonally dependent representation of the disease system [ 39 ]. These perturbations are in the form of a population growth scenario in the study site in Eastern Province, Zambia in which human and cattle populations have increased, and are increasing, on previously undeveloped land.…”