An agent-based model to simulate tsetse fly distribution and control techniques: A case study in Nguruman, Kenya

Abstract

Background African trypanosomiasis, also known as “sleeping sickness” in humans and “nagana” in livestock is an important vector-borne disease in Sub-Saharan Africa. Control of trypanosomiasis has focused on eliminating the vector, the tsetse fly (Glossina, spp.). Effective tsetse fly control planning requires models to predict tsetse population and distribution changes over time and space. Traditional planning models have used statistical tools to predict tsetse distributions and have been hindered by limited field survey data. Methodology/Results We developed an Agent-Based Model (ABM) to provide timing and location information for tsetse fly control without presence/absence training data. The model is driven by daily remotely-sensed environment data. The model provides a flexible tool linking environmental changes with individual biology to analyze tsetse control methods such as aerial insecticide spraying, wild animal control, releasing irradiated sterile tsetse males, and land use and cover modification. Significance This is a bottom-up process-based model with freely available data as inputs that can be easily transferred to a new area. The tsetse population simulation more closely approximates real conditions than those using traditional statistical models making it a useful tool in tsetse fly control planning.

Geostatistical models using remotely‐sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania

“…Numbers of G. pallidipes caught in traps have previously correlated inversely with Landsat shortwave infrared values-an indicator of moisture (Barsi, Lee, Kvaran, Markham, & Pedelty, 2014;Kitron et al, 1996). Normalized Difference Vegetation Index (NDVI) values above 0.39 have previously been used as an indicator of vegetation suitable for tsetse (Lin, DeVisser, & Messina, 2015;Moore & Messina, 2010), based on the observation that mortality rates decrease as NDVI increases (Rogers & Randolph, 1991). The NDVI is a measure of the density of plant matter-using the near-infrared and visible red wavelengths.…”

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 impact of vector migration on the effectiveness of strategies to control gambiense human African trypanosomiasis

“…Detailed information and data on tsetse density, movement ecology, and migration rates, for the area of interest, would be needed to correctly parameterize these models. Previous modeling studies have used spatially continuous and agent-based models to evaluate the effectiveness of different control measures for reducing or eradicating tsetse flies at different spatial scales [20,25,56–58]. Other studies, statistical models of species distribution, such as logistic regression and Maxent models, have been used to optimize the deployment of insecticide-treated targets, release density of sterile tsetse males, and location of monitoring traps for a tsetse eradication campaign in Senegal [59].…”

“…Future models should investigate the contribution of risk behavior to disease persistence in different g-HAT incidence intensity settings. Previous modeling studies have shown that the presence of non-human animal reservoirs would reduce the effectiveness of both medical intervention and vector control for interrupting disease transmission in high transmission intensity foci [14,15,20,25,56–58]. Therefore, tsetse migration and non-human animal reservoirs may have a synergistic impact in reducing the effectiveness of vector control measures.…”