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

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An agent-based model of tsetse fly response to seasonal climatic drivers : Assessing the impact on sleeping sickness transmission rates. / Alderton, Simon; Macleod, Ewan T.; Anderson, Neil E.; Palmer, Gwen; Machila, Noreen; Simuunza, Martin; Welburn, Susan C.; Atkinson, Peter M.

In: PLoS Neglected Tropical Diseases, Vol. 12, No. 2, e0006188, 09.02.2018.

Research output: Contribution to journalJournal article

Harvard

Alderton, S, Macleod, ET, Anderson, NE, Palmer, G, Machila, N, Simuunza, M, Welburn, SC & Atkinson, PM 2018, 'An agent-based model of tsetse fly response to seasonal climatic drivers: Assessing the impact on sleeping sickness transmission rates', PLoS Neglected Tropical Diseases, vol. 12, no. 2, e0006188. https://doi.org/10.1371/journal.pntd.0006188

APA

Alderton, S., Macleod, E. T., Anderson, N. E., Palmer, G., Machila, N., Simuunza, M., Welburn, S. C., & Atkinson, P. M. (2018). An agent-based model of tsetse fly response to seasonal climatic drivers: Assessing the impact on sleeping sickness transmission rates. PLoS Neglected Tropical Diseases, 12(2), [e0006188]. https://doi.org/10.1371/journal.pntd.0006188

Vancouver

Alderton S, Macleod ET, Anderson NE, Palmer G, Machila N, Simuunza M et al. An agent-based model of tsetse fly response to seasonal climatic drivers: Assessing the impact on sleeping sickness transmission rates. PLoS Neglected Tropical Diseases. 2018 Feb 9;12(2). e0006188. https://doi.org/10.1371/journal.pntd.0006188

Author

Alderton, Simon ; Macleod, Ewan T. ; Anderson, Neil E. ; Palmer, Gwen ; Machila, Noreen ; Simuunza, Martin ; Welburn, Susan C. ; Atkinson, Peter M. / An agent-based model of tsetse fly response to seasonal climatic drivers : Assessing the impact on sleeping sickness transmission rates. In: PLoS Neglected Tropical Diseases. 2018 ; Vol. 12, No. 2.

Bibtex

@article{2483db350cf24b16a4d01efd45519ec2,
title = "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.",
author = "Simon Alderton and Macleod, {Ewan T.} and Anderson, {Neil E.} and Gwen Palmer and Noreen Machila and Martin Simuunza and Welburn, {Susan C.} and Atkinson, {Peter M.}",
year = "2018",
month = feb
day = "9",
doi = "10.1371/journal.pntd.0006188",
language = "English",
volume = "12",
journal = "PLoS Neglected Tropical Diseases",
issn = "1935-2727",
publisher = "Public Library of Science",
number = "2",

}

RIS

TY - JOUR

T1 - An agent-based model of tsetse fly response to seasonal climatic drivers

T2 - Assessing the impact on sleeping sickness transmission rates

AU - Alderton, Simon

AU - Macleod, Ewan T.

AU - Anderson, Neil E.

AU - Palmer, Gwen

AU - Machila, Noreen

AU - Simuunza, Martin

AU - Welburn, Susan C.

AU - Atkinson, Peter M.

PY - 2018/2/9

Y1 - 2018/2/9

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pntd.0006188

DO - 10.1371/journal.pntd.0006188

M3 - Journal article

VL - 12

JO - PLoS Neglected Tropical Diseases

JF - PLoS Neglected Tropical Diseases

SN - 1935-2727

IS - 2

M1 - e0006188

ER -