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

PLoS Negl Trop Dis. 2018 Feb 9;12(2):e0006188. doi: 10.1371/journal.pntd.0006188. eCollection 2018 Feb.

Abstract

Background: This 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.

Methods: The 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.

Results: The 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.

Conclusions: In 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.

MeSH terms

  • Adolescent
  • Adult
  • Age Factors
  • Animals
  • Birth Rate
  • Cattle
  • Child
  • Child, Preschool
  • Climate*
  • Ethnicity
  • Female
  • Geographic Mapping
  • Humans
  • Incidence
  • Infant
  • Male
  • Middle Aged
  • Mortality
  • Population Dynamics
  • Rain
  • Schools
  • Seasons*
  • Sex Factors
  • Temperature
  • Trypanosoma brucei rhodesiense / pathogenicity*
  • Trypanosomiasis, African / epidemiology*
  • Trypanosomiasis, African / mortality
  • Trypanosomiasis, African / transmission*
  • Tsetse Flies / physiology*
  • Young Adult
  • Zambia

Grants and funding

SA is supported by an EPSRC Doctoral Training Centre grant (EP/G03690X/1). PMA and SCW are supported by, and the fieldwork in this investigation was carried out for, the Dynamic Drivers of Disease in Africa Consortium, NERC project no. NE/J000701/1, part of the Ecosystem Services for Poverty Alleviation (ESPA) programme. The ESPA programme is funded by the Department for International Development (DFID), the Economic and Social Research Council (ESRC) and the Natural Environment Research Council (NERC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.