Statistical methods for linking geostatistical maps and transmission models: Application to lymphatic filariasis in East Africa

https://doi.org/10.1016/j.sste.2020.100391Get rights and content
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Highlights

  • Novel methodology for combining geostatistical mapping and transmission modelling.

  • Guide the planning of spatial control programmes by identifying affected areas.

  • Current intervention strategy will not be sufficient to eliminate LF in most areas.

  • Alternative strategies will be required to accelerate LF elimination in East Africa.

Abstract

Infectious diseases remain one of the major causes of human mortality and suffering. Mathematical models have been established as an important tool for capturing the features that drive the spread of the disease, predicting the progression of an epidemic and hence guiding the development of strategies to control it. Another important area of epidemiological interest is the development of geostatistical methods for the analysis of data from spatially referenced prevalence surveys. Maps of prevalence are useful, not only for enabling a more precise disease risk stratification, but also for guiding the planning of more reliable spatial control programmes by identifying affected areas. Despite the methodological advances that have been made in each area independently, efforts to link transmission models and geostatistical maps have been limited. Motivated by this fact, we developed a Bayesian approach that combines fine-scale geostatistical maps of disease prevalence with transmission models to provide quantitative, spatially-explicit projections of the current and future impact of control programs against a disease. These estimates can then be used at a local level to identify the effectiveness of suggested intervention schemes and allow investigation of alternative strategies. The methodology has been applied to lymphatic filariasis in East Africa to provide estimates of the impact of different intervention strategies against the disease.

Keywords

Bayesian methods
Fine-scale spatial predictions
Linking maps with models
Lymphatic filariasis
Projections
Uncertainty

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