A geostatistical framework for combining spatially referenced disease prevalence data from multiple diagnostics

Amoah, Benjamin; Diggle, Peter J.; Giorgi, Emanuele

doi:10.1111/biom.13142

Cited by 11 publications

(9 citation statements)

References 28 publications

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“…Finally, we point out that the statistical ideas and principles presented in this paper are applicable to any statistical analysis of epidemiological data based on regression modelling. This also includes extensions of the standard geostatistical model for prevalence mapping to spatio-temporal analysis, modelling of zero-inflated prevalence data, combining data from a mix of randomized and opportunistic surveys [ 56 , 57 ] and multiple diagnostics [ 58 ].…”

Section: Discussionmentioning

confidence: 99%

Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict

Giorgi

Fronterrè

Macharia

et al. 2021

J. R. Soc. Interface.

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This paper provides statistical guidance on the development and application of model-based geostatistical methods for disease prevalence mapping. We illustrate the different stages of the analysis, from exploratory analysis to spatial prediction of prevalence, through a case study on malaria mapping in Tanzania. Throughout the paper, we distinguish between predictive modelling, whose main focus is on maximizing the predictive accuracy of the model, and explanatory modelling, where greater emphasis is placed on understanding the relationships between the health outcome and risk factors. We demonstrate that these two paradigms can result in different modelling choices. We also propose a simple approach for detecting over-fitting based on inspection of the correlation matrix of the estimators of the regression coefficients. To enhance the interpretability of geostatistical models, we introduce the concept of domain effects in order to assist variable selection and model validation. The statistical ideas and principles illustrated here in the specific context of disease prevalence mapping are more widely applicable to any regression model for the analysis of epidemiological outcomes but are particularly relevant to geostatistical models, for which the separation between fixed and random effects can be ambiguous.

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Section: Discussionmentioning

confidence: 99%

Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict

Giorgi

Fronterrè

Macharia

et al. 2021

J. R. Soc. Interface.

Self Cite

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“…When different diagnostics have been used in the same geographical region, statistical models that can combine information from multiple diagnostics have the potential to make better use of all available data sources. Amoah et al (2018) introduce a geostatistical framework for combining data from multiple diagnostics and apply this to prevalence data obtained by blood-smear microscopy and by RAPLOA. They propose a bivariate geostatistical model that allows estimation of the calibration relationship between the two diagnostics, and uses this relationship to predict blood-smear microscopy-based prevalence in subregions where only the RAPLOA method has been used.…”

Section: Combining Information From Multiple Diagnosticsmentioning

confidence: 99%

A Tale of Two Parasites: Statistical Modelling to Support Disease Control Programmes in Africa

Diggle¹,

Giorgi²,

Atsame³

et al. 2020

Statist. Sci.

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Vector-borne diseases have long presented major challenges to the health of rural communities in the wet tropical regions of the world, but especially in sub-Saharan Africa. In this paper we describe the contribution that statistical modelling has made to the global elimination programme for one vector-borne disease, onchocerciasis.We explain why information on the spatial distribution of a second vector-borne disease, Loa loa, is needed before communities at high risk of onchocerciasis can be treated safely with mass distribution of ivermectin, an antifiarial medication.

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“…For example, Manzi et al incorporate information from multiple, biased, commercial surveys to provide more accurate and precise estimates of smoking prevalence in local authorities across the east of England [11]. A number of geostatistical frameworks for infectious disease modelling based on multiple diagnostic tests have been developed [12,13,14]. These accommodate different sources of heterogeneity among the tests to deliver more reliable and precise inferences on disease prevalence.…”

Section: Introductionmentioning

confidence: 99%

“…13 https://github.com/cmmid/pcr-profile to estimate the term P(Infected t−k ) appearing twice in (49), evaluating the following estimator for each k = 0, . .…”

mentioning

confidence: 99%

Local prevalence of transmissible SARS-CoV-2 infection: an integrative causal model for debiasing fine-scale targeted testing data

Nicholson

Lehmann

Padellini

et al. 2021

Preprint

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Targeted surveillance testing schemes for SARS-CoV-2 focus on certain subsets of the population, such as individuals experiencing one or more of a prescribed list of symptoms. These schemes have routinely been used to monitor the spread of SARS-CoV-2 in countries across the world. The number of positive tests in a given region can provide local insights into important epidemiological parameters, such as prevalence and effective reproduction number. Moreover, targeted testing data has been used inform the deployment of localised non-pharmaceutical interventions. However, surveillance schemes typically suffer from ascertainment bias; the individuals who are tested are not necessarily representative of the wider population of interest. Here, we show that data from randomised testing schemes, such as the REACT study in the UK, can be used to debias fine-scale targeted testing data in order to provide accurate localised estimates of the number of infectious individuals. We develop a novel, integrative causal framework that explicitly models the process underlying the selection of individuals for targeted testing. The output from our model can readily be incorporated into longitudinal analyses to provide local estimates of the reproduction number. We apply our model to characterise the size of the infectious population in England between June 2020 and January 2021. Our local estimates of the effective reproduction number are predictive of future changes in positive case numbers. We also capture local increases in both prevalence and effective reproductive number in the South East from November 2020 to December 2020, reflecting the spread of the Kent variant. Preparations for future epidemics should ensure the rapid deployment of both types of schemes to accurately monitor the spread of emerging and ongoing infectious diseases.

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A geostatistical framework for combining spatially referenced disease prevalence data from multiple diagnostics

Cited by 11 publications

References 28 publications

Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict

Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict

A Tale of Two Parasites: Statistical Modelling to Support Disease Control Programmes in Africa

Local prevalence of transmissible SARS-CoV-2 infection: an integrative causal model for debiasing fine-scale targeted testing data

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