Achieving explanatory depth and spatial breadth in infectious disease modelling: integrating active and passive case surveillance

Nelli, L. , Ferguson, H. M. and Matthiopoulos, J. (2020) Achieving explanatory depth and spatial breadth in infectious disease modelling: integrating active and passive case surveillance. Statistical Methods in Medical Research, 29(5), pp. 1273-1287. (doi: 10.1177/0962280219856380) (PMID:31213191)

Abstract

Ideally, the data used for robust spatial prediction of disease distribution should be both high-resolution and spatially expansive. However, such in-depth and geographically broad data are rarely available in practice. Instead, researchers usually acquire either detailed epidemiological data with high resolution at a small number of active sampling sites, or more broad-ranging but less precise data from passive case surveillance. We propose a novel inferential framework, capable of simultaneously drawing insights from both passive and active data types. We developed a Bayesian latent point process approach, combining active data collection in a limited set of points, where in-depth covariates are measured, with passive case detection, where error-prone, large-scale disease data are accompanied only by coarse or remotely-sensed covariate layers. Using the example of malaria, we tested our method's efficiency under several hypothetical scenarios of reported incidence in different combinations of imperfect detection and spatial complexity of the environmental variables. We provide a simple solution to a widespread problem in spatial epidemiology, combining latent process modelling and spatially autoregressive modelling. By using active sampling and passive case detection in a complementary way, we achieved the best-of-both-worlds, in effect, a formal calibration of spatially extensive, error-prone data by localised, high-quality data.

• Item Type: Articles
• Additional Information: This project has received funding from the Wellcome Trust [grant no. 200222/Z/15/Z] MiRA.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Matthiopoulos, Professor Jason and Ferguson, Professor Heather and Nelli, Dr Luca
• Authors: Nelli, L., Ferguson, H. M., and Matthiopoulos, J.
• College/School: College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
• Journal Name: Statistical Methods in Medical Research
• Publisher: SAGE Publications
• ISSN: 0962-2802
• ISSN (Online): 1477-0334
• Published Online: 18 June 2019
• Copyright Holders: Copyright © 2019 The Authors
• First Published: Statistical Methods in Medical Research 29(5): 1273-1287
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher