Mapping Ex Ante Risks of COVID-19 in Indonesia using a Bayesian Geostatistical Model on Airport Network Data

Abstract

A rapid response to global infectious disease outbreaks is crucial to protect public health. Ex ante information on the spatial probability distribution of early infections can guide governments to better target protection efforts. We propose a two-stage statistical approach to spatially map the ex ante importation risk of COVID-19 and its uncertainty across Indonesia based on a minimal set of routinely available input data related to the Indonesian flight network, traffic and population data, and geographical information. In a first step, we use a generalised additive model to predict the ex ante COVID-19 risk for 78 domestic Indonesian airports based on data from a global model on the disease spread and covariates associated with Indonesian airport network flight data prior to the global COVID-19 outbreak. In a second step, we apply a Bayesian geostatistical model to propagate the estimated COVID-19 risk from the airports to all of Indonesia using freely available spatial covariates including traffic density, population and two spatial distance metrics. The results of our analysis are illustrated using exceedance probability surface maps, which provide policy-relevant information accounting for the uncertainty of the estimates on the location of areas at risk and those that might require further data collection.

Sub-national estimates of vitamin A supplementation coverage in children: a geospatial analysis of 45 low and middle-income countries

“…This approach applies Integrated Nested Laplace Approximations (INLA) (Rue et al 2009) and is easily applicable with the R-package R-INLA. This package has been very popular and successful, particularly in epidemiological modelling (Cameletti et al 2011, Bhatt et al 2015, Seufert et al 2022. R-INLA not only caters to a plethora of models (Rue et al 2009), but is also often faster than models using Markov Chain Monte Carlo, and provides easily interpretable diagnostics (Wang et al 2018).…”