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.
Vitamin A supplementation (VAS) can protect children from the adverse health consequences of vitamin A deficiency. To inform the geographically precise targeting of VAS programs and provide a benchmark for monitoring progress in reducing geographic disparities in coverage over time, we created high resolution maps (5km x 5km) of the proportion of preschool-age children (6-59 months) covered by VAS in 45 UNICEF designated VAS priority countries using data from the Demographic and Health Surveys program. In addition to prevalence, we estimated absolute VAS coverage and exceedance probabilities using thresholds of 0.5 and 0.7. We found that most countries had coverage levels below 70\%. Coverage varied substantially between and within countries. Inter-national variations were most notable in Latin America and the Caribbean, as well as Africa, whereas intra-national variations were greatest in some south Asian and west and central African countries. These maps, especially when used along with high-resolution data on indicators of VAS need, could help VAS programs improve equity.
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