A Downscaling Approach to Compare COVID-19 Count Data from Databases Aggregated at Different Spatial Scales

Python, André; Bender, Andreas; Blangiardo, Marta; Illian, Janine; Ying, Lin; Liu, Baoli; Lucas, Tim; Tan, Siwei; Wen, Yingying; Svanidze, Davit; Yin, Jianwei

doi:10.1111/rssa.12738

Cited by 4 publications

(6 citation statements)

References 45 publications

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“…A location 1% farther away from an airport decreases the (log) COVID‐19 risk by 0.5% on average while keeping all other explanatory variables constant. Both logged traffic intensity (

\log (TRAFFIC)

) and logged population (

\log (POP)

) are associated with an increase in the logged COVID‐19 risk, which is consistent with the literature (Bhadra et al, 2021; Python et al, 2022; Zhang et al, 2020).…”

Section: Resultssupporting

confidence: 90%

“…Larger population density increases the pool of individuals to be infected and quickens the spread of the virus (Bhadra et al, 2021 ). Population is considered to be a major driver associated with the transmission of the COVID‐19 at various spatial scales (Python et al, 2022 ; Zhang et al, 2020 ). Therefore, we expect more vulnerability to COVID‐19 outbreaks close to densely populated areas.…”

Section: Spatial Propagation Model: a Hierarchical Bayesian Geostatis...mentioning

confidence: 99%

“…Similarly, we assume an a priori positive effect of

\log (TRAFFIC)

and

\log (POP)

on COVID‐19 risk (cf. Python et al, 2022; Xu et al, 2019; Zhang et al, 2020). Further details on the prior specifications are provided in Appendix A.3.2.…”

Section: Spatial Propagation Model: a Hierarchical Bayesian Geostatis...mentioning

confidence: 99%

“…The COVID‐19 transmission among humans tends to occur indoors, especially during cold weather periods (Ahlawat et al, 2020). COVID‐19 initially spread from Wuhan to other regions through the worldwide air and sea traffic network (Python et al, 2022; Zhang et al, 2020). The role of global transport has also been observed previously in the spread of the severe acute respiratory syndrome (SARS), another coronavirus disease (Tatem et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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

Seufert

Python

Weisser

et al. 2022

Journal of the Royal Statistical Society Series A: Statistics in Society

Self Cite

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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.

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“…A location 1% farther away from an airport decreases the (log) COVID‐19 risk by 0.5% on average while keeping all other explanatory variables constant. Both logged traffic intensity (

\log (TRAFFIC)

) and logged population (

\log (POP)

) are associated with an increase in the logged COVID‐19 risk, which is consistent with the literature (Bhadra et al, 2021; Python et al, 2022; Zhang et al, 2020).…”

Section: Resultssupporting

confidence: 90%

Section: Spatial Propagation Model: a Hierarchical Bayesian Geostatis...mentioning

confidence: 99%

“…Similarly, we assume an a priori positive effect of

\log (TRAFFIC)

and

\log (POP)

on COVID‐19 risk (cf. Python et al, 2022; Xu et al, 2019; Zhang et al, 2020). Further details on the prior specifications are provided in Appendix A.3.2.…”

Section: Spatial Propagation Model: a Hierarchical Bayesian Geostatis...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Seufert

Python

Weisser

et al. 2022

Journal of the Royal Statistical Society Series A: Statistics in Society

Self Cite

View full text Add to dashboard Cite

show abstract

“…Simulation studies have shown promise but have raised questions (e.g., the ability for it to accurately specify fine‐scale patterns from aggregated data) about the appropriate usage of this method in real‐world scenarios (Arambepola et al, 2022). While not previously used in an applied ecological context, it has been successfully applied to disease risk modeling for COVID‐19 (Python et al, 2022) and malaria (Lucas et al, 2022). This approach does, however, build upon similar methodological studies implemented in ecology that downscales binary presence/absence species distribution model predictions to produce higher‐resolution predictions using a hierarchal approach (Keil et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Bayesian areal disaggregation regression to predict wildlife distribution and relative density with low‐resolution data

Murphy,

Ciuti,

Burkitt

et al. 2023

Ecological Applications

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For species of conservation concern and human‐wildlife conflict, it is imperative that spatial population data are available to design adaptive‐management strategies and be prepared to meet challenges such as land use and climate change, disease outbreaks, and invasive species spread. This can be difficult, perhaps impossible, if spatially explicit wildlife data are not available. Low‐resolution areal counts, however, are common in wildlife monitoring, i.e., number of animals reported for a region, usually corresponding to administrative subdivisions, e.g., region, province, county, departments, or cantons. Bayesian areal disaggregation regression is a solution to exploit areal counts and provide conservation biologists with high‐resolution species distribution predictive models. This method originated in epidemiology but lacks experimentation in ecology. It provides a plethora of applications to change the way we collect and analyse data for wildlife populations. Based on high‐resolution environmental rasters, the disaggregation method disaggregates the number of individuals observed in a region and distributes them at the pixel level (e.g., 5x5 km or finer resolution), therefore converting the low‐resolution data into high‐resolution distribution and indices of relative density. In our demonstrative study, we disaggregated areal count data from hunting bag returns to disentangle the changing distribution and population dynamics of three deer species (red, sika and fallow) in Ireland from 2000 to 2018. We show an application of the Bayesian areal disaggregation regression method and document marked increases in relative population density and extensive range expansion for each of the three deer species across Ireland. We challenged our disaggregated model predictions by correlating them with independent deer surveys carried out in field sites and alternative deer distribution models built using presence‐only and presence‐absence data. Finding high correlation with both independent datasets, we highlighted the accurate ability of Bayesian areal disaggregation regression to capture fine scale spatial patterns of animal distribution. This study opens new scenarios for wildlife managers and conservation biologists to reliably use regional count data disregarded so far in species distribution modeling. Thus, representing a step forward in our ability to monitor wildlife population and meet challenges in our changing world.This article is protected by copyright. All rights reserved.

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Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales

Nightingale,

Subramanian,

Schwarzer

et al. 2024

Commun Med

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Background As cases of visceral leishmaniasis (VL) in India dwindle, there is motivation to monitor elimination progress on a finer geographic scale than sub-district (block). Low-incidence projections across geographically- and demographically- heterogeneous communities are difficult to act upon, and equitable elimination cannot be achieved if local pockets of incidence are overlooked. However, maintaining consistent surveillance at this scale is resource-intensive and not sustainable in the long-term. Methods We analysed VL incidence across 45,000 villages in Bihar state, exploring spatial autocorrelation and associations with local environmental conditions in order to assess the feasibility of inference at this scale. We evaluated a statistical disaggregation approach to infer finer spatial variation from routinely-collected, block-level data, validating against observed village-level incidence. Results This disaggregation approach does not estimate village-level incidence more accurately than a baseline assumption of block-homogeneity. Spatial auto-correlation is evident on a block-level but weak between neighbouring villages within the same block, possibly suggesting that longer-range transmission (e.g., due to population movement) may be an important contributor to village-level heterogeneity. Conclusions Increasing the range of reactive interventions to neighbouring villages may not improve their efficacy in suppressing transmission, but maintaining surveillance and diagnostic capacity in areas distant from recently observed cases - particularly along routes of population movement from endemic regions - could reduce reintroduction risk in currently unaffected villages. The reactive, spatially-targeted approach to VL surveillance limits interpretability of data observed at the village level, and hence the feasibility of routinely drawing and validating inference at this scale.

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A Downscaling Approach to Compare COVID-19 Count Data from Databases Aggregated at Different Spatial Scales

Cited by 4 publications

References 45 publications

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

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

Bayesian areal disaggregation regression to predict wildlife distribution and relative density with low‐resolution data

Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales

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