Dataset Information

A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany.

ABSTRACT:

Background

As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale.

Methods

By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations.

Results

A strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables' partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure.

Conclusions

The multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.

SUBMITTER: Scarpone C

PROVIDER: S-EPMC7424139 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesKnowledge about the socioeconomic spread of the first wave of COVID-19 infections in Germany is scattered across different studies. We explored whether COVID-19 incidence rates differed between counties according to their socioeconomic characteristics using a wide range of indicators.Data and methodWe used data from the Robert Koch-Institute (RKI) on 204 217 COVID-19 diagnoses in the total German population of 83.1 million, distinguishing five distinct periods between 1 January and 23 July 2020. For each period, we calculated age-standardised incidence rates of COVID-19 diagnoses on the county level and characterised the counties by 166 macro variables. We trained gradient boosting models to predict the age-standardised incidence rates with the macrostructures of the counties and used SHapley Additive exPlanations (SHAP) values to characterise the 20 most prominent features in terms of negative/positive correlations with the outcome variable.ResultsThe first COVID-19 wave started as a disease in wealthy rural counties in southern Germany and ventured into poorer urban and agricultural counties during the course of the first wave. High age-standardised incidence in low socioeconomic status (SES) counties became more pronounced from the second lockdown period onwards, when wealthy counties appeared to be better protected. Features related to economic and educational characteristics of the young population in a county played an important role at the beginning of the pandemic up to the second lockdown phase, as did features related to the population living in nursing homes; those related to international migration and a large proportion of foreigners living in a county became important in the postlockdown period.ConclusionHigh mobility of high SES groups may drive the pandemic at the beginning of waves, while mitigation measures and beliefs about the seriousness of the pandemic as well as the compliance with mitigation measures may put lower SES groups at higher risks later on.