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Assessing human responses to extreme weather events is of great importance to plan for climate-related risks and improve disaster management. In this research, we investigate changes in mobility trends of Facebook users during the 2021 flood in Europe. We use advanced spatial-analytical techniques to identify hot spots of mobility change across Belgium, the Netherlands, Germany, and Luxembourg. We find heterogenous peaks of (im-)mobility across time and regions, with notable variability within hydrographic basins. Major changes of population density can also be detected before, during, and after the flood event. While caution must be taken regarding the representativeness of Facebook data, our findings remain valuable for guiding disaster management strategies in complementarity with other satellite-based datasets. These emphasise the value of data-sharing by tech companies and potentially opens up new opportunities for more coordinated responses to climate-extremes at the international level.

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Additional file 1: Table S1. Detailed spatiotemporal clusters of cholera cases, western DRC, week 45, 2012���week 42, 2013.

Additional file 3: : Figure S3. Response curves estimated for the boosted regression trees (BRT) model trained on measures of hospitalisation incidence (HI) computed for the entire epidemic period under consideration (01/03–30/11/2020).

Additional file 1: Figure S1. Visual comparison between the evolution of daily new hospitalisations and the evolution of the temporal covariates considered in the present study. All temporal covariates were averaged over a distinct hospital catchment area (HCA), and each curve thus corresponds to a distinct HCA. All variables were also preliminary treated by a moving average of 7 days. The mobility index is based on mobile phone data (see the text for further detail); the temperature is reported in the Kelvin scale (K); the relative humidity is expressed as a percentage (ratio between vapor partial pressure and saturation vapor partial pressure; 100% meaning an air mass fully charged in humidity); and the solar radiation is reported in Joules per square metre (J/m2).

info:eu-repo/semantics/inPress

Additional file 4: Table S1. Analyses of the potential predictors of spatial heterogeneity in hospitalisation incidence of nursing home (NH) residents. This table is equivalent to Table 1 and summarises the results of univariate linear regression (ULR), multivariate linear regression (MLR), and boosted regression trees (BRT) analyses performed to investigate the association between measures of hospitalisation incidence (HI) of NH residents and various spatial covariates associated with hospital catchment areas (HCAs). We report the following metrics: the coefficient of determination (R2) for the ULR analyses, the regression coefficient (β) for the MLR analyses, and the relative influence (RI) associated with each spatial covariate for the BRT analyses. In addition, we also report the overall R2 and Spearman correlation (“cor.”) for each distinct MLR and BRT analysis, respectively. (*) indicates if a given R2 or β is significant (p-value < 0.05). Table S2. Analyses of the potential predictors of spatial heterogeneity in hospitalisation incidence, when excluding Brussels-Capital Region and a potential outlier area. This table is equivalent to Table 1 and summarises the results of univariate linear regression (ULR), multivariate linear regression (MLR), and boosted regression trees (BRT) analyses performed to investigate the association between measures of hospitalisation incidence (HI) and various spatial covariates associated with hospital catchment areas (HCAs). For these alternative analyses, we discarded the six HCAs of the Brussels-Capital Region, as well as a potential outlier HCA (marked with an asterisk in Fig. 4). We report the following metrics: the coefficient of determination (R2) for the ULR analyses, the regression coefficient (β) for the MLR analyses, and the relative influence (RI) associated with each spatial covariate for the BRT analyses. In addition, we also report the overall R2 and Spearman correlation (“cor.”) for each distinct MLR and BRT analysis, respectively. (*) indicates if a given R2 or β is significant (p-value < 0.05). Table S3. Comparison of the different boosted regression trees (BRT) models trained in the present study. Specifically, we here report the Spearman correlation between hospitalisation incidence predicted under various BRT models (one row = one specific BRT model) and observed hospitalisation incidence. As detailed in the text, the measure of hospitalisation incidence (HI) is computed as the cumulative number of new hospitalisations per 100,000 inhabitants for a given hospital catchment area (HCA) and a given time period. For each comparison between predicted and observed set of HI values, we report both the mean Spearman correlation value, as well as the minimum and maximum Spearman correlation values, all obtained while considering the ten BRT model replicates. (*) refers to the BRT model trained with HI values of the second epidemic wave (01/09-30/11/2020) but also including HI values of the first epidemic wave (01/03-31/05/2020) as a potential predictor in addition to all the other considered spatial predictors.

Additional file 2: : Figure S2. Principal component analysis (PCA) based on all spatial covariates as well as measures of hospitalisation incidence (HI). Specifically, we here included in the ACP HI values computed for the period corresponding to the first (01/03–31/05/2020) and the second (01/09–30/11/2020) epidemic waves. Each dot corresponds to a distinct hospital catchment area (HCA) and is displayed with an area proportional to the HI value computed for the entire epidemic period under consideration (01/03–30/11/20).

Additional file 2: Table S2. Detailed spatiotemporal clusters of cholera cases, western DRC, week 40, 2017���week 52, 2018.

SARS-CoV-2, and the disease it causes, COVID-19, is sweeping through the world, disrupting human activities everywhere. The consequences of this on-going event on societies are yet to be fully understood. The emergence of SARS-CoV-2 illustrates how human-environment interaction should be framing research on pathogen spillover. Furthermore, the geography of human contacts at various scales in our globalized and urbanized world affects its diffusion. Both elements plead for a robust backbone of geography of health, including land use, to understanding disease emergence and diffusion. ispartof: CURRENT OPINION IN ENVIRONMENTAL SUSTAINABILITY vol:46 pages:43-45 ispartof: location:United States status: published

Additional file 2 Descriptive Statistics of parasite prevalence surveys assembled in Dar es Salaam and Kampala, respectively.