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Proportion of simulated prevalence data that fit the observed maximum prevalence value. a SSA = 30 m. b SSA = 90 m. c SSA = 250 m. d SSA = 500 m. e SSA = 1 km. Abbreviation: SSA, spatial support of analysis
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Background:
The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers...
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... the maximum observed prevalence value (Fig. 6). For all SSA it is likely to see a similar number of predicted maximum prevalence values compared to the observed data. For the second test statistic, ppP-values ranged from 0.87 to 0.93 (Table 4). This means that simulated data are biased around 0.36 to 0.43 from the minimum observed prevalence data (Fig. 7). For almost all SSA, simulated data predict a higher number of minimum prevalence values compared to the observed data. For the last test statistics, ppP-values ranged from 0.59 to 0.67 ( Table 4), showing that simulated data deviate from around 0.09 to 0.17 from the mean observed prevalence value (Fig. ...
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Background: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers...
Background: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers...
Citations
... Such an estimated density of points at one location can be derived by applying kernel smoothing functions [17] that include the concentration of points within the neighbouring areas, allow for the depiction of smoothed surface maps, and aid in the identification of areas with high or low densities of events, such as identified COVID-19 cases. When it comes to disease mapping, various epidemiological and health science studies have addressed the MAUP throughout the years [18][19][20][21][22][23]. However, only a few studies describe the spatio-temporal pattern of COVID-19 at the point level. ...
Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.
... MAUP is a core geographical consideration. Some domains have extensively investigated the consequences of ignoring its effects, particularly demographics [15] and epidemiology [16], but it is often overlooked in landscape systems modelling or has only recently been discovered [17,18]. In the context of ESs, NC and land use decisions, the support of spatial data affects patterns identified in any analysis for a given spatial extent (e.g., farm field, farm holding and catchment). ...
Spatial data are used in many scientific domains including analyses of Ecosystem Services (ES) and Natural Capital (NC), with results used to inform planning and policy. However, the data spatial scale (or support) has a fundamental impact on analysis outputs and, thus, process understanding and inference. The Modifiable Areal Unit Problem (MAUP) describes the effects of scale on analyses of spatial data and outputs, but it has been ignored in much environmental research, including evaluations of land use with respect to ES and NC. This paper illustrates the MAUP through an ES optimisation problem. The results show that MAUP effects are unpredictable and nonlinear, with discontinuities specific to the spatial properties of the case study. Four key recommendations are as follows: (1) The MAUP should always be tested for in ES evaluations. This is commonly performed in socio-economic analyses. (2) Spatial aggregation scales should be matched to process granularity by identifying the aggregation scale at which processes are considered to be stable (stationary) with respect to variances, covariances, and other moments. (3) Aggregation scales should be evaluated along with the scale of decision making (e.g., agricultural field, farm holding, and catchment). (4) Researchers in ES and related disciplines should up-skill themselves in spatial analysis and core paradigms related to scale to overcome the scale blindness commonly found in much research.
... Ce problème a été constaté pour la première fois en 1934 (Gehlke et Biehl, 1934), puis ensuite exploré par Openshaw et Taylor (1979), et décrit en détails par Openshaw (1984), qui a montré que les coefficients de régression estimés et leurs variances peuvent varier considérablement en fonction des frontières ou des échelles choisies pour l'agrégation et qu'il n'existe pas de choix naturel optimal. Le MAUP a été largement étudié en géographie physique (Dark et Bram, 2007), mais aussi dans les disciplines qui utilisent des données agrégées à l'échelle de zones administratives, comme l'économie (Briant et al., 2010;Kitchin et Mcardle, 2015) et l'épidémiologie (Araujo Navas et al., 2020;Parenteau et Sawada, 2011). ...
La surveillance épidémiologique repose le plus souvent sur l'analyse d'indicateurs de santé agrégés. Nous étudions les problèmes méthodologiques rencontrés lorsque l'on travaille sur ce type de données dans un contexte de santé publique. Dans un premier temps, nous nous intéressons au calcul de la fraction attribuable lorsque l'exposition est épidémique et le nombre d'événements de santé saisonnier. Pour les modèles statistiques de séries temporelles les plus souvent utilisés, nous présentons une méthode d'estimation de cette fraction et de ses intervalles de confiance. Ce travail nous a permis de montrer que la campagne de sensibilisation "Les antibiotiques, c'est pas automatique !" avait conduit à une diminution de plus de moitié des prescriptions antibiotiques associées aux épidémies de syndromes grippaux dès 2005. Par ailleurs, récemment 17% des prescriptions seraient attribuables aux infections virales des voies respiratoires basses pendant la période hivernale, et près de 38% chez les enfants, dont la moitié attribuables aux bronchiolites. Dans un second temps, nous proposons les processus de Hawkes comme modèles pour les maladies contagieuses et étudions l'impact de l'agrégation des données sur leur estimation. Dans ce contexte, nous développons une méthode d'estimation des paramètres du processus et prouvons que les estimateurs ont de bonnes propriétés asymptotiques. Ces travaux fournissent des outils statistiques pour éviter certains biais dus à l'agrégation de données individuelles pour l'étude de fractions attribuables et de maladies contagieuses.
