Regionalization of a Landscape-Based Hazard Index of Malaria Transmission: An Example of the State of Amapá, Brazil

Abstract

Identifying and assessing the relative effects of the numerous determinants of malaria transmission, at different spatial scales and resolutions, is of primary importance in defining control strategies and reaching the goal of the elimination of malaria. In this context, based on a knowledge-based model, a normalized landscape-based hazard index (NLHI) was established at a local scale, using a 10 m spatial resolution forest vs. non-forest map, landscape metrics and a spatial moving window. Such an index evaluates the contribution of landscape to the probability of human-malaria vector encounters, and thus to malaria transmission risk. Since the knowledge-based model is tailored to the entire Amazon region, such an index might be generalized at large scales for establishing a regional view of the landscape contribution to malaria transmission. Thus, this study uses an open large-scale land use and land cover dataset (i.e., the 30 m TerraClass maps) and proposes an automatic data-processing chain for implementing NLHI at large-scale. First, the impact of coarser spatial resolution (i.e., 30 m) on NLHI values was studied. Second, the data-processing chain was established using R language for customizing the spatial moving window and computing the landscape metrics and NLHI at large scale. This paper presents the results in the State of Amapá, Brazil. It offers the possibility of monitoring a significant determinant of malaria transmission at regional scale.

A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires

“…To quantify the relationships between urban landscape factors and dengue cases, the authors adopted methodologies based on statistical and spatial analysis fields, classically employed in spatial epidemiology or disease risks geography [38]. Correlation is commonly used to quantify the direction and strength of the relationship, through Pearson and Spearman (ranking) correlation coefficients (ids: 1, 24,29,31,33,42,44,53,56,60,61,62,64,65,67,69,76). The odds ratio, which quantifies the strength of the association between two events is also often used (ids: 13,20,25,26,27,34,48,68).…”

“…Only two articles used landscape metrics to explore the impact of more in-depth ecological characteristics of an urban landscape on dengue transmission (ids: 57, 69). Landscape metrics have been separately applied to malaria transmission for assessing the influence of landscape factors relative to exposure risk [75,76]. The representativeness of sampling strategies during intra-urban dengue seroprevalence surveys may be improved by the use of GIS and remote sensing techniques ( [77], e.g., urban environmental clustering and Aedes density); ( [78], e.g., Urban typology) and help to objectify the choice of geographical units ( [70], e.g., criteria of intra-unit homogeneity, areal and population size, compactness); ( [71,79], e.g., Concept of integrated geons).…”

“…-vegetation cover throughNDVI and VFC (ids: 3,10,29,36,42,44,45,47,51,56,69,76,78); -water areas (ids:14,36,41,47,56,66,67), and cropland (id: 36).• "Building" is characterized by roof shape (id: 54), density(ids: 31, 41, 57, 69, 70), and surroundings based on density and distance from other land cover/use classes, e.g., vegetation (ids: 31, 56, 57, 67, 69, 70, 71), bare soil (ids: 19, 71), water-areas (ids: 56, 67, 71), cropland (ids: 36, 70), or road density (id: 36);…”

Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth Observation

“…More in-depth landscape features (e.g., compositional and configurational patterns) could be explored in future studies. Our previous studies characterized forest/non-forest landscapes by computing various landscape metrics and established their links with malaria cases for understanding the contribution of Amazon deforestation on human-vector contact [28,29]. We found very few examples that used landscape metrics in dengue epidemiology, although these metrics have been widely applied in the assessment of LULC changes.…”