Downscaling incidence risk mapping for a Colombian malaria endemic region

Altamiranda‐Saavedra, Mariano; Porcasi, Ximena; Scavuzzo, Carlos Marcelo; Correa, Margarita M.

doi:10.1111/tmi.13128

Cited by 9 publications

(9 citation statements)

References 27 publications

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“…In addition, information on environmental layers was extracted from the mask of urban areas. A database was created that contained the information of an urban centre per municipality, based on the criterion of greater nocturnal luminosity [20].…”

Section: Methodsmentioning

confidence: 99%

“…The analyses performed to obtain the risk map and validate the model were as previously described [20]. Briefly, the association between observed API and environmental variables was evaluated using the GLM; then, an estimated API map with a 1 km 2 resolution was constructed using the most explicative variables, precipitation and NDVI, in ENVI Software v.5.3.…”

Section: Methodsmentioning

confidence: 99%

“…Among the variables showing a stronger correlation with malaria risk are precipitation and temperature [13], vegetation [14], humidity [15], deforestation [16], land use and cover [17], altitude [18] and human population density, terrain elevations and proximity to Anopheles habitats [19]. The construction of risk maps is achieved by the use of geographic information systems – GIS, satellite images at different spatio‐temporal scales and the application of statistical models that may include the generalised linear model‐GLM [19,20], multicriteria decision analysis [21,22] and Bayesian logistic regression [23,24].…”

Section: Introductionmentioning

confidence: 99%

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Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Piedrahita

Altamiranda‐Saavedra

Correa

2020

Tropical Med Int Health

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Objective To categorise and map, at high resolution, the risk of malaria incidence in the Pacific region, the main malaria‐endemic region of Colombia. Methods The relationship between the environmental variables Normalized Difference Vegetation Index Normalized Difference Water Index, Topographic Wetness Index, precipitation and temperature with the observed Annual Parasitic Index was evaluated using a generalised linear model. An incidence risk map at a resolution of 1 km2 was constructed and projected to the entire endemic region. Associations of malaria risk categories with both presence records and co‐occurrence of the three main malaria vectors were determined. Results A significant correlation was found for the incidence of malaria with precipitation and Normalized Difference Vegetation Index (R2 = 0.98, P < 0.05), whereas there was no significant correlation with the remaining environmental and topographic variables. Moderate‐ to high‐risk areas were located mainly in central Chocó Department along the San Juan and Atrato rivers and in areas west of the Cauca River and Pacific lowlands of the Andes Mountains. There was a statistically significant relationship for the presence of the two main vectors Anopheles darlingi and Anopheles nuneztovari with the high malaria risk category. Furthermore, malaria risk was directly proportional to the number of co‐occurring vector species. Conclusions The map obtained provides useful information on the risk of malaria in particular places of the Colombian Pacific region. The data can be used by public entities to optimise the allocation of economic resources for vector control interventions and surveillance.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Piedrahita

Altamiranda‐Saavedra

Correa

2020

Tropical Med Int Health

Self Cite

View full text Add to dashboard Cite

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“…For example, a study in Colombia had developed a modeling framework based on geographic information systems (GIS) and remote-sensing environmental data using multiple regression analysis, and subsequently, a model was constructed to estimate the annual parasite incidence and to design risk maps for the entire endemic region. 15 Another study used binomial logistic regression to examine the determinants of malaria risk among children, and, subsequently, model-based geostatistical methods were applied to analyze, predict, and map malaria prevalence. 16 Another study used epidemiology and surveillance data to develop and calculate risk factor coefficients via a Bayesian spatial negative binomial model and subsequently used the model-based relative risk estimates to map malaria risk areas.…”

Section: Introductionmentioning

confidence: 99%

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

Xiao-min

Thanapongtharm

Lawawirojwong

et al. 2020

The American Journal of Tropical Medicine and Hygiene

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. In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance–response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China–Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.

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“…Examples include downscaling malaria incidence rates from regional to urban centers through multivariate regression, hand-foot-mouth disease from national to township levels using generalized linear models, and applying hierarchical Bayesian frameworks to develop 5 km gridded risk maps of malaria, Plasmodium falciparum. (Gething 2012; Wang et al 2017;Altamiranda-Saavedra et al 2018). While these studies were able to improve coarse-scale information, they still failed to meet a spatial resolution relevant to tactical-level epidemiological mapping applications or the processing time required to support time-sensitive operations.…”

Section: Literature Reviewmentioning

confidence: 99%

New and Enhanced Tools for Civil Military Operations (NET-CMO)

Wayant¹,

Becker²,

Parker³

et al. 2020

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Civil Military Operations (CMO) associated geospatial modeling is intended to enable increased knowledge of regional stability, assist in Foreign Humanitarian Assistance (FHA), and provide support to Force Health Protection (FHP) operational planning tasks. However, current geoenabled methodologies and technologies are lacking in their overall capacity to support complex mission analysis efforts focused on understanding these important stability factors and mitigating threats to Army soldiers and civilian populations. CMO analysts, planners, and decision-makers do not have a robust capability to both spatially and quantitatively identify Regions of Interest (ROI), which may experience a proliferation in health risks such as vector-borne diseases in areas of future conflict. Additionally, due to this general absence of geoenabled health assessment models and derived end-products, CMO stakeholders are adversely impacted in their Military Decision Making Process (MDMP) capabilities to develop comprehensive area studies and plans such as Course of Action (COA). The NET-CMO project is focused on fostering emerging geoenabling capabilities and technologies to improve military situational awareness for assessment and planning of potential health threat-risk vulnerabilities.

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Downscaling incidence risk mapping for a Colombian malaria endemic region

Cited by 9 publications

References 27 publications

Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

New and Enhanced Tools for Civil Military Operations (NET-CMO)

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