Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Gopal, Sucharita; Ma, Yaxiong; Chen, Xin; Pitts, Joshua; Were, Lawrence

doi:10.3390/ijerph16245078

Cited by 13 publications

(9 citation statements)

References 61 publications

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“…The difference in malaria incidence spatially is influenced by several variables, including rainfall, proximity to water, vegetation, and population density. The differences in these variables show different effects on malaria incidence 5,7,11 .…”

Section: Resultsmentioning

confidence: 99%

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Predictors of Malaria Incidence in Rural Eastern Indonesia

2020

IJFMT

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In Indonesia, the largest number of malaria cases was contributed to by Eastern Indonesia. The study aimed to analyze the predictors of malaria incidence in rural Eastern Indonesia. This study analyzes the 2018 Indonesia Basic Health Survey data. In the final stage binary logistic regression was used to determine the incidence of malaria incidence in 86,382 respondents. Variables analyzed included malaria incidence, regional/province, age, gender, educational level, work type, marital status, wealth status, and health insurance. The analysis found that people who lived in rural Maluku and rural North Maluku had a lower risk of experiencing malaria than those who lived in rural areas of East Nusa Tenggara. People in rural West Papua and rural Papua have a higher risk than those who live in rural East Nusa Tenggara. Men were 1.107 times more likely than women to have malaria incidence.People with primary school education were 1.237 times more likely to develop malaria than those without education. People with a college education were 1.440 times more likely to develop malaria than those without education. People who have a wealth of poorer status are 0.804 times more likely to have malaria than those who are poorest. People who have the richest wealth status are 0.851 times more likely to have malaria than those who are the poorest. There were 4 variables that have proven to be significant as predictors of incidence in rural Eastern Indonesia, namely regional/province, gender, education, and wealth.

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Section: Resultsmentioning

confidence: 99%

“…Environmental factors found to be associated with malaria incidence are often related to water location and climate change. This is related to the Plasmodium ecosystem as parasites, Anopheles mosquitoes as vectors, and humans as hosts 3,4,5 . Ecosystem risks of this kind are found in many rural areas 6,7,8 .…”

Section: Introductionmentioning

confidence: 99%

Predictors of Malaria Incidence in Rural Eastern Indonesia

2020

IJFMT

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“…Twenty papers used a geographically weighted regression (GWR) model [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] which fits local regression models to each observation or region rather than a single global model [66]. Each local model has different coefficients, estimated using information from connected observations that are weighted by a function of distance, such as the one shown in figure 3c.…”

Section: Local Regression Modelsmentioning

confidence: 99%

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Lee

Jarvis

Edmunds

et al. 2021

J. R. Soc. Interface.

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Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

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“…We use the population size, average annual rainfall, vegetation index of the region, and the proximity to water as spatial covariates. Under a spatial regression framework, Gopal et al (2019) analyzes malaria incidence in Kenya using these environmental variables. In this study, we extend this approach to multiple countries in the African Great Lakes region.…”

Section: Malaria Incidence In the African Great Lakes Regionmentioning

confidence: 99%

A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets

Seiyon¹,

Park²

2020

Preprint

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Nonstationary non-Gaussian spatial data are common in many disciplines, including climate science, ecology, epidemiology, and social sciences. Examples include count data on disease incidence and binary satellite data on cloud mask (cloud/nocloud). Modeling such datasets as stationary spatial processes can be unrealistic since they are collected over large heterogeneous domains (i.e., spatial behavior differs across subregions). Although several approaches have been developed for nonstationary spatial models, these have focused primarily on Gaussian responses. In addition, fitting nonstationary models for large non-Gaussian datasets is computationally prohibitive. To address these challenges, we propose a scalable algorithm for modeling such data by leveraging parallel computing in modern high-performance computing systems. We partition the spatial domain into disjoint subregions and fit locally nonstationary models using a carefully curated set of spatial basis functions. Then, we combine the local processes using a novel neighbor-based weighting scheme. Our approach scales well to massive datasets (e.g., 1 million samples) and can be implemented in nimble, a popular software environment for Bayesian hierarchical modeling. We demonstrate our method to simulated examples and two large real-world datasets pertaining to infectious diseases and remote sensing.

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Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Cited by 13 publications

References 61 publications

Predictors of Malaria Incidence in Rural Eastern Indonesia

Predictors of Malaria Incidence in Rural Eastern Indonesia

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets

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