Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya

Geospatial based model for malaria risk prediction in Kilombero valley, South-eastern, Tanzania

BACKGROUND

Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

METHODS

This study employs a geospatial based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability.

RESULTS

The study demonstrates that the majority of the study area falls under moderate risk level (61%), followed by the low risk level (31%), while the high malaria risk area covers a small area, which occupies only 8% of the total area.

CONCLUSION

The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions.

[Contributions of epidemiology to the control of leishmaniasis]

Leishmaniasis despite government efforts, groups and individuals continues to be a major public health problem, it is estimated that globally occur between 50 000 and 90 000 new cases of visceral leishmaniasis and between 0.5 and 1 million tegumentary leishmaniasis, plus in some regions, this parasitism has an endemo-epidemic nature, and in recent years its frequency and distribution have increased. The purpose of this paper is to show some contributions of epidemiology to the control of leishmaniasis, as a result of the description and analysis of the distribution and determinants of this parasitism, extremely complex in terms of transmitter, etiological agent, reservoir and susceptible. Based on the review of the scientific literature in the context of a descriptive, documentary and retrospective study, the objective of this paper was achieved. It is concluded that the usefulness of epidemiology in the control of leishmaniasis is clear or in any case reaffirms the validity and practicality of epidemiology in the programmatic and operational task of health intervention in the case of leishmaniasis.

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).

Spatial distribution of Culex mosquito abundance and associated risk factors in Hanoi, Vietnam

Japanese encephalitis (JE) is the major cause of viral encephalitis (VE) in most Asian-Pacific countries. In Vietnam, there is no nationwide surveillance system for JE due to lack of medical facilities and diagnoses. Culex tritaeniorhynchus, Culex vishnui, and Culex quinquefasciatus have been identified as the major JE vectors in Vietnam. The main objective of this study was to forecast a risk map of Culex mosquitoes in Hanoi, which is one of the most densely populated cities in Vietnam. A total of 10,775 female adult Culex mosquitoes were collected from 513 trapping locations. We collected temperature and precipitation information during the study period and its preceding month. In addition, the other predictor variables (e.g., normalized difference vegetation index [NDVI], land use/land cover and human population density), were collected for our analysis. The final model selected for estimating the Culex mosquito abundance included centered rainfall, quadratic term rainfall, rice cover ratio, forest cover ratio, and human population density variables. The estimated spatial distribution of Culex mosquito abundance ranged from 0 to more than 150 mosquitoes per 900m2. Our model estimated that 87% of the Hanoi area had an abundance of mosquitoes from 0 to 50, whereas approximately 1.2% of the area showed more than 100 mosquitoes, which was mostly in the rural/peri-urban districts. Our findings provide better insight into understanding the spatial distribution of Culex mosquitoes and its associated environmental risk factors. Such information can assist local clinicians and public health policymakers to identify potential areas of risk for JE virus. Risk maps can be an efficient way of raising public awareness about the virus and further preventive measures need to be considered in order to prevent outbreaks and onwards transmission of JE virus.

Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa

Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000 persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1:2 (presence/absence) were generated at buffer distances of 0.5-20 km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10 km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (ρ < 0.05) and yielded a deviance (D²) of 36%. The significantly positive relationship (ρ < 0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a₁ index) and malaria distribution in buffer distances of 0.5, 10, and 20 km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10 km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall.

Satellite-derived NDVI, LST, and climatic factors driving the distribution and abundance of Anopheles mosquitoes in a former malarious area in northwest Argentina

Distribution and abundance of disease vectors are directly related to climatic conditions and environmental changes. Remote sensing data have been used for monitoring environmental conditions influencing spatial patterns of vector-borne diseases. The aim of this study was to analyze the effect of the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic factors (temperature, humidity, wind velocity, and accumulated rainfall) on the distribution and abundance of Anopheles species in northwestern Argentina using Poisson regression analyses. Samples were collected from December, 2001 to December, 2005 at three localities, Aguas Blancas, El Oculto and San Ramón de la Nueva Orán. We collected 11,206 adult Anopheles species, with the major abundance observed at El Oculto (59.11%), followed by Aguas Blancas (22.10%) and San Ramón de la Nueva Orán (18.79%). Anopheles pseudopunctipennis was the most abundant species at El Oculto, Anopheles argyritarsis predominated in Aguas Blancas, and Anopheles strodei in San Ramón de la Nueva Orán. Samples were collected throughout the sampling period, with the highest peaks during the spring seasons. LST and mean temperature appear to be the most important variables determining the distribution patterns and major abundance of An. pseudopunctipennis and An. argyritarsis within malarious areas.

The role of environmental factors in the spatial distribution of Japanese encephalitis in mainland China

Japanese encephalitis (JE) is the most common cause of viral encephalitis and an important public health concern in the Asia-Pacific region, particularly in China where 50% of global cases are notified. To explore the association between environmental factors and human JE cases and identify the high risk areas for JE transmission in China, we used annual notified data on JE cases at the center of administrative township and environmental variables with a pixel resolution of 1 km×1 km from 2005 to 2011 to construct models using ecological niche modeling (ENM) approaches based on maximum entropy. These models were then validated by overlaying reported human JE case localities from 2006 to 2012 onto each prediction map. ENMs had good discriminatory ability with the area under the curve (AUC) of the receiver operating curve (ROC) of 0.82-0.91, and low extrinsic omission rate of 5.44-7.42%. Resulting maps showed JE being presented extensively throughout southwestern and central China, with local spatial variations in probability influenced by minimum temperatures, human population density, mean temperatures, and elevation, with contribution of 17.94%-38.37%, 15.47%-21.82%, 3.86%-21.22%, and 12.05%-16.02%, respectively. Approximately 60% of JE cases occurred in predicted high risk areas, which covered less than 6% of areas in mainland China. Our findings will help inform optimal geographical allocation of the limited resources available for JE prevention and control in China, find hidden high-risk areas, and increase the effectiveness of public health interventions against JE transmission.

Spatial modelling of rheumatoid arthritis in Turkey by geographic information systems (GIS)

We described the recent spatial distribution of rheumatoid arthritis in Turkey and assessed the role of environmental variables in this distribution. We developed an observed rheumatoid arthritis (RA) incidence grid map by using georeferenced rheumatoid arthritis case data (2011) from the centres of 81 provinces and the kriging method with a spherical variogram model in geographic information systems (GIS). We also modelled rheumatoid arthritis incidence in GIS by using complementary spatial database including the grid map layers of 14 environmental variables of Turkey. We conducted principle component analysis and multiple regression to investigate the relationships among variables and develop a model, respectively. The produced model was run in GIS to obtain a predicted (model) RA map. We tested the reliability of the model map by residual statistics and found the model map dependable. Observed and model incidence maps revealed the geographic distribution of rheumatoid arthritis cases in Turkey. The mean temperature, minimum temperature, maximum temperature, water vapour pressure, elevation, potential evapotranspiration, latitude, distance to seas, sunshine fraction, precipitation, longitude and aspect variables were found to have significant impacts on rheumatoid arthritis. Consequently, the model incidence map established a good background to predict rheumatoid arthritis cases following environmental changes.

Spatiotemporal change and ecological modelling of malaria in Turkey by means of geographic information systems

We described the spatiotemporal change of malaria (Plasmodium vivax) in Turkey over 34 years (1975-2008), and assessed the role of environmental variables in this change. We developed seven 5-year-period raster maps by using geo-referenced malaria case data from the centres of 81 provinces and the kriging method with a spherical variogram model in a geographic information systems (GIS) model. We also modelled malaria incidence in GIS by using our average malaria incidence raster map, and complementary spatial database including the raster map layers of 14 environmental variables. We chose linear regression analysis with backward method to investigate relationships among variables and develop a model. The model was run in GIS to obtain a model incidence raster map. We tested the reliability of the model map by residual statistics, and found the model map dependable. Five-year-period maps revealed that the distribution of malaria cases moved from the East Mediterranean region to the Southeast Anatolia region due to changing human activities. The latitude, minimum temperature, distance to seas and elevation variables were found to have significant impacts on malaria. Consequently, the model incidence map established a good background for early warning systems to predict epidemics of malaria following environmental changes.

Geomapping generalized eigenvalue frequency distributions for predicting prolific Aedes albopictus and Culex quinquefasciatus habitats based on spatiotemporal field-sampled count data

Marked spatiotemporal variabilities in mosquito infection of arboviruses require adaptive strategies for determining optimal field-sampling timeframes, pool screening, and data analyses. In particular, the error distribution and aggregation patterns of adult arboviral mosquitoes can vary significantly by species, which can statistically bias analyses of spatiotemporal-sampled predictor variables generating misinterpretation of prolific habitat surveillance locations. Currently, there is a lack of reliable and consistent measures of risk exposure based on field-sampled georeferenced explanatory covariates which can compromise quantitative predictions generated from arboviral mosquito surveillance models for implementing larval control strategies targeting productive habitats. In this research we used spatial statistics and QuickBird visible and near-infra-red data for determining trapping sites that were related to Culex quinquefasciatus and Aedes albopictus species abundance and distribution in Birmingham, Alabama. Initially, a Land Use Land Cover (LULC) model was constructed from multiple spatiotemporal-sampled georeferenced predictors and the QuickBird data. A Poisson regression model with a non-homogenous, gamma-distributed mean then decomposed the data into positive and negative spatial filter eigenvectors. An autoregressive process in the error term then was used to derive the sample distribution of the Moran's I statistic for determining latent autocorrelation components in the model. Spatial filter algorithms established means, variances, distributional functions, and pairwise correlations for the predictor variables. In doing so, the eigenfunction spatial filter quantified the residual autocorrelation error in the mean response term of the model as a linear combination of various distinct Cx. quinquefasciatus and Ae. albopictus habitat map patterns. The analyses revealed 18-27% redundant information in the data. Prolific habitats of Cx. quinquefasciatus and Ae. albopictus can be accurately spatially targeted based on georeferenced field-sampled count data using QuickBird data, LULC explanatory covariates, robust negative binomial regression estimates and space-time eigenfunctions.

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

Background

Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature.

Methods

Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4^® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS^® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3^®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix.

Results

By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat.

Conclusion

An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity.

Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali

BACKGROUND

The risk of Plasmodium falciparum infection is variable over space and time and this variability is related to environmental variability. Environmental factors affect the biological cycle of both vector and parasite. Despite this strong relationship, environmental effects have rarely been included in malaria transmission models.Remote sensing data on environment were incorporated into a temporal model of the transmission, to forecast the evolution of malaria epidemiology, in a locality of Sudanese savannah area.

METHODS

A dynamic cohort was constituted in June 1996 and followed up until June 2001 in the locality of Bancoumana, Mali. The 15-day composite vegetation index (NDVI), issued from satellite imagery series (NOAA) from July 1981 to December 2006, was used as remote sensing data.The statistical relationship between NDVI and incidence of P. falciparum infection was assessed by ARIMA analysis. ROC analysis provided an NDVI value for the prediction of an increase in incidence of parasitaemia.Malaria transmission was modelled using an SIRS-type model, adapted to Bancoumana's data. Environmental factors influenced vector mortality and aggressiveness, as well as length of the gonotrophic cycle. NDVI observations from 1981 to 2001 were used for the simulation of the extrinsic variable of a hidden Markov chain model. Observations from 2002 to 2006 served as external validation.

RESULTS

The seasonal pattern of P. falciparum incidence was significantly explained by NDVI, with a delay of 15 days (p = 0.001). An NDVI threshold of 0.361 (p = 0.007) provided a Diagnostic Odd Ratio (DOR) of 2.64 (CI95% [1.26;5.52]).The deterministic transmission model, with stochastic environmental factor, predicted an endemo-epidemic pattern of malaria infection. The incidences of parasitaemia were adequately modelled, using the observed NDVI as well as the NDVI simulations. Transmission pattern have been modelled and observed values were adequately predicted. The error parameters have shown the smallest values for a monthly model of environmental changes.

CONCLUSION

Remote-sensed data were coupled with field study data in order to drive a malaria transmission model. Several studies have shown that the NDVI presents significant correlations with climate variables, such as precipitations particularly in Sudanese savannah environments. Non-linear model combining environmental variables, predisposition factors and transmission pattern can be used for community level risk evaluation.

Describing Anopheles arabiensis aquatic habitats in two riceland agro-ecosystems in Mwea, Kenya using a negative binomial regression model with a non-homogenous mean

This research illustrates a geostatistical approach for modeling the spatial distribution patterns of Anopheles arabiensis Patton (Patton) aquatic habitats in two riceland environments. QuickBird 0.61 m data, encompassing the visible bands and the near-infra-red (NIR) band, were selected to synthesize images of An. arabiensis aquatic habitats. These bands and field sampled data were used to determine ecological parameters associated with riceland larval habitat development. SAS was used to calculate univariate statistics, correlations and Poisson regression models. Global autocorrelation statistics were generated in ArcGISfrom georeferenced Anopheles aquatic habitats in the study sites. The geographic distribution of Anopheles gambiae s.l. aquatic habitats in the study sites exhibited weak positive autocorrelation; similar numbers of log-larval count habitats tend to clustered in space. Individual rice land habitat data were further evaluated in terms of their covariations with spatial autocorrelation, by regressing them on candidate spatial filter eigenvectors. Each eigenvector generated from a geographically weighted matrix, for both study sites, revealed a distinctive spatial pattern. The spatial autocorrelation components suggest the presence of roughly 14-30% redundant information in the aquatic habitat larval count samples. Synthetic map pattern variables furnish a method of capturing spatial dependency effects in the mean response term in regression analyses of rice land An. arabiensis aquatic habitat data.

Contribution of different aquatic habitats to adult Anopheles arabiensis and Culex quinquefasciatus (Diptera: Culicidae) production in a rice agroecosystem in Mwea, Kenya

Studies were conducted to determine the contribution of diverse larval habitats to adult Anopheles arabiensis Patton and Culex quinquefasciatus Say production in a rice land agro-ecosystem in Mwea, Kenya. Two sizes of cages were placed in different habitat types to investigate the influence of non-mosquito invertebrates on larval mortalities and the contribution of each habitat type to mosquito productivities, respectively. These emergence traps had fine netting material covers to prevent adult mosquitoes from ovipositing in the area covered by the trap and immature mosquitoes from entering the cages. The emergence of Anopheles arabiensis in seeps, tire tracks, temporary pools, and paddies was 10.53%, 17.31%, 12.50%, and 2.14%, respectively, while the corresponding values for Cx. quinquefasciatus were 16.85% in tire tracks, 8.39% in temporary pools, and 5.65% in the paddies from 0.125 m3 cages during the study. Cages measuring 1 m3 were placed in different habitat types which included paddy, swamp, marsh, ditch, pool, and seep to determine larval habitat productivity. An. arabiensis was the predominant anopheline species (98.0%, n = 232), although a few Anopheles coustani Laveran (2.0%, n = 5) emerged from the habitats. The productivity for An. arabiensis larvae was 6.0 mosquitoes per m2 for the temporary pools, 5.5 for paddy, 5.4 for marsh, 2.7 for ditch, and 0.6 for seep. The Cx. quinquefasciatus larval habitat productivity was 47.8 mosquitoes per m2 for paddies, 35.7 for ditches, 11.1 for marshes, 4.2 for seeps, 2.4 for swamps, and 1.0 for temporary pools. Pools, paddy, and marsh habitat types were the most productive larval habitats for An. arabiensis while paddy, ditch, and marsh were the most productive larval habitats for Cx. quinquefasciatus. The most common non-mosquito invertebrate composition in the cages included Dytiscidae, Notonectidae, Belostomatidae, and Ephemerellidae, and their presence negatively affected the number of emergent mosquitoes from the cages. In conclusion, freshly formed habitats are the most productive aquatic habitats, while old and more permanent habitats are the least productive due to natural regulation of mosquito immatures.