Remote sensing of environmental risk factors for malaria in different geographic contexts

Bayesian spatio-temporal modelling of environmental, climatic, and socio-economic influences on malaria in Central Vietnam

BACKGROUND

Despite the successful efforts in controlling malaria in Vietnam, the disease remains a significant health concern, particularly in Central Vietnam. This study aimed to assess correlations between environmental, climatic, and socio-economic factors in the district with malaria cases.

METHODS

The study was conducted in 15 provinces in Central Vietnam from January 2018 to December 2022. Monthly malaria cases were obtained from the Institute of Malariology, Parasitology, and Entomology Quy Nhon, Vietnam. Environmental, climatic, and socio-economic data were retrieved using a Google Earth Engine script. A multivariable Zero-inflated Poisson regression was undertaken using a Bayesian framework with spatial and spatiotemporal random effects with a conditional autoregressive prior structure. The posterior random effects were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling.

RESULTS

There was a total of 5,985 Plasmodium falciparum and 2,623 Plasmodium vivax cases during the study period. Plasmodium falciparum risk increased by five times (95% credible interval [CrI] 4.37, 6.74) for each 1-unit increase of normalized difference vegetation index (NDVI) without lag and by 8% (95% CrI 7%, 9%) for every 1ºC increase in maximum temperature (TMAX) at a 6-month lag. While a decrease in risk of 1% (95% CrI 0%, 1%) for a 1 mm increase in precipitation with a 6-month lag was observed. A 1-unit increase in NDVI at a 1-month lag was associated with a four-fold increase (95% CrI 2.95, 4.90) in risk of P. vivax. In addition, the risk increased by 6% (95% CrI 5%, 7%) and 3% (95% CrI 1%, 5%) for each 1ºC increase in land surface temperature during daytime with a 6-month lag and TMAX at a 4-month lag, respectively. Spatial analysis showed a higher mean malaria risk of both species in the Central Highlands and southeast parts of Central Vietnam and a lower risk in the northern and north-western areas.

CONCLUSION

Identification of environmental, climatic, and socio-economic risk factors and spatial malaria clusters are crucial for designing adaptive strategies to maximize the impact of limited public health resources toward eliminating malaria in Vietnam.

Malaria risk mapping among children under five in Togo

Malaria is a major health threat in sub-Sahara Africa, especially for children under five. However, there is considerable heterogeneity between areas in malaria risk reported, associated with environmental and climatic. We used data from Togo to explore spatial patterns of malaria incidence. Geospatial covariate datasets, including climatic and environmental variables from the 2017 Malaria Indicator Survey in Togo, were used for this study. The association between malaria incidence and ecological predictors was assessed using three regression techniques, namely the Ordinary Least Squares (OLS), spatial lag model (SLM), and spatial error model (SEM). A total of 171 clusters were included in the survey and provided data on environmental and climate variables. Spatial autocorrelation showed that the distribution of malaria incidence was not random and revealed significant spatial clustering. Mean temperature, precipitation, aridity and proximity to water bodies showed a significant and direct association with malaria incidence rate in the SLM model, which best fitted the data according to AIC. Five malaria incidence hotspots were identified. Malaria incidence is spatially clustered in Togo associated with climatic and environmental factors. The results can contribute to the development of specific malaria control plans taking geographical variation into consideration and targeting transmission hotspots.

Forecasting deep learning-based risk assessment of vector-borne diseases using hybrid methodology

BACKGROUND

Dengue fever is rapidly becoming Malaysia's most pressing health concern, as the reported cases have nearly doubled over the past decade. Without efficacious antiviral medications, vector control remains the primary strategy for battling dengue, while the recently introduced tetravalent immunization is being evaluated. The most significant and dangerous risk increasing recently is vector-borne illnesses. These illnesses induce significant human sickness and are transmitted by blood-feeding arthropods such as fleas, parasites, and mosquitos. A thorough grasp of various factors is necessary to improve prediction accuracy and typically generate inaccurate and unstable predictions, as well as machine learning (ML) models, weather-driven mechanisms, and numerical time series.

OBJECTIVE

In this research, we propose a novel method for forecasting vector-borne disease risk using Radial Basis Function Networks (RBFNs) and the Darts Game Optimizer (DGO) algorithm.

METHODS

The proposed approach entails training the RBFNs with historical disease data and enhancing their parameters with the DGO algorithm. To prepare the RBFNs, we used a massive dataset of vector-borne disease incidences, climate variables, and geographical data. The DGO algorithm proficiently searches the RBFN parameter space, fine-tuning the model's architecture to increase forecast accuracy.

RESULTS

RBFN-DGO provides a potential method for predicting vector-borne disease risk. This study advances predictive demonstrating in public health by shedding light on effectively controlling vector-borne diseases to protect human populations. We conducted extensive testing to evaluate the performance of the proposed method to standard optimization methods and alternative forecasting methods.

CONCLUSION

According to the findings, the RBFN-DGO model beats others in terms of accuracy and robustness in predicting the likelihood of vector-borne illness occurrences.

Factors associated with malaria infection among children after distribution of PBO-pyrethroid synergist-treated nets and indoor residual spraying in north-western Tanzania

BACKGROUND

After a decade of successful control, malaria is on the rise again. The prevalence of malaria in Tanzania has increased from 7% in 2017 to 8% in 2022 and reached 18% in Kagera region in the North West of Tanzania. Malaria vectors in Muleba district Kagera have high level of pyrethroid resistance. The aim of this paper is to explore factors associated with malaria infection prevalence in children aged 6 months to 14 years in Muleba, where Long Lasting Insecticidal Net (LLIN) combining a pyrethroid insecticide and synergist piperonyl butoxide (PBO) that counteract resistance in the mosquitoes, was first distributed under trial conditions in 2015.

METHODS

The trial was a community randomized control in which there were two malaria prevalence cross-sectional household surveys each year (June and December) from 2015 to 2017 in Muleba. In this study we conducted a secondary data analysis of the December surveys only. Multilevel Poisson regression analysis was used to assess factors associated with malaria infection.

RESULTS

A total of 10,941 children and 4,611 households were included in this study. Overall malaria prevalence was 35.8%, 53.3% and 54.4% in the year 2015, 2016 and 2017 respectively. Living in an area with standard LLIN as opposed to the novel PBO synergist LLIN, being a male child, above 5 years of age, living in a house with open eaves, living in house without IRS, having head of household with no formal education, lower socioeconomic status and survey year were associated with increased risk of malaria infection.

CONCLUSIONS

Using PBO LLIN reduced the risk of malaria infection. However, additional measures could further reduce malaria infection in areas of insecticide resistance such as housing improvement.

Spatial analyses of Plasmodium knowlesi vectors with reference to control interventions in Malaysia

BACKGROUND

Malaria parasites such as Plasmodium knowlesi, P. inui, and P. cynomolgi are spread from macaques to humans through the Leucosphyrus Group of Anopheles mosquitoes. It is crucial to know the distribution of these vectors to implement effective control measures for malaria elimination. Plasmodium knowlesi is the most predominant zoonotic malaria parasite infecting humans in Malaysia.

METHODS

Vector data from various sources were used to create distribution maps from 1957 to 2021. A predictive statistical model utilizing logistic regression was developed using significant environmental factors. Interpolation maps were created using the inverse distance weighted (IDW) method and overlaid with the corresponding environmental variables.

RESULTS

Based on the IDW analysis, high vector abundances were found in the southwestern part of Sarawak, the northern region of Pahang and the northwestern part of Sabah. However, most parts of Johor, Sabah, Perlis, Penang, Kelantan and Terengganu had low vector abundance. The accuracy test indicated that the model predicted sampling and non-sampling areas with 75.3% overall accuracy. The selected environmental variables were entered into the regression model based on their significant values. In addition to the presence of water bodies, elevation, temperature, forest loss and forest cover were included in the final model since these were significantly correlated. Anopheles mosquitoes were mainly distributed in Peninsular Malaysia (Titiwangsa range, central and northern parts), Sabah (Kudat, West Coast, Interior and Tawau division) and Sarawak (Kapit, Miri, and Limbang). The predicted Anopheles mosquito density was lower in the southern part of Peninsular Malaysia, the Sandakan Division of Sabah and the western region of Sarawak.

CONCLUSION

The study offers insight into the distribution of the Leucosphyrus Group of Anopheles mosquitoes in Malaysia. Additionally, the accompanying predictive vector map correlates well with cases of P. knowlesi malaria. This research is crucial in informing and supporting future efforts by healthcare professionals to develop effective malaria control interventions.

Predicting Continental Scale Malaria With Land Surface Water Predictors Based on Malaria Dispersal Mechanisms and High-Resolution Earth Observation Data

Despite malaria prevalence being linked to surface water through vector breeding, spatial malaria predictors representing surface water often predict malaria poorly. Furthermore, precipitation, which precursors surface water, often performs better. Our goal is to determine whether novel surface water exposure indices that take malaria dispersal mechanisms into account, derived from new high-resolution surface water data, can be stronger predictors of malaria prevalence compared to precipitation. One hundred eighty candidate predictors were created by combining three surface water malaria exposures from high-accuracy and resolution (5 m resolution, overall accuracy 96%, Kappa Coefficient 0.89, Commission and Omission error 3% and 13%, respectively) water maps of East Africa. Through variable contribution analysis a subset of strong predictors was selected and used as input for Boosted Regression Tree models. We benchmarked the performance and Relative Contribution of this set of novel predictors to models using precipitation instead of surface water predictors, alternative lower resolution predictors, and simpler surface water predictors used in previous studies. The predictive performance of the novel indices rivaled or surpassed that of precipitation predictors. The novel indices substantially improved performance over the identical set of predictors derived from the lower resolution Joint Research Center surface water data set (+10% R 2, +17% Relative Contribution) and over the set of simpler predictors (+18% R 2, +30% Relative Contribution). Surface water derived indices can be strong predictors of malaria, if the spatial resolution is sufficiently high to detect small waterbodies and dispersal mechanisms of malaria related to surface water in human and vector water exposure assessment are incorporated.

Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda

As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

The diseases transmitted through vectors such as mosquitoes are named vector-borne diseases (VBDs), such as malaria, dengue, and leishmaniasis. Malaria spreads by a vector named Anopheles mosquitos. Dengue is transmitted through the bite of the female vector Aedes aegypti or Aedes albopictus mosquito. The female Phlebotomine sandfly is the vector that transmits leishmaniasis. The best way to control VBDs is to identify breeding sites for their vectors. This can be efficiently accomplished by the Geographical Information System (GIS). The objective was to find the relation between climatic factors (temperature, humidity, and precipitation) to identify breeding sites for these vectors. Our data contained imbalance classes, so data oversampling of different sizes was created. The machine learning models used were Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron for model training. Their results were compared and analyzed to select the best model for disease prediction in Punjab, Pakistan. Random Forest was the selected model with 93.97% accuracy. Accuracy was measured using an F score, precision, or recall. Temperature, precipitation, and specific humidity significantly affect the spread of dengue, malaria, and leishmaniasis. A user-friendly web-based GIS platform was also developed for concerned citizens and policymakers.

Malaria among under-five children in Ethiopia: a systematic review and meta-analysis

BACKGROUND

Globally, malaria is among the leading cause of under-five mortality and morbidity. Despite various malaria elimination strategies being implemented in the last decades, malaria remains a major public health concern, particularly in tropical and sub-tropical regions. Furthermore, there have been limited and inconclusive studies in Ethiopia to generate information for action towards malaria in under-five children. Additionally, there is a considerable disparity between the results of the existing studies. Therefore, the pooled estimate from this study will provide a more conclusive result to take evidence-based interventional measures against under-five malaria.

METHODS

The protocol of this review is registered at PROSPERO with registration number CRD42020157886. All appropriate databases and grey literature were searched to find relevant articles. Studies reporting the prevalence or risk factors of malaria among under-five children were included. The quality of each study was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Data was extracted using Microsoft Excel 2016 and analysis was done using STATA 16.0 statistical software. The pooled prevalence and its associated factors of malaria were determined using a random effect model. Heterogeneity between studies was assessed using the Cochrane Q-test statistics and I2 test. Furthermore, publication bias was checked by the visual inspection of the funnel plot and using Egger's and Begg's statistical tests.

RESULTS

Twelve studies with 34,842 under-five children were included. The pooled prevalence of under-five malaria was 22.03% (95% CI 12.25%, 31.80%). Lack of insecticide-treated mosquito net utilization (AOR: 5.67, 95% CI 3.6, 7.74), poor knowledge of child caretakers towards malaria transmission (AOR: 2.79, 95% CI 1.70, 3.89), and living near mosquito breeding sites (AOR: 5.05, 95% CI 2.92, 7.19) were risk factors of under-five malaria.

CONCLUSION

More than one in five children aged under five years were infected with malaria. This suggests the rate of under-five malaria is far off the 2030 national malaria elimination programme of Ethiopia. The Government should strengthen malaria control strategies such as disseminating insecticide-treated mosquito nets (ITNs), advocating the utilization of ITNs, and raising community awareness regarding malaria transmission.

Malaria Transmission Intensity Likely Modifies RTS, S/AS01 Efficacy Due to a Rebound Effect in Ghana, Malawi, and Gabon

BACKGROUND

RTS,S/AS01 is the first malaria vaccine to be approved and recommended for widespread implementation by the World Health Organization (WHO). Trials reported lower vaccine efficacies in higher-incidence sites, potentially due to a "rebound" in malaria cases in vaccinated children. When naturally acquired protection in the control group rises and vaccine protection in the vaccinated wanes concurrently, malaria incidence can become greater in the vaccinated than in the control group, resulting in negative vaccine efficacies.

METHODS

Using data from the 2009-2014 phase III trial (NCT00866619) in Lilongwe, Malawi; Kintampo, Ghana; and Lambaréné, Gabon, we evaluate this hypothesis by estimating malaria incidence in each vaccine group over time and in varying transmission settings. After estimating transmission intensities using ecological variables, we fit models with 3-way interactions between vaccination, time, and transmission intensity.

RESULTS

Over time, incidence decreased in the control group and increased in the vaccine group. Three-dose efficacy in the lowest-transmission-intensity group (0.25 cases per person-year [CPPY]) decreased from 88.2% to 15.0% over 4.5 years, compared with 81.6% to -27.7% in the highest-transmission-intensity group (3 CPPY).

CONCLUSIONS

These findings suggest that interventions, including the fourth RTS,S dose, that protect vaccinated individuals during the potential rebound period should be implemented for high-transmission settings.

Malaria and Helminthic Co-Infection during Pregnancy in Sub-Saharan Africa: A Systematic Review and Meta-Analysis

Malaria and helminthic co-infection during pregnancy causes fetomaternal haemorrhage and foetal growth retardation. This study determined the pooled burden of pregnancy malaria and helminthic co-infection in sub-Saharan Africa. CINAHL, EMBASE, Google Scholar, Scopus, PubMed, and Web of Science databases were used to retrieve data from the literature, without restricting language and publication year. The Joanna Briggs Institute's critical appraisal tool for prevalence studies was used for quality assessment. STATA Version 14.0 was used to conduct the meta-analysis. The I2 statistics and Egger's test were used to test heterogeneity and publication bias. The random-effects model was used to estimate the pooled prevalence at a 95% confidence interval (CI). The review protocol has been registered in PROSPERO, with the number CRD42019144812. In total, 24 studies (n = 14,087 participants) were identified in this study. The pooled analysis revealed that 20% of pregnant women were co-infected by malaria and helminths in sub-Saharan Africa. The pooled prevalence of malaria and helminths were 33% and 35%, respectively. The most prevalent helminths were Hookworm (48%), Ascaris lumbricoides (37%), and Trichuris trichiura (15%). Significantly higher malaria and helminthic co-infection during pregnancy were observed. Health systems in sub-Saharan Africa must implement home-grown innovative solutions to underpin context-specific policies for the early initiation of effective intermittent preventive therapy.