Dataset Information

A spatio-temporal analysis to identify the drivers of malaria transmission in Bhutan.

ABSTRACT: At a time when Bhutan is on the verge of malaria elimination, the aim of this study was to identify malaria clusters at high geographical resolution and to determine its association with local environmental characteristics. Malaria cases from 2006-2014 were obtained from the Vector-borne Disease Control Program under the Ministry of Health, Bhutan. A Zero-Inflated Poisson multivariable regression model with a conditional autoregressive (CAR) prior structure was developed. Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling was used to estimate posterior parameters. A total of 2,062?Plasmodium falciparum and 2,284?Plasmodium vivax cases were reported during the study period. Both species of malaria showed seasonal peaks with decreasing trend. Gender and age were not associated with the transmission of either species of malaria. P. falciparum increased by 0.7% (95% CrI: 0.3%, 0.9%) for a one mm increase in rainfall, while climatic variables (temperature and rainfall) were not associated with P. vivax. Insecticide treated bed net use and residual indoor insecticide coverage were unaccounted for in this study. Hot spots and clusters of both species were isolated in the central southern part of Bhutan bordering India. There was significant residual spatial clustering after accounting for climate and demographic variables.

SUBMITTER: Wangdi K

PROVIDER: S-EPMC7184595 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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A spatio-temporal analysis to identify the drivers of malaria transmission in Bhutan.

Wangdi Kinley K Xu Zhijing Z Suwannatrai Apiporn T AT Kurscheid Johanna J Lal Aparna A Namgay Rinzin R Glass Kathryn K Gray Darren J DJ Clements Archie C A ACA

Scientific reports 20200427 1

At a time when Bhutan is on the verge of malaria elimination, the aim of this study was to identify malaria clusters at high geographical resolution and to determine its association with local environmental characteristics. Malaria cases from 2006-2014 were obtained from the Vector-borne Disease Control Program under the Ministry of Health, Bhutan. A Zero-Inflated Poisson multivariable regression model with a conditional autoregressive (CAR) prior structure was developed. Bayesian Markov chain M ...[more]

PMID: 32341415

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Project description:Knowledge of how malaria infections spread locally is important both for the design of targeted interventions aiming to interrupt malaria transmission and the design of trials to assess the interventions. A previous analysis of 1602 genotyped Plasmodium falciparum parasites in Kilifi, Kenya collected over 12 years found an interaction between time and geographic distance: the mean number of single nucleotide polymorphism (SNP) differences was lower for pairs of infections which were both a shorter time interval and shorter geographic distance apart. We determine whether the empiric pattern could be reproduced by a simple model, and what mean geographic distances between parent and offspring infections and hypotheses about genotype-specific immunity or a limit on the number of infections would be consistent with the data. We developed an individual-based stochastic simulation model of households, people and infections. We parameterized the model for the total number of infections, and population and household density observed in Kilifi. The acquisition of new infections, mutation, recombination, geographic location and clearance were included. We fit the model to the observed numbers of SNP differences between pairs of parasite genotypes. The patterns observed in the empiric data could be reproduced. Although we cannot rule out genotype-specific immunity or a limit on the number of infections per individual, they are not necessary to account for the observed patterns. The mean geographic distance between parent and offspring malaria infections for the base model was 0.5?km (95% CI 0.3-1.5), for a distribution with 68% of distances shorter than the mean. Very short mean distances did not fit well, but mixtures of distributions were also consistent with the data. For a pathogen which undergoes meiosis in a setting with moderate transmission and a low coverage of infections, analytic methods are limited but an individual-based model can be used with genotyping data to estimate parameter values and investigate hypotheses about underlying processes.

Project description:BACKGROUND:Malaria incidence is largely influenced by vector abundance. Among the many interconnected factors relating to malaria transmission, weather conditions such as rainfall and temperature are known to create suitable environmental conditions that sustain reproduction and propagation of anopheles mosquitoes and malaria parasites. In Ghana, climatic conditions vary across the country. Understanding the heterogeneity of malaria morbidity using data sourced from a recently setup data repository for routine health facility data could support planning. METHODS:Monthly aggregated confirmed uncomplicated malaria cases from the District Health Information Management System and average monthly rainfall and temperature records obtained from the Ghana Meteorological Agency from 2008 to 2016 were analysed. Univariate time series models were fitted to the malaria, rainfall and temperature data series. After pre-whitening the morbidity data, cross correlation analyses were performed. Subsequently, transfer function models were developed for the relationship between malaria morbidity and rainfall and temperature. RESULTS:Malaria morbidity patterns vary across zones. In the Guinea savannah, morbidity peaks once in the year and twice in both the Transitional forest and Coastal savannah, following similar patterns of rainfall at the zonal level. While the effects of rainfall on malaria morbidity are delayed by a month in the Guinea savannah and Transitional Forest zones those of temperature are delayed by two months in the Transitional forest zone. In the Coastal savannah however, incidence of malaria is significantly associated with two months lead in rainfall and temperature. CONCLUSION:Data captured on the District Health Information Management System has been used to demonstrate heterogeneity in the dynamics of malaria morbidity across the country. Timing of these variations could guide the deployment of interventions such as indoor residual spraying, Seasonal Malaria Chemoprevention or vaccines to optimise effectiveness on zonal basis.

Project description:BackgroundThe global decline of malaria burden and goals for elimination has led to an increased interest in the fine-scale epidemiology of malaria. Micro-geographic heterogeneity of malaria infection could have implications for designing targeted small-area interventions.MethodsTwo-year longitudinal cohort study data were used to explore the spatial and spatio-temporal distribution of malaria episodes in 2040 children aged < 10 years in 16 villages near the Gilgel-Gibe hydropower dam in Southwest Ethiopia. All selected households (HHs) were geo-referenced, and children were followed up through weekly house-to-house visits for two consecutive years to identify febrile episodes of P. falciparum and P. vivax infections. After confirming the spatial dependence of malaria episodes with Ripley's K function, SatScanTM was used to identify purely spatial and space-time clusters (hotspots) of annual malaria incidence for 2 years follow-up: year 1 (July 2008-June 2009) and year 2 (July 2009-June 2010).ResultsIn total, 685 P. falciparum episodes (in 492 HHs) and 385 P. vivax episodes (in 290 HHs) were identified, representing respectively incidence rates of 14.6 (95% CI: 13.4-15.6) and 8.2 (95% CI: 7.3-9.1) per 1000 child-months at risk. In year 1, the most likely (128 HHs with 63 episodes, RR = 2.1) and secondary (15 HHs with 12 episodes, RR = 5.31) clusters of P. vivax incidence were found respectively in southern and north-western villages; while in year 2, the most likely cluster was located only in north-western villages (85 HHs with 16 episodes, RR = 4.4). Instead, most likely spatial clusters of P. falciparum incidence were consistently located in villages south of the dam in both years: year 1 (167 HHs with 81 episodes, RR = 1.8) and year 2 (133 HHs with 67 episodes, RR = 2.2). Space-time clusters in southern villages for P. vivax were found in August-November 2008 in year 1 and between November 2009 and February 2010 in year 2; while for P. falciparum, they were found in September-November 2008 in year 1 and October-November 2009 in year 2.ConclusionHotspots of P. falciparum incidence in children were more stable at the geographical level and over time compared to those of P. vivax incidence during the study period.

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A spatio-temporal analysis to identify the drivers of malaria transmission in Bhutan.

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A spatio-temporal analysis to identify the drivers of malaria transmission in Bhutan.

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