Forecasting paediatric malaria admissions on the Kenya Coast using rainfall

Projecting Malaria Incidence Based on Climate Change Modeling Approach: A Systematic Review

BACKGROUND: Climate change will affect the transmission of malaria by shifting the geographical space of the vector. AIM: The review aims to examine the climate change modeling approach and climatic variables used for malaria projection. METHODS: Articles were systematically searched from four databases, Scopus, Web of Science, PubMed, and SAGE. The PICO concept was used for formulation search and PRISMA approach to identify the final articles. RESULTS: A total of 27 articles were retrieved and reviewed. There were six climate factors identified in this review: Temperature, rainfall/precipitation, humidity, wind, solar radiation, and climate change scenarios. Modeling approaches used to project future malarial trend includes mathematical and computational approach. CONCLUSION: This review provides robust evidence of an association between the impact of climate change and malaria incidence. Prediction on seasonal patterns would be useful for malaria surveillance in public health prevention and mitigation strategies.

A space-time analysis of recurrent malnutrition-related hospitalisations in Kilifi, Kenya for children under-5 years

Background

Globally, malnutrition underlies 45% of under-5 s mortality, mainly from potentiating common infections such as diarrhoea and pneumonia. Malnutrition as a public health problem is not evenly disbursed because of disparities in food insecurity and health, and children commonly suffer recurrent episodes of opportunistic infections. We aimed to understand better the spatial and temporal structure of multiple paediatric hospital admissions associated with malnutrition-related illnesses. This paper aimed to investigate the spatial-temporal variations in malnutrition-related recurrent morbidity of children under-5 years from the Kilifi County in Kenya between 2002 and 2015.

Methods

The study included data from children under-5 years old who had more than one admission to a rural district hospital in Kenya within the Kilifi Health and Demographic Surveillance System (KHDSS). The primary outcome was a malnutrition-related admission, based on wasting (WHZ < -2) or nutritional oedema. Individual, household and environmental level covariates were examined as exposures. We first fitted a SARIMA model for the temporality, and the Moran’s Index affirmed spatial clustering in malnutrition admissions. Kulldorf Statistics using SaTScan were applied to detect hotspots. Then, bivariate analysis was done using repeated values tabulation and analysis of covariance (ANCOVA). Inferential analysis was done using a mixed effect multivariable negative-binomial regression model, adjusting for spatiotemporal random effects.

Results

A total of 2821 children were admitted more than once, giving a total of 6375 admissions. Of these 6375 admissions, 1866 were malnutrition-related, and 3.9% (109/2821) of the children with repeat admissions died. There was a seasonal pattern of re-admissions, peaking from May to July over the years. Hotspots were found in both the Northern and Southern areas of the KHDSS, while the areas near Kilifi Town were least affected. We found that disease severity was most likely associated with a malnutrition re-admission to the hospital.

Conclusion

Disease severity was strongly associated with admission with malnutrition but its effect reduced after adjusting for the spatial and temporal random effects. Adjusting for clustering in space and in time (spatial-temporal) in models helps to improve the understanding of recurrent hospitalisations involving malnutrition.

Electronic supplementary material

The online version of this article (10.1186/s40795-019-0296-5) contains supplementary material, which is available to authorized users.

A model comparison algorithm for increased forecast accuracy of dengue fever incidence in Singapore and the auxiliary role of total precipitation information

Many time-series models for disease counts utilise information from environmental variables. We focus on weekly dengue fever (DF) incidence rates in Singapore and demonstrate the strong negative correlation between an appropriately time-lagged total weekly rainfall and DF incidence. A Bayesian neural network time-series model for predicting DF incidence which utilizes rainfall data is introduced. A comparison is made between this neural network model and a time-series model which does not use any covariate information. An easily implementable method for choosing between the models which optimizes future prediction accuracy is suggested as well. We note that our proposed comparison method is applicable to any competing time-series models. This algorithm is demonstrated through examples of comparisons between pairs of different time-series models.

Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case Study of Uganda

Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. However quantitative rainfall prediction is normally a challenge and for this reason, this study was conducted with an aim of improving rainfall prediction using ensemble methods. It first assessed the performance of six convective schemes (Kain–Fritsch (KF); Betts–Miller–Janjić (BMJ); Grell–Fretas (GF); Grell 3D ensemble (G3); New–Tiedke (NT) and Grell–Devenyi (GD)) using the root mean square error (RMSE) and mean error (ME) focusing on the March–May 2013 rainfall period over Uganda. 18 ensemble members were then generated from the three best performing convective schemes (i.e., KF, GF and G3). The daily rainfall predicted by the three ensemble methods (i.e., ensemble mean (ENS); ensemble mean analogue (EMA) and multi–member analogue ensemble (MAEM)) was then compared with the observed daily rainfall and the RMSE and ME computed. The results shows that the ENS presented a smaller RMSE compared to individual schemes (ENS: 10.02; KF: 23.96; BMJ: 26.04; GF: 25.85; G3: 24.07; NT: 29.13 and GD: 26.27) and a better bias (ENS: −1.28; KF: −1.62; BMJ: −4.04; GF: −3.90; G3: −3.62; NT: −5.41 and GD: −4.07). The EMA and MAEM presented 13 out of 21 stations and 17 out of 21 stations respectively with smaller RMSE compared to ENS thus demonstrating additional improvement in predictive performance. This study proposed and described MAEM and found it producing comparatively better quantitative rainfall prediction performance compared to the other ensemble methods used. The MAEM method should be valid regardless the nature of the rainfall season.

Overlap Extension Barcoding for the Next Generation Sequencing and Genotyping of Plasmodium falciparum in Individual Patients in Western Kenya

Large-scale molecular epidemiologic studies of Plasmodium falciparum parasites have provided insights into parasite biology and transmission, can identify the spread of drug resistance, and are useful in assessing vaccine targets. The polyclonal nature infections in high transmission settings is problematic for traditional genotyping approaches. Next-generation sequencing (NGS) approaches to parasite genotyping allow sensitive detection of minority variants, disaggregation of complex parasite mixtures, and scalable processing of large samples sets. Therefore, we designed, validated, and applied to field parasites an approach that leverages sequencing of individually barcoded samples in a multiplex manner. We utilize variant barcodes, invariant linker sequences and modular template-specific primers to allow for the simultaneous generation of high-dimensional sequencing data of multiple gene targets. This modularity permits a cost-effective and reproducible way to query many genes at once. In mixtures of reference parasite genomes, we quantitatively detected unique haplotypes comprising as little as 2% of a polyclonal infection. We applied this genotyping approach to field-collected parasites collected in Western Kenya in order to simultaneously obtain parasites genotypes at three unlinked loci. In summary, we present a rapid, scalable, and flexible method for genotyping individual parasites that enables molecular epidemiologic studies of parasite evolution, population structure and transmission.