Models for short term malaria prediction in Sri Lanka

<p>Abstract</p> <p>Background</p> <p>Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control.</p> <p>Methods</p> <p>Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models.</p> <p>Results</p> <p>The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons.</p> <p>Conclusion</p> <p>Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed.</p

Geographic Coincidence of Increased Malaria Transmission Hazard and Vulnerability Occurring at the Periphery of two Tanzanian Villages.

The goal of malaria elimination necessitates an improved understanding of any fine-scale geographic variations in transmission risk so that complementary vector control tools can be integrated into current vector control programmes as supplementary measures that are spatially targeted to maximize impact upon residual transmission. This study examines the distribution of host-seeking malaria vectors at households within two villages in rural Tanzania. Host-seeking mosquitoes were sampled from 72 randomly selected households in two villages on a monthly basis throughout 2008 using CDC light-traps placed beside occupied nets. Spatial autocorrelation in the dataset was examined using the Moran's I statistic and the location of any clusters was identified using the Getis-Ord Gi* statistic. Statistical associations between the household characteristics and clusters of mosquitoes were assessed using a generalized linear model for each species. For both Anopheles gambiae sensu lato and Anopheles funestus, the density of host-seeking females was spatially autocorrelated, or clustered. For both species, houses with low densities were clustered in the semi-urban village centre while houses with high densities were clustered in the periphery of the villages. Clusters of houses with low or high densities of An. gambiae s.l. were influenced by the number of residents in nearby houses. The occurrence of high-density clusters of An. gambiae s.l. was associated with lower elevations while An. funestus was also associated with higher elevations. Distance from the village centre was also positively correlated with the number of household occupants and having houses constructed with open eaves. The results of the current study highlight that complementary vector control tools could be most effectively targeted to the periphery of villages where the households potentially have a higher hazard (mosquito densities) and vulnerability (open eaves and larger households) to malaria infection

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined

Topography-derived wetness indices are associated with household-level malaria risk in two communities in the western Kenyan highlands

<p>Abstract</p> <p>Background</p> <p>Transmission of <it>Plasmodium falciparum </it>generally decreases with increasing elevation, in part because lower temperature slows the development of both parasites and mosquitoes. However, other aspects of the terrain, such as the shape of the land, may affect habitat suitability for <it>Anopheles </it>breeding and thus risk of malaria transmission. Understanding these local topographic effects may permit prediction of regions at high risk of malaria within the highlands at small spatial scales.</p> <p>Methods</p> <p>Hydrologic modelling techniques were adapted to predict the flow of water across the landscape surrounding households in two communities in the western Kenyan highlands. These surface analyses were used to generate indices describing predicted water accumulation in regions surrounding the study area. Households with and without malaria were compared for their proximity to regions of high and low predicted wetness. Predicted wetness and elevation variables were entered into bivariate and multivariate regression models to examine whether significant associations with malaria were observable at small spatial scales.</p> <p>Results</p> <p>On average, malaria case households (n = 423) were located 280 m closer to regions with very high wetness indices than non-malaria "control" households (n = 895) (t = 10.35, p < 0.0001). Distance to high wetness indices remained an independent predictor of risk after controlling for household elevation in multivariate regression (OR = 0.93 [95% confidence interval = 0.89–0.96] for a 100 m increase in distance). For every 10 m increase in household elevation, there was a 12% decrease in the odds of the house having a malaria case (OR = 0.88 [0.85–0.90]). However, after controlling for distance to regions of high predicted wetness and the community in which the house was located, this reduction in malaria risk was not statistically significant (OR = 0.98 [0.94–1.03]).</p> <p>Conclusion</p> <p>Proximity to terrain with high predicted water accumulation was significantly and consistently associated with increased household-level malaria incidence, even at small spatial scales with little variation in elevation variables. These results suggest that high wetness indices are not merely proxies for valley bottoms, and hydrologic flow models may prove valuable for predicting areas of high malaria risk in highland regions. Application in areas where malaria surveillance is limited could identify households at higher risk and help focus interventions.</p

Malaria risk factors in north-east Tanzania

BACKGROUND: Understanding the factors which determine a household's or individual's risk of malaria infection is important for targeting control interventions at all intensities of transmission. Malaria ecology in Tanzania appears to have reduced over recent years. This study investigated potential risk factors and clustering in face of changing infection dynamics. METHODS: Household survey data were collected in villages of rural Muheza district. Children aged between six months and thirteen years were tested for presence of malaria parasites using microscopy. A multivariable logistic regression model was constructed to identify significant risk factors for children. Geographical information systems combined with global positioning data and spatial scan statistic analysis were used to identify clusters of malaria. RESULTS: Using an insecticide-treated mosquito net of any type proved to be highly protective against malaria (OR 0.75, 95% CI 0.59-0.96). Children aged five to thirteen years were at higher risk of having malaria than those aged under five years (OR 1.71, 95% CI 1.01-2.91). The odds of malaria were less for females when compared to males (OR 0.62, 95% CI 0.39-0.98). Two spatial clusters of significantly increased malaria risk were identified in two out of five villages. CONCLUSIONS: This study provides evidence that recent declines in malaria transmission and prevalence may shift the age groups at risk of malaria infection to older children. Risk factor analysis provides support for universal coverage and targeting of long-lasting insecticide-treated nets (LLINs) to all age groups. Clustering of cases indicates heterogeneity of risk. Improved targeting of LLINs or additional supplementary control interventions to high risk clusters may improve outcomes and efficiency as malaria transmission continues to fall under intensified control

The Actin-Binding Protein Capulet Genetically Interacts with the Microtubule Motor Kinesin to Maintain Neuronal Dendrite Homeostasis

BACKGROUND: Neurons require precise cytoskeletal regulation within neurites, containing microtubule tracks for cargo transport in axons and dendrites or within synapses containing organized actin. Due to the unique architecture and specialized function of neurons, neurons are particularly susceptible to perturbation of the cytoskeleton. Numerous actin-binding proteins help maintain proper cytoskeletal regulation. METHODOLOGY/PRINCIPAL FINDINGS: From a Drosophila forward genetic screen, we identified a mutation in capulet--encoding a conserved actin-binding protein--that causes abnormal aggregates of actin within dendrites. Through interaction studies, we demonstrate that simultaneous genetic inactivation of capulet and kinesin heavy chain, a microtubule motor protein, produces elongate cofilin-actin rods within dendrites but not axons. These rods resemble actin-rich structures induced in both mammalian neurodegenerative and Drosophila Alzheimer's models, but have not previously been identified by loss of function mutations in vivo. We further demonstrate that mitochondria, which are transported by Kinesin, have impaired distribution along dendrites in a capulet mutant. While Capulet and Cofilin may biochemically cooperate in certain circumstances, in neuronal dendrites they genetically antagonize each other. CONCLUSIONS/SIGNIFICANCE: The present study is the first molecularly defined loss of function demonstration of actin-cofilin rods in vivo. This study suggests that simultaneous, seemingly minor perturbations in neuronal dendrites can synergize producing severe abnormalities affecting actin, microtubules and mitochondria/energy availability in dendrites. Additionally, as >90% of Alzheimer's and Parkinson's cases are sporadic this study suggests mechanisms by which multiple mutations together may contribute to neurodegeneration instead of reliance on single mutations to produce disease

A Functional Misexpression Screen Uncovers a Role for Enabled in Progressive Neurodegeneration

Drosophila is a well-established model to study the molecular basis of neurodegenerative diseases. We carried out a misexpression screen to identify genes involved in neurodegeneration examining locomotor behavior in young and aged flies. We hypothesized that a progressive loss of rhythmic activity could reveal novel genes involved in neurodegenerative mechanisms. One of the interesting candidates showing progressive arrhythmicity has reduced enabled (ena) levels. ena down-regulation gave rise to progressive vacuolization in specific regions of the adult brain. Abnormal staining of pre-synaptic markers such as cystein string protein (CSP) suggest that axonal transport could underlie the neurodegeneration observed in the mutant. Reduced ena levels correlated with increased apoptosis, which could be rescued in the presence of p35, a general Caspase inhibitor. Thus, this mutant recapitulates two important features of human neurodegenerative diseases, i.e., vulnerability of certain neuronal populations and progressive degeneration, offering a unique scenario in which to unravel the specific mechanisms in an easily tractable organism

Identification of Neural Outgrowth Genes using Genome-Wide RNAi

While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. To address this, we developed methods to screen the Drosophila genome using RNA-interference (RNAi) on primary neural cells and present the results of the first full-genome RNAi screen in neurons. We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown. From the full genome screen, we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi, have morphological defects such as reduced axon extension, excessive branching, loss of fasciculation, and blebbing. To assist in the phenotypic analysis of the large data sets, we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes. The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons. Our analysis revealed unexpected, essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules, enzymes, channels, receptors, and cytoskeletal proteins. We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes. We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons, respectively. Collectively, our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes, and the screening technique can be used to identify many new genes that have important functions in the nervous system