The Dynamics of Transmission and Spatial Distribution of Malaria in Riverside Areas of Porto Velho, Rondônia, in the Amazon Region of Brazil

The study area in Rondônia was the site of extensive malaria epidemic outbreaks in the 19th and 20th centuries related to environmental impacts, with large immigration flows. The present work analyzes the transmission dynamics of malaria in these areas to propose measures for avoiding epidemic outbreaks due to the construction of two Hydroelectric Power Plants. A population based baseline demographic census and a malaria prevalence follow up were performed in two river side localities in the suburbs of Porto Velho city and in its rural vicinity. The quantification and nature of malaria parasites in clinical patients and asymptomatic parasite carriers were performed using microscopic and Real Time PCR methodologies. Anopheles densities and their seasonal variation were done by monthly captures for defining HBR (hourly biting rate) values. Main results: (i) malaria among residents show the riverside profile, with population at risk represented by children and young adults; (ii) asymptomatic vivax and falciparum malaria parasite carriers correspond to around 15% of adults living in the area; (iii) vivax malaria relapses were responsible for 30% of clinical cases; (iv) malaria risk for the residents was evaluated as 20–25% for vivax and 5–7% for falciparum malaria; (v) anopheline densities shown outdoors HBR values 5 to 10 fold higher than indoors and reach 10.000 bites/person/year; (vi) very high incidence observed in one of the surveyed localities was explained by a micro epidemic outbreak affecting visitors and temporary residents. Temporary residents living in tents or shacks are accessible to outdoors transmission. Seasonal fishermen were the main group at risk in the study and were responsible for a 2.6 fold increase in the malaria incidence in the locality. This situation illustrates the danger of extensive epidemic outbreaks when thousands of workers and secondary immigrant population will arrive attracted by opportunities opened by the Hydroelectric Power Plants constructions

Unveiling relationships between crime and property in England and Wales via density scale-adjusted metrics and network tools

Scale-adjusted metrics (SAMs) are a significant achievement of the urban scaling hypothesis. SAMs remove the inherent biases of per capita measures computed in the absence of isometric allometries. However, this approach is limited to urban areas, while a large portion of the world’s population still lives outside cities and rural areas dominate land use worldwide. Here, we extend the concept of SAMs to population density scale-adjusted metrics (DSAMs) to reveal relationships among different types of crime and property metrics. Our approach allows all human environments to be considered, avoids problems in the definition of urban areas, and accounts for the heterogeneity of population distributions within urban regions. By combining DSAMs, cross-correlation, and complex network analysis, we find that crime and property types have intricate and hierarchically organized relationships leading to some striking conclusions. Drugs and burglary had uncorrelated DSAMs and, to the extent property transaction values are indicators of affluence, twelve out of fourteen crime metrics showed no evidence of specifically targeting affluence. Burglary and robbery were the most connected in our network analysis and the modular structures suggest an alternative to "zero-tolerance" policies by unveiling the crime and/or property types most likely to affect each other

Serologically defined variations in malaria endemicity in Pará state, Brazil

BACKGROUND: Measurement of malaria endemicity is typically based on vector or parasite measures. A complementary approach is the detection of parasite specific IgG antibodies. We determined the antibody levels and seroconversion rates to both P. vivax and P. falciparum merozoite antigens in individuals living in areas of varying P. vivax endemicity in Pará state, Brazilian Amazon region. METHODOLOGY/PRINCIPAL FINDINGS: The prevalence of antibodies to recombinant antigens from P. vivax and P. falciparum was determined in 1,330 individuals. Cross sectional surveys were conducted in the north of Brazil in Anajás, Belém, Goianésia do Pará, Jacareacanga, Itaituba, Trairão, all in the Pará state, and Sucuriju, a free-malaria site in the neighboring state Amapá. Seroprevalence to any P. vivax antigens (MSP1 or AMA-1) was 52.5%, whereas 24.7% of the individuals were seropositive to any P. falciparum antigens (MSP1 or AMA-1). For P. vivax antigens, the seroconversion rates (SCR) ranged from 0.005 (Sucuriju) to 0.201 (Goianésia do Pará), and are strongly correlated to the corresponding Annual Parasite Index (API). We detected two sites with distinct characteristics: Goianésia do Pará where seroprevalence curve does not change with age, and Sucuriju where seroprevalence curve is better described by a model with two SCRs compatible with a decrease in force of infection occurred 14 years ago (from 0.069 to 0.005). For P. falciparum antigens, current SCR estimates varied from 0.002 (Belém) to 0.018 (Goianésia do Pará). We also detected a putative decrease in disease transmission occurred ∼29 years ago in Anajás, Goianésia do Pará, Itaituba, Jacareacanga, and Trairão. CONCLUSIONS: We observed heterogeneity of serological indices across study sites with different endemicity levels and temporal changes in the force of infection in some of the sites. Our study provides further evidence that serology can be used to measure and monitor transmission of both major species of malaria parasite

Markov Chain Ontology Analysis (MCOA)

<p>Abstract</p> <p>Background</p> <p>Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data.</p> <p>Results</p> <p>In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods.</p> <p>Conclusion</p> <p>A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches.</p