An online analytical processing multi-dimensional data warehouse for malaria data

Malaria is a vector-borne disease that contributes substantially to the global burden of morbidity and mortality. The management of malaria-related data from heterogeneous, autonomous, and distributed data sources poses unique challenges and requirements. Although online data storage systems exist that address specific malaria-related issues, a globally integrated online resource to address different aspects of the disease does not exist. In this article, we describe the design, implementation, and applications of a multidimensional, online analytical processing data warehouse, named the VecNet Data Warehouse (VecNet-DW). It is the first online, globally-integrated platform that provides efficient search, retrieval and visualization of historical, predictive, and static malaria-related data, organized in data marts. Historical and static data are modelled using star schemas, while predictive data are modelled using a snowflake schema. The major goals, characteristics, and components of the DW are described along with its data taxonomy and ontology, the external data storage systems and the logical modelling and physical design phases. Results are presented as screenshots of a Dimensional Data browser, a Lookup Tables browser, and a Results Viewer interface. The power of the DW emerges from integrated querying of the different data marts and structuring those queries to the desired dimensions, enabling users to search, view, analyse, and store large volumes of aggregated data, and responding better to the increasing demands of users

Comparison between observed (in red) and modeled (in blue) abundance of <i>Acartia tonsa</i> (log₁₀(x+1)) at three sampling stations in the Gironde estuary. A

<p>. Station F. <b>B</b>. Station E. <b>C</b>. Station K. Modeled data originated from the realized niche assessed from monthly water temperature and salinity and using a mixed Gausian-linear model (see Fig. 2C).</p

Monthly water temperature and salinity of each record of <i>Acartia tonsa</i> at Station E. A

<p>. Occurrences of <i>Acartia tonsa</i> (blue dots) for period 1978–1983 (all months). <b>B</b>. Occurrences of <i>Acartia tonsa</i> (blue dots) for period 1978–1983 (August). <b>C</b>. Occurrences of <i>Acartia tonsa</i> (blue dots) for period 1984–2011 (all months). <b>D</b>. Occurrences of <i>Acartia tonsa</i> (blue dots) for period 1984–2011 (August). The estimated realized niche was superimposed (see Fig. 2A). Both temperature and salinity maxima from period 1978–1983 were also superimposed as black dotted lines.</p

Comparison between observed (in red) and modelled (in blue) abundance of <i>Acartia tonsa</i> (log₁₀(x+1)) at three sampling stations in the Gironde estuary. A

<p>. Station F. <b>B</b>. Station E. <b>C</b>. Station K. Modeled data originated from the NPPEN model (see Fig. 2D).</p

Comparison between observed (in red) and estimated (in blue) abundance of <i>Acartia tonsa</i> (log₁₀(x+1)) at three sampling stations in the Gironde estuary. A

<p>. Station F. <b>B</b>. Station E. <b>C</b>. Station K. Estimated data originated from the realized niche assessed by discretization from monthly water temperature and salinity (see Fig. 2B).</p

Map of the Gironde estuary with the three sampling sites (F, E, and K).

<p>Map of the Gironde estuary with the three sampling sites (F, E, and K).</p

Observed and modeled ecological niche of <i>Acartia tonsa</i> in the Gironde estuary. A

<p>. Observed niche of <i>A. tonsa</i> (log₁₀ (x+1)) as a function of monthly water temperature and salinity (considering all available data). <b>B</b>. Realized niche of <i>A. tonsa</i> (log₁₀ (x+1)) as a function of monthly water temperature and salinity. <b>C</b>. Realized niche of <i>A. tonsa</i> (log₁₀ (x+1)) as a function of monthly water temperature and salinity modeled by a mixed Gausian-linear model. <b>D</b>. Fundamental niche of <i>A. tonsa</i> (occurrence probability) modeled by the NPPEN model.</p

Proposition d'espèces non-indigènes pour les façades maritimes du territoire métropolitain à soumettre à réglementation

Le règlement européen relatif aux espèces exotiques envahissantes (EEE) (1143/20141) fournit une liste d’EEE réglementées à l’échelle européenne. A ce jour, sur les 66 EEE de cette liste, seulement deux sont des espèces non indigènes (ENI) marines. L’objectif du présent travail est de proposer une liste d’espèces à réglementation à l’échelle nationale (selon les articles L411-5 dit de Niveau 1 et L411-6 dit de Niveau 2 du Code de l’Environnement2), suivant une nouvelle procédure d’analyse de risques décrite sommairement ci-dessous

Local and regional dynamics of chikungunya virus transmission in Colombia : The role of mismatched spatial heterogeneity

Background: Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data. Methods: We adapted the recently developed Disease Transmission Kernel (DTK)-Dengue model for modeling chikungunya virus (CHIKV) transmission, given the numerous similarities of these viruses vectored by a common mosquito vector. We fitted versions of this model specified at different spatial scales to weekly case reports aggregated at different spatial scales: (1) single-patch national model fitted to national data; (2) single-patch departmental models fitted to departmental data; and (3) multi-patch departmental models fitted to departmental data, where the multiple patches refer to municipalities within a department. We compared the consistency of simulations from fitted models with empirical data. Results: We found that model consistency with epidemic dynamics improved with increasing spatial granularity of the model. Specifically, the sum of single-patch departmental model fits better captured national-level temporal patterns than did a single-patch national model. Likewise, multi-patch departmental model fits better captured department-level temporal patterns than did single-patch departmental model fits. Furthermore, inferences about municipal-level incidence based on multi-patch departmental models fitted to department-level data were positively correlated with municipal-level data that were withheld from model fitting. Conclusions: Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns