Maplaria: a user friendly web-application for spatio-temporal malaria prevalence mapping

Background Model-based geostatistical (MBG) methods have been extensively used to map malaria risk using community survey data in low-resource settings where disease registries are incomplete or non-existent. However, the wider adoption of MBG methods by national control programmes to inform health policy decisions is hindered by the lack of advanced statistical expertise and suitable computational equipment. Here, Maplaria, an interactive, user-friendly web-application that allows users to upload their own malaria prevalence data and carry out geostatistical prediction of annual malaria prevalence at any desired spatial scale, is introduced. Methods In the design of the Maplaria web application, two main criteria were considered: the application should be able to classify subnational divisions into the most likely endemicity levels; the web application should allow only minimal input from the user in the set-up of the geostatistical inference process. To achieve this, the process of fitting and validating the geostatistical models is carried out by statistical experts using publicly available malaria survey data from the Harvard database. The stage of geostatistical prediction is entirely user-driven and allows the user to upload malaria data, as well as vector data that define the administrative boundaries for the generation of spatially aggregated inferences. Results The process of data uploading and processing is split into a series of steps spread across screens through the progressive disclosure technique that prevents the user being immediately overwhelmed by the length of the form. Each of these is illustrated using a data set from the Malaria Indicator carried out in Tanzania in 2017 as an example. Conclusions Maplaria application provides a user-friendly solution to the problem making geostatistical methods more accessible to users that have not undertaken formal training in statistics. The application is a useful tool that can be used to foster ownership, among policy makers, of disease risk maps and promote better use of data for decision-making in low resource settings.

Kriging-based prediction of the Earth’s pole coordinates

Coordinates of the Earth’s pole represent two out of five Earth orientation parameters describing Earth’s rotation. They are necessary in transformation between celestial reference frame and terrestrial reference frame and what goes further in precise positioning and navigation, applications in astronomy, communication with outer space objects. Complexity of measuring techniques and data processing involved in the pole coordinates determination make it impossible to obtain them in real-time mode, hence a prediction problem of the polar motion emerges. In this study, geostatistical prediction methods, i. e., simple and ordinary kriging are applied. Millions of predictions have been performed to draw reasonable conclusions on prediction capabilities of applied kriging variants. The study is intended in ultra-short-term prediction (up to 15 days into the future) using the IERS EOP 14 C04 (IAU2000A) and IERS EOP 05 C04 (IAU2000A) series as a reference. Mean absolute prediction errors (for days 1–15) with respect to IERS 14 C04 are ranging 0.66–5.25 mas for PMx and 0.47–3.59 mas for PMy. On the other hand, for IERS 05 C04 the values are 0.60–4.95 mas and 0.44–3.29 mas for PMx and PMy; respectively. The results indicate competitiveness of the introduced methods with existing ones.

Geostatistical Distribution and Contamination Status of Heavy Metals in the Sediment of Perak River, Malaysia

Heavy metal pollution is one of the major environmental issues in recent decades owing to the rapid increase in urbanisation and industrialisation. Sediments usually act as sinks for heavy metals due to their complex physical and chemical adsorption mechanisms. In this study, heavy metals like lead (Pb), Zinc (Zn), Cadmium (Cd), Copper (Cu) and Iron (Fe) in the surface sediment from 15 location (upstream and downstream) on the Perak River, Malaysia were investigated by means of inductively coupled plasma optical emission spectroscopy (ICP-OES). The geostatistical prediction map showed the range of Pb, Zn, Cd, Cu and Fe concentration in upstream area was 14.56–27.0 µg/g, 20–51.27 µg/g, 1.51–3.0 µg/g, 6.6–19.12 µg/g and 20.24–56.58%, respectively, and in downstream areas was 27.6–60.76 µg/g, 49.04–160.5 µg/g, 2.77–4.02 µg/g, 9.82–59.99 µg/g and 31.34–39.5%, respectively. Based on the enrichment factor and geoaccumulation index, Cd was found to be the most dominant pollutant in the study area. Pollution load index, sediment quality guidelines and sediment environmental toxicity quotient data showed that the downstream sediment was more polluted than the upstream sediment in the Perak River. The multivariate analysis showed that Pb, Zn and Cu mainly originated from natural sources with minor contribution from human activities, whereas Fe and Cd originated from various industrial and agricultural activities along the studied area.