Developing spatio-temporal prediction models for arbovirus activity in nothern Australia based on remotely sensed bioclimatic variables

Vector-borne diseases pose an ongoing threat to public and animal health in the north ofAustralia. A number of surveillance programs are in place to determine the extent of virus activityand control the risk, but these are labour- and cost intensive while producing data with largetemporal and spatial gaps. Using the example of Bluetongue virus, the aim of this study was toinvestigate the potential of remotely sensed variables to facilitate the development of area-widepredictive models that complement traditional surveillance activities.Bioclimatic variables were derived for the Northern Territory from MODIS and TRMM remotesensing data products covering a period of nine years. Spatial and temporal uncertainty in thesurveillance data required the annual aggregation of environmental variables on a pastoralproperty level. Generalized Additive Models (GAM) were developed based on variables such asNDVI and land surface temperature to produce annual prediction maps of virus activity. Externalvalidation showed that the model correctly predicted 75% of the results from cattle stations testedfor Bluetongue. Remaining uncertainty in the model can be mainly attributed to the spatio-temporalinconsistency of the available surveillance data.This case study has developed a cost-effective approach based on a set of robustenvironmental predictors that facilitate the generation of arbovirus prediction maps soon after thepeak of risk for infection. While this research focused on Bluetongue Virus, we see a large potentialto expand the method to other areas and viruses particularly in view of the increasing populationsin Northern Australia

Application of satellite precipitation data to analyse and model arbovirus activity in the tropics

Background: Murray Valley encephalitis virus (MVEV) is a mosquito-borne Flavivirus (Flaviviridae: Flavivirus) which isclosely related to Japanese encephalitis virus, West Nile virus and St. Louis encephalitis virus. MVEV is enzootic innorthern Australia and Papua New Guinea and epizootic in other parts of Australia. Activity of MVEV in WesternAustralia (WA) is monitored by detection of seroconversions in flocks of sentinel chickens at selected sample sitesthroughout WA.Rainfall is a major environmental factor influencing MVEV activity. Utilising data on rainfall and seroconversions,statistical relationships between MVEV occurrence and rainfall can be determined. These relationships can be usedto predict MVEV activity which, in turn, provides the general public with important information about diseasetransmission risk. Since ground measurements of rainfall are sparse and irregularly distributed, especially in northWA where rainfall is spatially and temporally highly variable, alternative data sources such as remote sensing (RS)data represent an attractive alternative to ground measurements. However, a number of competing alternatives areavailable and careful evaluation is essential to determine the most appropriate product for a given problem.Results: The Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42product was chosen from a range of RS rainfall products to develop rainfall-based predictor variables and buildlogistic regression models for the prediction of MVEV activity in the Kimberley and Pilbara regions of WA. Twomodels employing monthly time-lagged rainfall variables showed the strongest discriminatory ability of 0.74 and0.80 as measured by the Receiver Operating Characteristics area under the curve (ROC AUC).Conclusions: TMPA data provide a state-of-the-art data source for the development of rainfall-based predictivemodels for Flavivirus activity in tropical WA. Compared to ground measurements these data have the advantage ofbeing collected spatially regularly, irrespective of remoteness. We found that increases in monthly rainfall andmonthly number of days above average rainfall increased the risk of MVEV activity in the Pilbara at a time-lag oftwo months. Increases in monthly rainfall and monthly number of days above average rainfall increased the risk ofMVEV activity in the Kimberley at a lag of three months.I

Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

The objective of this paper is to test the relationships between Above Ground Biomass (AGB) and remotely sensed vegetation indices for AGB assessments in the Kimberley area in Western Australia. For 19 different sites, vegetation indices were derived from eight Landsat ETM+ scenes over a period of two years (2011–2013). The sites were divided into three groups (Open plains, Bunch grasses and Spinifex) based on similarities in dominant vegetation types. Dry and green biomass fractions were measured at these sites. Single and multiple regression relationships between vegetation indices and green and total AGB were calibrated and validated using a "leave site out" cross validation. Four tests were compared: (1) relationships between AGB and vegetation indices combining all sites; (2) separate relationships per site group; (3) multiple regressions including selected vegetation indices per site group; and (4) as in 3 but including rainfall and elevation data. Results indicate that relationships based on single vegetation indices are moderately accurate for green biomass in wide open plains covered with annual grasses. The cross-validation results for green AGB improved for a combination of indices for the Open plains and Bunch grasses sites, but not for Spinifex sites. When rainfall and elevation data are included, cross validation improved slightly with a Q2 of 0.49–0.72 for Open plains and Bunch grasses sites respectively. Cross validation results for total AGB were moderately accurate (Q2 of 0.41) for Open plains but weak or absent for other site groups despite good calibration results, indicating strong influence of site-specific factors

Modelling typhoid risk in Dhaka Metropolitan Area of Bangladesh: the role of socio-economic and environmental factors

BackgroundDeveloping countries in South Asia, such as Bangladesh, bear a disproportionate burden of diarrhoeal diseases such as Cholera, Typhoid and Paratyphoid. These seem to be aggravated by a number of social and environmental factors such as lack of access to safe drinking water, overcrowdedness and poor hygiene brought about by poverty. Some socioeconomic data can be obtained from census data whilst others are more difficult to elucidate. This study considers a range of both census data and spatial data from other sources, including remote sensing, as potential predictors of typhoid risk. Typhoid data are aggregated from hospital admission records for the period from 2005 to 2009. The spatial and statistical structures of the data are analysed and Principal Axis Factoring is used to reduce the degree of co-linearity in the data. The resulting factors are combined into a Quality of Life index, which in turn is used in a regression model of typhoid occurrence and risk.ResultsThe three Principal Factors used together explain 87% of the variance in the initial candidate predictors, which eminently qualifies them for use as a set of uncorrelated explanatory variables in a linear regression model. Initial regression result using Ordinary Least Squares (OLS) were disappointing, this was explainable by analysis of the spatial autocorrelation inherent in the Principal factors. The use of Geographically Weighted Regression caused a considerable increase in the predictive power of regressions based on these factors. The best prediction, determined by analysis of the Akaike Information Criterion (AIC) was found when the three factors were combined into a quality of life index, using a method previously published by others, and had a coefficient of determination of 73%.ConclusionsThe typhoid occurrence/risk prediction equation was used to develop the first risk map showing areas of Dhaka Metropolitan Area whose inhabitants are at greater or lesser risk of typhoid infection. This, coupled with seasonal information on typhoid incidence also reported in this paper, has the potential to advise public health professionals on developing prevention strategies such as targeted vaccination