Modelling habitat suitability in Jordan for the cutaneous leishmaniasis vector (Phlebotomus papatasi) using multicriteria decision analysis.

Cutaneous leishmaniasis (CL) is a zoonotic vector-borne neglected tropical disease transmitted by female Phlebotomine sand flies. It is distributed globally but a large proportion of cases (70-75%) are found in just ten countries. CL is endemic in Jordan yet there is a lack of robust entomological data and true reporting status is unknown. This study aimed to map habitat suitability of the main CL vector, Phlebotomus papatasi, in Jordan as a proxy for CL risk distribution to (i) identify areas potentially at risk of CL and (ii) estimate the human population at risk of CL. A literature review identified potential environmental determinants for P. papatasi occurrence including temperature, humidity, precipitation, vegetation, wind speed, presence of human households and presence of the fat sand rat. Each predictor variable was (a) mapped; (b) standardized to a common size, resolution and scale using fuzzy membership functions; (c) assigned a weight using the analytical hierarchy process (AHP); and (d) included within a multicriteria decision analysis (MCDA) model to produce monthly maps illustrating the predicted habitat suitability (between 0 and 1) for P. papatasi in Jordan. Suitability increased over the summer months and was generally highest in the north-western regions of the country and along the Jordan Valley, areas which largely coincided with highly populated parts of the country, including areas where Syrian refugee camps are located. Habitat suitability in Jordan for the main CL vector-P. papatasi-was heterogeneous over both space and time. Suitable areas for P. papatasi coincided with highly populated areas of Jordan which suggests that the targeted implementation of control and surveillance strategies in defined areas such as those with very high CL vector suitability (>0.9 suitability) would focus only on 3.42% of the country's total geographic area, whilst still including a substantial proportion of the population at risk: estimates range from 72% (European Commission's Global Human Settlement population grid) to 89% (Gridded Population of the World) depending on the human population density data used. Therefore, high impact public health interventions could be achieved within a reduced spatial target, thus maximizing the efficient use of resources.

Spatial and temporal epidemiological analysis in the Big Data era.

Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.

Modeling habitat suitability for occurrence of highly pathogenic avian influenza virus H5N1 in domestic poultry in Asia: a spatial multicriteria decision analysis approach.

Risk maps are one of several sources used to inform risk-based disease surveillance and control systems, but their production can be hampered by lack of access to suitable disease data. In such situations, knowledge-driven spatial modeling methods are an alternative to data-driven approaches. This study used multicriteria decision analysis (MCDA) to identify areas in Asia suitable for the occurrence of highly pathogenic avian influenza virus (HPAIV) H5N1 in domestic poultry. Areas most suitable for H5N1 occurrence included Bangladesh, the southern tip and eastern coast of Vietnam, parts of north-central Thailand and large parts of eastern China. The predictive accuracy of the final model, as determined by the area under the receiver operating characteristic curve (ROC AUC), was 0.670 (95% CI 0.667-0.673) suggesting that, in data-scarce environments, MCDA provides a reasonable alternative to the data-driven approaches usually used to inform risk-based disease surveillance and control strategies.