A schematic overview of the process of predicting spatial disease risk.

<p>The <i>definitive extent</i> of infectious disease occurrence at the national level (red is certain presence, green is certain absence) <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001413#pmed.1001413-Brady1" target="_blank">[16]</a> is combined with assemblies of known occurrence, presence <i>points</i> (red dots), to generate putative <i>pseudo-absence points</i> (blue dots). The <i>presence</i> and <i>pseudo-absence</i> data are then used in the analyses, with selected <i>environmental covariates</i> to predict <i>disease risk</i>, formally the probability of occurrence of the target disease. In this example a risk map of dengue is shown, shaded from low probability of occurrence in blue to high probability of occurrence in red <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001413#pmed.1001413-Simmons1" target="_blank">[8]</a>. The arrows represent data flows.</p

An assessment of the challenges of using Big Data in disease mapping.

<p>The potential Big Data challenges in each stage of an iterative mapping process are highlighted in the table. The columns represent each of the mapping stages defined in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001413#pmed-1001413-g001" target="_blank">Figure 1</a>. The rows reflect the volume, velocity, and variety descriptors of data contributions. The future Big Data challenge in relation to infectious disease risk mapping is as follows: low (+), medium (++), and high (+++).</p

Appendix C. A figure showing the distribution of AUC for model fits by the two different methods of simulation.

A figure showing the distribution of AUC for model fits by the two different methods of simulation

Appendix D. A figure summarizing extinction patterns over time from simulations.

A figure summarizing extinction patterns over time from simulations

Supplement 1. Code for parameter estimation and simulation with the stochastic patch-occupancy model.

<h2>File List</h2><div> <p><a href="SPOMcode.txt">SPOMcode.txt</a> (MD5: e9aca88ace4c6c59761eb5cffe061f70) </p> </div><h2>Description</h2><div> <p>The MATLAB script SPOMcode.txt generates the likelihood equation to be maximized for parameter estimation. The script also contains a function (pdogoptTMC) for conducting the estimation and script for conducting simulations with the estimated parameters. </p> </div

Appendix A. A figure showing colony areas over time and relative colony size relationships.

A figure showing colony areas over time and relative colony size relationships

Appendix B. A figure showing results of sensitivity analyses for initial conditions of the simulations.

A figure showing results of sensitivity analyses for initial conditions of the simulations

Database of cutaneous leishmaniasis occurrences

A database containing cutaneous leishmaniasis occurrences used in the boosted regression trees analysi

Database of visceral leishmaniasis occurrences

A database containing visceral leishmaniasis occurrences used in the boosted regression trees analysi

Global Evidence Consensus for Visceral Leishmanisis

A survey of the evidence for the presence or absence of visceral leishmaniasis in each province/state in the world (Admin 1