Using meta-quality to assess the utility of volunteered geographic information for science.

BACKGROUND: Volunteered geographic information (VGI) has strong potential to be increasingly valuable to scientists in collaboration with non-scientists. The abundance of mobile phones and other wireless forms of communication open up significant opportunities for the public to get involved in scientific research. As these devices and activities become more abundant, questions of uncertainty and error in volunteer data are emerging as critical components for using volunteer-sourced spatial data. METHODS: Here we present a methodology for using VGI and assessing its sensitivity to three types of error. More specifically, this study evaluates the reliability of data from volunteers based on their historical patterns. The specific context is a case study in surveillance of tsetse flies, a health concern for being the primary vector of African Trypanosomiasis. RESULTS: Reliability, as measured by a reputation score, determines the threshold for accepting the volunteered data for inclusion in a tsetse presence/absence model. Higher reputation scores are successful in identifying areas of higher modeled tsetse prevalence. A dynamic threshold is needed but the quality of VGI will improve as more data are collected and the errors in identifying reliable participants will decrease. CONCLUSIONS: This system allows for two-way communication between researchers and the public, and a way to evaluate the reliability of VGI. Boosting the public's ability to participate in such work can improve disease surveillance and promote citizen science. In the absence of active surveillance, VGI can provide valuable spatial information given that the data are reliable.

Embracing the Open-Source Movement for the Management of Spatial Data: A Case Study of African Trypanosomiasis in Kenya.

The past decade has seen an explosion in the availability of spatial data not only for researchers, but the public alike. As the quantity of data increases, the ability to effectively navigate and understand the data becomes more challenging. Here we detail a conceptual model for a spatially explicit database management system that addresses the issues raised with the growing data management problem. We demonstrate utility with a case study in disease ecology: to develop a multi-scale predictive model of African Trypanosomiasis in Kenya. International collaborations and varying technical expertise necessitate a modular open-source software solution. Finally, we address three recurring problems with data management: scalability, reliability, and security.

A methodology for projecting hospital bed need: a Michigan case study.

Michigan's Department of Community Health (MDCH) is responsible for managing hospitals through the utilization of a Certificate of Need (CON) Commission. Regulation is achieved by limiting the number of beds a hospital can use for inpatient services. MDCH assigns hospitals to service areas and sub areas by use patterns. Hospital beds are then assigned within these Hospital Service Areas and Facility Sub Areas. The determination of the number of hospital beds a facility subarea is authorized to hold, called bed need, is defined in the Michigan Hospital Standards and published by the CON Commission and MDCH. These standards vaguely define a methodology for calculating hospital bed need for a projection year, five years ahead of the base year (defined as the most recent year for which patient data have been published by the Michigan Hospital Association). MDCH approached the authors and requested a reformulation of the process. Here we present a comprehensive guide and associated code as interpreted from the hospital standards with results from the 2011 projection year. Additionally, we discuss methodologies for other states and compare them to Michigan's Bed Need methodology.

Learning by the parasitoid wasp, Aphidius ervi (Hymenoptera: Braconidae), alters individual fixed preferences for pea aphid color morphs.

Learning, defined as changes in behavior that occur due to past experience, has been well documented for nearly 20 species of hymenopterous parasitoids. Few studies, however, have explored the influence of learning on population-level patterns of host use by parasitoids in field populations. Our study explores learning in the parasitoid Aphidius ervi Haliday that attacks pea aphids, Acyrthosiphon pisum (Harris). We used a sequence of laboratory experiments to investigate whether there is a learned component in the selection of red or green aphid color morphs. We then used the results of these experiments to parameterize a model that examines whether learned behaviors can explain the changes in the rates of parasitism observed in field populations in South-central Wisconsin, USA. In the first of two experiments, we measured the sequence of host choice by A. ervi on pea aphid color morphs and analyzed this sequence for patterns in biased host selection. Parasitoids exhibited an inherent preference for green aphid morphs, but this preference was malleable; initial encounters with red aphids led to a greater chance of subsequent orientation towards red aphids than predicted by chance. In a second experiment, we found no evidence that parasitoids specialize on red or green morphs; for the same parasitoids tested in trials separated by 2 h, color preference in the first trial did not predict color preference in the second, as would be expected if they differed in fixed preferences or exhibited long-term (> 2 h) learning. Using data from the two experiments, we parameterized a population dynamics model and found that learning of the magnitude observed in our experiments leads to biased parasitism towards the most common color morph. This bias is sufficient to explain changes in the ratio of aphid color morphs observed in field sites over multiple years. Our study suggests that for even relatively simple organisms, learned behaviors may be important for explaining the population dynamics of their hosts.