Periodic collection of field data, analysis and interpretation of data are key to a good healthcare service. This data is used by the subsequent decision makers to recognize preventive measures, provide timely support to the affected and to help measure the effects of their interventions. While the resources required for good disease surveillance and proactive healthcare are available more readily in developed countries, the lack of these in developing countries may compromise the quality of service provided. This combined with the critical nature of some diseases makes this an essential issue to be addressed. Taking advantage of the rapid growth of cell phone usage and related infrastructure in developed as well as developing countries, several systems have been established to address the gaps in data collection. Android, being an open sourced platform, has gained considerable popularity in this aspect. Open data kit is one such tool developed to aid in data collection. The aim of this paper is to present a prototype framework built using few such existing tools and technologies to address data collection for seasonal influenza, commonly referred to as the flu.

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In sofmare organizations there is a very real possibility that a commitment to existing assets will require migration of legacy software towards new environments that use modern technology. One technique that has been suggested for facilitating the migration of existing legacy assets to new plaqorms is via the use of the adapter design pattern, also known as component wrapping. In this papec we describe an approach f o r facilitating the integration of legacy software into new applications using component wrapping. That is, we demonstrate the use of a software architecture description language as a means f o r specifying various properties that can be used to assist in the construction of wrappers. In addition, we show how these wrapped components can be used within a distributed object infrastructure as services that are d)v"cally integrated at runtime.

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Capture-mark-recapture (CMR) approaches are often used in the management and conservation of wildlife species and effective approaches to estimate populations rely on accurate datacollection techniques. Unfortunately, sampling errors can arise at several steps in the CMR process. We designed and applied a mobile application (app) for field-collected data from a CMR study on herpetofauna. Entering data into a digital format in the field using the mobile application eliminated timely steps to process handwritten data sheets and double-checking data entries. Applications were developed for mobile devices on the iOS and Android operating systems. Both platforms have similar user interactions via data entry on a touch screen using pre-programmed fields, checkboxes, drop-down menus, and keypad entry. Our mobile application includes features to insure collection of all measurements in the field through pop-up messages and can proof entries for valid formats. The application can assign unique numbers to newly captured individuals by querying the database for unused codes and eliminates the potential to assign 2 individuals with the same unique code. Technicians can query a database to view histories of previously captured animals from the field. During a 2-month project, we estimated that using the mobile application instead of manual data entry and proofing via data sheets reduced our total project time by 10%. To our knowledge, this is the first application developed for mobile devices for biologists using CMR techniques and could be developed for use in other areas of field data collection. Ó 2013 The Wildlife Society.

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