ABSTRACT. We describe a novel software application (QCal) that was developed for calculation of ... business logic tier in a single package, and is comprised ...
QCal: A Software Application for the Calculation of Dose–Response Curves In Insecticide Resistance Bioassays Author(s): Saul Lozano-Fuentes, Karla Saavedra-Rodriguez, William C. Black IV, and Lars Eisen Source: Journal of the American Mosquito Control Association, 28(1):59-61. 2012. Published By: The American Mosquito Control Association DOI: http://dx.doi.org/10.2987/11-6192.1 URL: http://www.bioone.org/doi/full/10.2987/11-6192.1
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Journal of the American Mosquito Control Association, 28(1):59–61, 2012 Copyright E 2012 by The American Mosquito Control Association, Inc.
SCIENTIFIC NOTE QCAL: A SOFTWARE APPLICATION FOR THE CALCULATION OF DOSE– RESPONSE CURVES IN INSECTICIDE RESISTANCE BIOASSAYS SAUL LOZANO-FUENTES, KARLA SAAVEDRA-RODRIGUEZ, WILLIAM C. BLACK IV AND LARS EISEN Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO 80523 ABSTRACT. We describe a novel software application (QCal) that was developed for calculation of dose–response curves in insecticide resistance bioassays. QCal uses a logistic regression model to generate values for lethal dose/knockdown dose based on data from a bioassay entered into the application user interface. The application can be freely distributed to interested parties. KEY WORDS
Insecticide resistance bioassay, dose–response curve, mosquito vector
We recently completed the development of a novel multidisease data management system for operational dengue and malaria control programs (Eisen et al. 2011, Lozano-Fuentes et al. 2011). The system includes extensive functionalities for capture of mosquito surveillance data, including insecticide resistance monitoring data based on bioassays, biochemical assays, or genetic assays. The insecticide resistance bioassay is a critically important component of programs to monitor the resistance of mosquito vectors to commonly used insecticide active ingredients (Brogdon and McAllister 1998a, 1998b; Fundac¸a˜o Nacional de Sau´de 1999; Hemingway and Ranson 2000; Coleman and Hemingway 2007). Detailed manuals for conducting insecticide resistance bioassays with mosquito vectors have been produced by the World Health Organization (WHO 1998, 2002), and the US Centers for Disease Control and Prevention has produced an online course for evaluating mosquitoes for insecticide resistance (http://www.cdc.gov/ncidod/wbt/resistance/toc. htm). However, in the course of developing the abovementioned multidisease data management system, we realized that a user-friendly software application to generate dose–response curves based on data from insecticide resistance bioassays was lacking. We therefore proceeded to develop such an application, called QCal, as a companion application to the multidisease data management system. QCal does not include a database but rather is a tool to calculate data— for example, Lethal Dose50 values—for subsequent inclusion in databases or applications with underlying databases such as the multidisease data management system. The QCal application was developed as opensource software, with the presentation tier and business logic tier in a single package, and is comprised exclusively of software components that can be distributed without licensing costs.
The application was written in Visual Basic .NET 2008 (Microsoft Corporation, Redmond, WA) for a Microsoft Windows platform. It incorporates the ZedGraph library (http://sourceforge. net/projects/zedgraph/) to generate graphs. The target operating system is Windows 7, but the application has been informally tested on and shown to function also for Windows XP and Vista. The logistic regression model used in QCal is based on Visual Basic 6 code produced and freely shared with us by Chad Rankin. The source code for the logistic regression model was ported to Visual Basic .NET 2008 and incorporated into QCal. QCal logistic regression model results were tested extensively against independent results from a corresponding logistic regression model available in the R statistical package (http://www. r-project.org/). The QCal project source code can be found at http://irmaproj.svn.sourceforge.net/ viewvc/irmaproj/QCal/. QCal was developed specifically for calculation of dose–response curves in insecticide resistance bioassays. The application uses a logistic regression model to generate values for lethal dose/ knockdown dose (LD/KD) based on data from a bioassay entered into the QCal user interface. We use the combined language for lethal/knockdown because the same mosquito species may exhibit knockdown for some insecticide active ingredients but be killed by others, and the active ingredient used is not specified in QCal. Standard outputs include values for LD50/KD50 and LD90/ KD90, and QCal also has an advanced option to obtain values for other custom LD/KD percentages. There is an option in QCal to display the results of the calculation, based on the same logistic regression model, as time–response rather than dose–response, but the user is cautioned that, although this is sometimes done, this is not statistically correct unless the mortality data for the different time points are not aggregated. 59
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Fig. 1. QCal interface for entry of insecticide resistance bioassay data and display of binary logistic regression model results. Buttons at the top left allow the user to switch between dose–response (beaker) and time–response (clock) calculations, save to Excel (Excel symbol), edit grid table data (pencil), erase grid table data (eraser), and paste copied data into the grid (clipboard). Clicking the OK button at the lower right runs the logistic regression model. The Custom Values button allows for calculations based on user-defined lethal dose/knockdown dose (LD/ KD) values. Note: Knocked-down or dead mosquitoes are collectively referred to as Inactive in the grid table.
Clickable icons allow the user to select dose– response mode or time–response mode. Thereafter, the relevant data from the bioassay are entered into the QCal interface data grid: this can be done either manually or by copying from a *.xls spreadsheet or tab-separated text file and pasting into QCal’s data entry grid (Fig. 1). Data cannot be stored in the QCal application itself because it does not include an underlying database. The logistic regression calculation is run when the user clicks the OK button. The results for the model are shown in a graph, and a text box under the graph provides statistical details for the model (estimate, SE, z-value, and P-value) and values for LD50/KD50 and LD90/KD90 (log value, backtransformed value from the log value, lower limit for 95% confidence interval, and upper limit for 95% confidence interval) (Fig. 1). Following the calculation, the user can select the advance modeling option by clicking the ‘‘Custom Values’’ icon and then sliding a bar to select a given response percentage of interest. Regression model outputs can be saved to a *.xls spreadsheet by clicking a ‘‘Save to Excel’’ icon. The potential user should note that the current version of QCal, which was developed primarily to support operational vector control programs, does not provide the full set of statistical outputs that may
be needed for publication of the results in scientific journals. QCal is licensed under the GNU General Public License version 3 (http://www.gnu.org/licenses/gpl. html), which is a free, copyleft license for software and other kinds of works. The application can be accessed for download directly from websites (http://sourceforge.net/projects/irmaproj/files/ or http://www.rams-aid.org/DDSS/ssapplication. php) or by contacting the Innovative Vector Control Consortium (http://www.ivcc.com/index.htm). This work was funded by the Innovative Vector Control Consortium. REFERENCES CITED Brogdon WG, McAllister JC. 1998a. Insecticide resistance and vector control. Emerg Infect Dis 4:605– 613. Brogdon WG, McAllister JC. 1998b. Simplification of adult mosquito bioassays through use of time– mortality determinations in glass bottles. J Am Mosq Contr Assoc 14:159–164. Coleman M, Hemingway J. 2007. Insecticide resistance monitoring and evaluation in disease transmitting mosquitoes. J Pestic Sci 32:69–76. Eisen L, Coleman M, Lozano-Fuentes S, McEachen N, Orlans M, Coleman M. 2011. Multi-disease data management system platform for vector-borne diseases. PLoS Negl Trop Dis 5:e1016.
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Fundac¸a˜o Nacional de Sau ´ de. 1999. Reunia˜o te´cnica para discutir status de resisteˆncia de Aedes aegypti e definir estrate´gias a serem implantadas para monitoramento da resisteˆncia no Brasil. Brası´lia, Brazil: Ministe´rio da Sau´de. Hemingway J, Ranson H. 2000. Insecticide resistance in insect vectors of human disease. Annu Rev Entomol 45:371–391. Lozano-Fuentes S, Barker CM, Coleman M, Coleman M, Park B, Reisen WK, Eisen L Emerging information technologies to provide improved decision support for surveillance, prevention, and control of vector-borne diseases. In: Jao C, ed. Efficient decision support systems–practice and challenges in
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biomedical related domain. Rijeka, Croatia: InTech– Open Access Publisher. p 89–114. (Available from: http://www.intechopen.com/articles/show/title/emerg ing-information-technologies-to-provide-improved-deci sion-support-for-surveillance-prevention-a.) WHO [World Health Organization]. 1998. Test procedures for insecticide resistance monitoring in malaria vectors, bio-efficacy and persistence of insecticide on treated surfaces. WHO/CDS/CPC/MAL/98.12. Geneva, Switzerland: World Health Organization. WHO [World Health Organization]. 2002. Supplies for monitoring insecticide resistance in disease vectors: procedures and conditions. WHO/CDS/CPE/PVC/2001.2. Geneva, Switzerland: World Health Organization.
