» Articles » PMID: 21468310

Multi-disease Data Management System Platform for Vector-borne Diseases

Overview
Date 2011 Apr 7
PMID 21468310
Citations 25
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Emerging information technologies present new opportunities to reduce the burden of malaria, dengue and other infectious diseases. For example, use of a data management system software package can help disease control programs to better manage and analyze their data, and thus enhances their ability to carry out continuous surveillance, monitor interventions and evaluate control program performance.

Methods And Findings: We describe a novel multi-disease data management system platform (hereinafter referred to as the system) with current capacity for dengue and malaria that supports data entry, storage and query. It also allows for production of maps and both standardized and customized reports. The system is comprised exclusively of software components that can be distributed without the user incurring licensing costs. It was designed to maximize the ability of the user to adapt the system to local conditions without involvement of software developers. Key points of system adaptability include 1) customizable functionality content by disease, 2) configurable roles and permissions, 3) customizable user interfaces and display labels and 4) configurable information trees including a geographical entity tree and a term tree. The system includes significant portions of functionality that is entirely or in large part re-used across diseases, which provides an economy of scope as new diseases downstream are added to the system at decreased cost.

Conclusions: We have developed a system with great potential for aiding disease control programs in their task to reduce the burden of dengue and malaria, including the implementation of integrated vector management programs. Next steps include evaluations of operational implementations of the current system with capacity for dengue and malaria, and the inclusion in the system platform of other important vector-borne diseases.

Citing Articles

Real-time, spatial decision support to optimize malaria vector control: The case of indoor residual spraying on Bioko Island, Equatorial Guinea.

Garcia G, Atkinson B, Donfack O, Hilton E, Smith J, Eyono J PLOS Digit Health. 2023; 1(5):e0000025.

PMID: 36812503 PMC: 9931250. DOI: 10.1371/journal.pdig.0000025.


VectorMap-GR: A local scale operational management tool for entomological monitoring, to support vector control activities in Greece and the Mediterranean Basin.

Fotakis E, Orfanos M, Kouleris T, Stamatelopoulos P, Tsiropoulos Z, Kampouraki A Curr Res Parasitol Vector Borne Dis. 2022; 1:100053.

PMID: 35284881 PMC: 8906066. DOI: 10.1016/j.crpvbd.2021.100053.


Ecological Dynamics Impacting Bluetongue Virus Transmission in North America.

Mayo C, McDermott E, Kopanke J, Stenglein M, Lee J, Mathiason C Front Vet Sci. 2020; 7:186.

PMID: 32426376 PMC: 7212442. DOI: 10.3389/fvets.2020.00186.


Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors.

Moyes C, Wiebe A, Gleave K, Trett A, Hancock P, Padonou G Sci Data. 2019; 6(1):121.

PMID: 31308378 PMC: 6629700. DOI: 10.1038/s41597-019-0134-2.


Efficacy of the In2Care® auto-dissemination device for reducing dengue transmission: study protocol for a parallel, two-armed cluster randomised trial in the Philippines.

Salazar F, Angeles J, Sy A, Inobaya M, Aguila A, Toner T Trials. 2019; 20(1):269.

PMID: 31088515 PMC: 6518692. DOI: 10.1186/s13063-019-3376-6.


References
1.
Booman M, Sharp B, Martin C, Manjate B, La Grange J, Durrheim D . Enhancing malaria control using a computerised management system in southern Africa. Malar J. 2003; 2:13. PMC: 161823. DOI: 10.1186/1475-2875-2-13. View

2.
Eisen L, Eisen R . Using geographic information systems and decision support systems for the prediction, prevention, and control of vector-borne diseases. Annu Rev Entomol. 2010; 56:41-61. DOI: 10.1146/annurev-ento-120709-144847. View

3.
Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W . The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol. 2007; 25(11):1251-5. PMC: 2814061. DOI: 10.1038/nbt1346. View

4.
Eisen L, Lozano-Fuentes S . Use of mapping and spatial and space-time modeling approaches in operational control of Aedes aegypti and dengue. PLoS Negl Trop Dis. 2009; 3(4):e411. PMC: 2668799. DOI: 10.1371/journal.pntd.0000411. View

5.
Ellis A, Garcia A, Focks D, Morrison A, Scott T . Parameterization and sensitivity analysis of a complex simulation model for mosquito population dynamics, dengue transmission, and their control. Am J Trop Med Hyg. 2011; 85(2):257-64. PMC: 3144822. DOI: 10.4269/ajtmh.2011.10-0516. View