Project overview

Managing infectious disease requires rapid and effective response to support decision making. The decisions are complex and require understanding of the diseases, disease intervention and control measures, and the disease-relevant characteristics of the local community. Though disease modeling frameworks have been developed to address these questions, the complexity of current models presents a significant barrier to community-level decision makers in using the outputs of the most scientifically robust methods to support pragmatic decisions about implementing a public health response effort, even for endemic diseases with which they are already familiar.

Here, we describe the development of an application available for mobile devices or on the internet, with a simple user interface, to support on-the-ground decision-making for integrating disease control programs, given local conditions and practical constraints. The model upon which the tool is built provides predictive analysis for the effectiveness of integration of schistosomiasis and malaria control, two diseases with extensive geographical and epidemiological overlap, and which result in significant morbidity and mortality in affected regions. Working with data from countries across sub-Saharan Africa and the Middle East, we present a proof-of-principle method and corresponding prototype tool to provide guidance on how to optimize integration of vertical disease control programs. This method and tool demonstrate significant progress in effectively translating the best available scientific models to support practical decision making on the ground with the potential to significantly increase the efficacy and cost-effectiveness of disease control.

Citation and data use

A peer-reviewed paper describing this project is available here, with the following suggested citation:

Standley CJ, Graeden E, Kerr J, Sorrell EM, Katz R. Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis. PLOS Neglected Tropical Diseases, 2018; 12 (4): e0006328. DOI: 10.1371/journal.pntd.0006328

The Georgetown University Center for Global Health Science and Security has created and maintains this site for use by researchers, decision-makers and other interested parties. We encourage you to use the data from this site. If you do, though, please use the following citation:

Georgetown University Center for Global Health Science & Security. Integrated Neglected Tropical Disease Tool. Washington, DC: Georgetown University. Available at

This tool and the underlying dataset are available for use under the Creative Commons Attribution By License agreement (https://creativecommons.org/licenses/by/4.0/), with appropriate reference and acknowledgement of the original research team.