Internet app could help controlling diseases, says Dr. Clair J. Standley

Malaria and schistosomiasis as often co-endemic and impact the same populations, with children at a greater risk of contracting both diseases

An article recently published on PloS Neglected Tropical Diseases entitled “Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis” presents the development of an internet-based app. The tool allows the user to input information regarding a population’s demographic structure (in any scale – country, province, district, etc.), some basic epidemiological parameters about schistosomiasis and malaria endemicity (schistosomiasis prevalence in the area, estimates of malaria mosquito bites, seasonality of malaria transmission, etc.), the kind of interventions in the place and when these interventions are held along the year. The tool was developed by assistant research professor at the Center for Global Health Science and Safety (CGHSS) at Georgetown University, Dr. Claire J. Standley, along with a group of Global Health researchers.

To know more about the subject, the Brazilian Society of Tropical Medicine’s press advisory talked to Dr. Standley. Find the full interview below.

BSMT: How did the idea of this application available on the internet?

Dra. Claire J. Standley: We wanted to create a tool that could be easily and freely accessible to all potential end-users, such as national or district disease control officers. As such, we thought putting it on the internet would be one way to allow for that, as well as gain feedback from the scientific and policy communities on the utility and applicability of the tool.

BSMT: How does it work?

Dra. Claire J. Standley: The tool allows users to input data on the demographic structure of their population (at any scale – it could be for a country, a province, a district, etc), some basic epidemiological parameters related to schistosomiasis and malaria endemicity (such as prevalence level of schistosomiasis in the area, estimated bite rate for malaria, and seasonality of malaria transmission), the type of interventions being used in the target area, and when these interventions are deployed during the year. The tool then uses a complex mathematical model, based on the best-available scientific data, to calculate whether there would be an epidemiological benefit (measured in terms of prevalence) to integrating interventions for malaria and schistosomiasis in the target area. It will also provide a recommendation as to how the interventions could be best integrated, from a timing perspective, for maximum epidemiological benefit.

BSMT: With regard to endemic and emerging infectious diseases, how can this tool help?

Dra. Claire J. Standley: We feel this tool presents an important proof-of-principle that it is possible to create user-friendly, easily accessible tools that put the best-available scientific evidence in the hands of people who can actually use the data to improve disease control. There is a lot of talk about the benefits of integration, but very few attempts have been made to make sure ahead of time that integration will actually have a significant benefit in a particular community or population. While the tool is currently just a prototype, focusing on one specific interaction between malaria and schistosomiasis, we are hoping to be able to expand it to incorporate additional aspects of integration, including cost and resource allocation, which are critical elements supporting integration efforts. Finally, while this particular tool is focused on malaria and schistosomiasis, in theory the same approach could be used to look at integration of any disease control efforts.

BSMT: 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. Could you tell me a little bit about this?

Dra. Claire J. Standley: As I describe above, we wanted to provide decision-makers and disease control officers at the country or even sub-national level with an evidence-based approach to disease integration. Rather than just integrating disease programs and hoping the outcome will be beneficial, our tool allows for a more deliberate and quantifiable approach. Moreover, it can also provide a benchmark against which to set monitoring and evaluation efforts, once integration is implemented. Our colleagues in sub-Saharan Africa and the Middle East were very generous in terms of providing data on disease control in their countries to help inform the development of the tool, and also contributed their review and suggestions to the overall concept and its applicability.

BSTM: Why were schistosomiasis and malaria chosen?

Dra. Claire J. Standley: We chose these two diseases for several reasons. First, they are frequently co-endemic, and also impact similar populations, with children at particular risk of both diseases. This means the rate of co-infection is high in many parts of the world. Secondly, schistosomiasis is a neglected tropical disease, which means that it doesnt receive the same amount of funding or international attention as more prominent diseases, such as malaria. We thought that demonstrating the benefits of integrating a neglected tropical disease like schistosomiasis with a more substantially-funded disease, like malaria, we might demonstrate opportunities for leveraging the global attention and resources of major global diseases for less well known ones, with positive benefit for controlling both.

BSTM: One of the major challenges in eliminating malaria, for example, is to determine which, where and how, disease control measures should be used. Will the tool be able to predict these variants?

Dra. Claire J. Standley: The tool relies on user inputs to determine the type of interventions available, since the idea is that it is predicting the potential benefit of integrating existing control programs, rather than assisting with designing new ones. However, the tool will provide guidance on the timing of existing interventions, within the context of integration, to determine whether these interventions can be optimized across the year for maximum epidemiological benefit for the target diseases.

BSTM: In the case of schistosomiasis, do you believe that the tool can help in the planning of policies, aiming to eliminate this parasitosis as a public health disease?

Dra. Claire J. Standley: For the prototype tool, we primarily focused on malaria prevalence as the tested outcome, with mass drug administration as the primary intervention available for schistosomiasis. The tool doesnt, for example, currently examine how malaria control interventions might also impact schistosomiasis infection or transmission. However, this is something we would very much like to include in the future, if given the opportunity (and funding!) to expand the tool and do some field-testing.

BSTM: The outcomes of decision-support tool also describe conditions under which integration may not have significant epidemiological benefits. Could you explain why?

Dra. Claire J. Standley: The first reason is that we set the threshold for significant epidemiological benefit as a reduction in malaria prevalence of 5% or greater. So, in some cases, there may be a 2% or 3% anticipated reduction in malaria prevalence, but we did not consider this to be significant. Perhaps more meaningfully, whether or not there will be any epidemiological benefits depends on the local context. Depending on the transmission season of malaria and the force of infection, treatment with schistosomiasis is going to have a stronger or weaker impact on the risk of infection with malaria for the population, and will contribute to determining the strength (if any) of the benefit of integration.

BSMT: What are the next steps?

Dra. Claire J. Standley: This work was funded through a Bill & Melinda Gates Foundation Grand Challenges Explorations grant, for which we are very grateful, as it allowed us to build the model and demonstrate the value of the tool as a proof-of-principle. However, we acknowledge that there is a lot more we could do to make the tool as useful as possible to the disease control community. As Ive mentioned, we would love to expand the tool to consider schistosomiasis in more detail, as well as add in a cost and resource allocation component, to look at the financial and resource savings that might be gained through integration. Finally, we would like to field-test the tool in different places that are co-endemic for schistosomiasis and malaria, to assess the extent to which our tools predictions align with reality, if its recommendations are followed in a real-life setting. We have partners in Uganda and Mali who are eager to collaborate with us, and so we are looking for funding sources that might be interested in supporting this additional work.

BSTM: Would you like to add anything you may find important and that was not approached above?

Dra. Claire J. Standley: Thank you for the opportunity to share more information abou…