Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis

Abstract

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 on the internet, including from mobile devices, 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.Author summaryDesigning and implementing effective programs for infectious disease control requires complex decision-making, informed by an understanding of the diseases, the types of disease interventions and control measures available, and the disease-relevant characteristics of the local community. Though disease modeling frameworks have been developed to address these questions and support decision-making, the complexity of current models presents a significant barrier to on-the-ground end users. The picture is further complicated when considering approaches for integration of different disease control programs, where co-infection dynamics, treatment interactions, and other variables must also be taken into account. Here, we describe the development of an application available on the internet with a simple user interface, to support on-the-ground decision-making for integrating disease control, 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. This proof-of-concept method and tool demonstrate significant progress in effectively translating the best available scientific models to support pragmat...

Have there been efforts to integrate malaria and schistosomiasis prevention and control programs? A scoping review of the literature

“…Given the potential for co-infection in endemic areas, there is an opportunity for local integrated control strategies for malaria and schistosomiasis, such as combining activities (i.e., IPTsc for malaria and MDA for schistosomiasis) to optimize the use of existing resources as well as increase intervention access and/or coverage for an improved outcome. There is currently a proof-ofprinciple modeling tool published by Standley et al 2018, designed for local public health officials or policy makers to provide guidance on how and when to integrate malaria and schistosomiasis control methods-however, only for LLIN distribution, indoor residual spraying, and schistosomiasis MDA [21]. There is also evidence of other types of integration.…”

Knowledge, Attitudes and Practices Among Adult Malaria Patients Co-Infected with Opportunistic Intestinal Coccidian Parasites in Fundong Health District, Northwest, Cameroon: A Cross Section Study Design

“…The overlapping infections have been reported (Njunda et al, 2016), and the risk of death is higher in patients with multiple infections of malaria and intestinal infections (CDC, 2021). There is evidence to support the integration of diseases (Standley et al, 2018) control and prevention in different settings. In Cameroon the national programme for the control of malaria and the national deworming programmes both committed to eradicate malaria and intestinal diseases however, this does not include intestinal diseases related to coccidian infection.…”

Enhancing Capability of Surveillance actors in using Evidence Informed Decision Making on COVID-19 Response in Nigeria.

“…Managing infectious diseases requires a rapid and effective response to support decision-making. The decisions are complex and require an understanding of the disease, disease intervention and control measures, and the disease-relevant characteristics of the local community (Standley et al, 2018). Disease surveillance improves the flow of information needed to monitor disease spread, and to evaluate the effectiveness of control and preventive measures.…”