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

<div><p>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.</p><p>Author summary</p><p>Designing 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 pragmatic decision-making on the ground, with the potential to significantly increase the impact and cost-effectiveness of disease control.</p></div

Schematic framework for malaria schistosomiasis co-infection model to evaluation of integrated control programs.

<p>(A) Individuals enter the model either disease-free (left) or infected with schistosomiasis (right). Diagram shows transition between disease states of the modeled population (arrows). Praziquantel MDA is assumed effective in curing schistosomiasis in all individuals with access to treatment. Blue = malaria, red = schistosomiasis (schisto.), and purple = co-infection. (B) The model performs two simulations for each user submission to the decision support tool. The current (non-integrated) simulation delivers control measures with timing specified by the user. The Integrated simulation aligns all interventions to occur at the same time. For non-seasonal transmission, interventions are aligned to all occur at the earliest intervention time specified by the user. For seasonal transmission, all control measures are delivered one month prior to the start of malaria season. Malaria prevalence estimates for these two simulations are compared to evaluate the potential benefits of integration.</p

Key time points on the simulation timeline.

<p>The methods used to determine time points for both seasonal and continuous malaria transmission patterns.</p

Example of decision-focused summary and modeling results.

<p>(A) Results page summary for a region where a non-integrated approach is recommended. Results page summarizes estimated difference in the prevalence between intervention strategies in tabular and graphical formats. (B-C) Model results for malaria prevalence (green) and schistosomiasis prevalence (blue) used as the basis for the comparison in (A) by averaging the prevalence across the terminal year of each model run.</p

User-assigned parameters for malaria transmission and interventions.

<p>User-assigned parameters for malaria transmission and interventions.</p

Parameters determining symptomatic malaria cases and access to treatment.

<p>Parameters determining symptomatic malaria cases and access to treatment.</p

Example of decision-focused summary and modeling results.

<p>(A) Results page summary for a region where an integrated approach is recommended. Results page summarizes estimated difference in the prevalence between intervention strategies in tabular and graphical formats. (B-C) Model results for malaria prevalence (green) and schistosomiasis prevalence (blue) used as the basis for the comparison in (A) by averaging the prevalence across the terminal year of each model run.</p

Model parameters that determine malaria infection probability (<i>p</i>_<i>m</i>(<i>t</i>)).

<p>Model parameters that determine malaria infection probability (<i>p</i>_<i>m</i>(<i>t</i>)).</p

Example modeling results for a very high transmission region with seasonal malaria transmission.

<p>Graphs shows prevalence of malaria (green) and schistosomiasis (blue) at baseline (prior to time zero) and in the months following sequential, non-integrated (A) or integrated interventions (B). Arrows mark the timing of malaria and schistosomiasis control measure distribution.</p

Example distribution timeline from the results page.

<p>Summary table and timeline of the current (non-integrated) and recommended distribution times for control measures. (A) This example corresponds to a region were integration of control programs is predicted to reduce malaria prevalence compared to the current distribution strategy. (B) Summary table and timeline for a region where integration is not recommended based on modeling results. Distribution is matched to the current intervention strategy provided by the user.</p