How modelling can help steer the course set by the World Health Organization 2021-2030 roadmap on neglected tropical diseases

Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.

A solid-phase microextraction gas chromatography-mass spectrometry technique for urinary metabolomics of human samples infected with schistosomiasis-Case of the Okavango Delta, Botswana

We present a GC-MS metabolomics workflow for analyzing metabolites in urine samples infected with schistosomiasis. Schistosomiasis, a neglected tropical disease, affects 85% of the global population, with the majority residing in Sub-Saharan Africa. The workflow utilized in this study involved the utilization of the AMDIS freeware, Metab R for pre-processing, and multivariate statistical classification through partial least squares-discriminant analysis (PLS-DA). This classification aimed to categorize volatile metabolites found in urine samples from humans infected with schistosomiasis. All samples were collected from individuals in Botswana. A solid-phase microextraction-fused silica fiber was used to adsorb volatile metabolites from the urine samples and inserted into the GC-MS injection port for data acquisition. The acquired data were then subjected to AMDIS auto-deconvolution, Metab R pre-processing, and statistical evaluation for metabolite mining. A total of 12 metabolites, including 3-chloropropionic acid and heptadecyl ester with an AMDIS match factor of 96% at an approximated amount of 0.35% and cyclohexylamine with an AMDIS match factor of 100% and approximated amount of 0.39%, were identified. PLS-DA was used for the classification of the metabolites. The method showed good sensitivity and specificity as indicated by the receiver operating characteristic measured by the areas under the curves. Results indicated that metabolomics is a useful tool for mining metabolites because of the variance in metabolite composition of infected and non-infected urine samples.

Public Health Policy Pillars for the Sustainable Elimination of Zoonotic Schistosomiasis

Schistosomiasis is a parasitic disease acquired through contact with contaminated freshwater. The definitive hosts are terrestrial mammals, including humans, with some Schistosoma species crossing the animal-human boundary through zoonotic transmission. An estimated 12 million people live at risk of zoonotic schistosomiasis caused by Schistosoma japonicum and Schistosoma mekongi, largely in the World Health Organization’s Western Pacific Region and in Indonesia. Mathematical models have played a vital role in our understanding of the biology, transmission, and impact of intervention strategies, however, these have mostly focused on non-zoonotic Schistosoma species. Whilst these non-zoonotic-based models capture some aspects of zoonotic schistosomiasis transmission dynamics, the commonly-used frameworks are yet to adequately capture the complex epi-ecology of multi-host zoonotic transmission. However, overcoming these knowledge gaps goes beyond transmission dynamics modelling. To improve model utility and enhance zoonotic schistosomiasis control programmes, we highlight three pillars that we believe are vital to sustainable interventions at the implementation (community) and policy-level, and discuss the pillars in the context of a One-Health approach, recognising the interconnection between humans, animals and their shared environment. These pillars are: (1) human and animal epi-ecological understanding; (2) economic considerations (such as treatment costs and animal losses); and (3) sociological understanding, including inter- and intra-human and animal interactions. These pillars must be built on a strong foundation of trust, support and commitment of stakeholders and involved institutions.