Modelling morbidity for neglected tropical diseases: the long and winding road from cumulative exposure to long-term pathology

Abstract

Reducing the morbidities caused by neglected tropical diseases (NTDs) is a central aim of ongoing disease control programmes. The broad spectrum of pathogens under the umbrella of NTDs lead to a range of negative health outcomes, from malnutrition and anaemia to organ failure, blindness and carcinogenesis. For some NTDs, the most severe clinical manifestations develop over many years of chronic or repeated infection. For these diseases, the association between infection and risk of long-term pathology is generally complex, and the impact of multiple interacting factors, such as age, co-morbidities and host immune response, is often poorly quantified. Mathematical modelling has been used for many years to gain insights into the complex processes underlying the transmission dynamics of infectious diseases; however, long-term morbidities associated with chronic or cumulative exposure are generally not incorporated into dynamic models for NTDs. Here we consider the complexities and challenges for determining the relationship between cumulative pathogen exposure and morbidity at the individual and population levels, drawing on case studies for trachoma, schistosomiasis and foodborne trematodiasis. We explore potential frameworks for explicitly incorporating long-term morbidity into NTD transmission models, and consider the insights such frameworks may bring in terms of policy-relevant projections for the elimination era. This article is part of the theme issue ‘Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs’.

Revisiting the impact of Schistosoma mansoni regulating mechanisms on transmission dynamics using SchiSTOP, a novel modelling framework

“…Although geometric means lead to biased estimators of the true mean, they have traditionally been used in the analysis of data from field studies [47]. We defined models as successful when they could i) reproduce a convex age-intensity curve with a typical peak of intensity of infection in adolescents, ii) bring the prevalence down of at least 20% of their pre-control levels after 10 years of MDA, and iii) result in a rebound of prevalence to values close to pre-control levels, after stopping MDA, even in low endemicity settings [1,[48][49][50]. After stopping MDA, we expect that the prevalence of infection in SAC should go back to pre-control levels within 20 years.…”

Introduction to the special issue: challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs

“…For these diseases, the association between infection and risk of long-term pathology is generally complex and poorly understood. The paper by Borlase et al in this volume discusses the challenges for determining the relationship between cumulative pathogen exposure and morbidity at the individual and population levels, drawing on case studies for trachoma, schistosomiasis and foodborne trematode infections, and explores potential frameworks for explicitly incorporating long-term morbidity into NTD transmission models [28]. These frameworks are crucial for quantifying burden of disease, and the paper by Ledien and colleagues presents a modelling pipeline from serological surveys to morbi-mortality models for Chagas disease (an IDM) to quantify disease burden in time and space [29].…”

“…For this issue, NTD modellers have contributed seven papers (50% of the contributions), including the potential of alternative treatment strategies for accelerating programmatic action towards attaining interruption of transmission [ 9 ]; the design of NTD impact surveys [ 12 ]; the usefulness of current and novel tools for diagnosis of helminthiases in areas ranging from low to high prevalence [ 17 ]; the feasibility and challenges posed by NTDs with long incubation periods and diagnostic delays [ 20 ]; the impact of human movement on reaching and sustaining elimination efforts [ 27 ]; the relationship between cumulative exposure to infection and development of severe morbidity [ 28 ], and the use of serological surveys and force-of-infection models linked to frameworks of disease progression for quantifying spatio-temporal patterns of disease burden accounting for uncertainty at all steps of the proposed pipeline [ 29 ]. The linkage between NTD models and data have certainly improved considerably over the past decade, but lessons learnt from model construction on what should be measured to better understand the impact of given control interventions on infection, transmission and morbidity have yet to filter through to the practical design of most monitoring and evaluation programmes.…”