The NTD Modelling Consortium has been established to contribute to the Filling the Gaps project with expertise in epidemiological modelling to provide qualitative and quantitative insights across the highly diverse range of infections which are listed in the London Declaration.
The aim of the 2020 goals modelling project within the NTD Modelling Consortium is to develop robust, validated analyses of transmission dynamic models of 9 of the 10 diseases in the London Declaration – Guinea worm is excluded due to near extinction. The activity is focused on the core objective of the "Filling the Gaps – Operational Research to Ensure the Success of the Neglected Tropical Disease Control and Elimination Programs" research, led by the Task Force for Global Health:
"To improve the access of endemic populations to interventions targeting neglected tropical diseases (NTDs) and to ensure the effectiveness and impact of these interventions."
In particular, the models and results generated by the NTD Modelling Consortium will test and help to improve the effectiveness of the interventions in their aim of achieving the 2020 goals.
The objectives for all diseases are:
- Assess where the 2020 goals can be achieved using current strategies: This assessment will be updated throughout the grant, starting with initial insights and extending to results from more rigorously validated model comparisons.
- Identify additional strategies which will help accelerate the achievement of these goals: This analysis will be responsive to the ongoing developments in this area, including availability of new diagnostics, treatments and control strategies.
- Identify knowledge gaps: For some diseases the limitations of available data may not allow sufficient specification of the models to address the primary objective. In this case the modelers will work with programs and researchers to design studies or alter existing M&E strategies to fill these gaps in our knowledge and improve the quality of modelling analysis and prediction.
To build scientific robustness, these analyses will be performed by at least two modelling groups per disease. The different groups will use complementary approaches to address the same questions and work together to develop a consensus view on their results. The modelling groups for each disease were selected by open scientific competition and independent peer review.
Preventative Chemotherapy diseases
Three modelling approaches will be explored: population-level, deterministic, partial differential equation (PDE)-based model initially developed as EPIFIL (Edwin Michael, Notre Dame), a stochastic microsimulation model LYMFASIM (Wilma Stolk, Erasmus MC) and a new deterministic model for the transmission of LF (Deirdre Hollingsworth, Warwick).
Two already available and well-validated models will be employed: a stochastic, individual-based model, used for decision support by APOC and previously the OCP (ONCHOSIM, Wilma Stolk, Erasmus MC) and a deterministic population model developed at Imperial College London, based on detailed analysis of underlying biological mechanisms (EpiOncho, Maria-Gloria Basanez, Imperial).
Two modelling approaches will be developed: stochastic multicompartmental models for each species, based on different underlying assumptions on the distribution of aggregation of infection, mating probability, density dependence, and age-related host factors (Roy Anderson, Imperial and Charles King, CWRU/Yale), and the individual-based stochastic microsimulation models (Roy Anderson, Imperial / Charles King, CWRU/Yale / Erasmus MC)
Two modelling approaches will be developed: age-structured deterministic models and individual stochastic simulation models (Roy Anderson, Imperial) and an individual-based microsimulation model based on the WORMSIM environment (Sake de Vlas, Erasmus MC)
Intensified Disease Management diseases
Two groups (Andrew Dobson, Princeton and Bruce Lee, Johns Hopkins/Yale) will develop deterministic models parameterized to reflect regional heterogeneities that predict the drivers of Chagas transmission.
Three modelling approaches will be developed: statistical/empirical (Graham Medley, Warwick), stochastic compartmental or mathematical models (Travis Porco, UCSF/Yale), and the individual-based stochastic microsimulation model SIMCOLEP (Jan Hendrik Richardus, Erasmus MC).
Two modelling approaches will be developed: an individual-based microsimulation of the Tübingen compartmental model, extended with age of the host and health-systems related processes (Sake de Vlas, Erasmus MC), and a deterministic model combining local into regional scenarios (Graham Medley, Warwick).