Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases

Abstract

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination 'as a public health problem' when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models' predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020.

Fig. 1

Schematic of LF results. The results include: a) highlighting that heterogeneity in human exposure and intervention greatly alters time to elimination by Irvine et al. [11]; b) a description of the association between antigenaemia and the presence of adult worms by Jambulinga et al. [13]; and c) a Bayesian fitting methodology of a deterministic model including information on model inputs and outputs by Singh et al. [12]

Fig. 2

Schematic of onchocerciasis results. The results include a comparison of a stochastic individual-based model (ONCHOSIM) and a deterministic population-based model (EPIONCHO) and an investigation into the impact of systematic non-adherence in different endemicity settings by Stolk et al. [71]

Fig. 3

Schematic of schistosomiasis results. The results include: a) an assessment of the potential success of MDA in different scenarios using a deterministic modelling framework by Gurarie et al. [36]; and b) an investigation into the feasibility of elimination using an age-structured deterministic model by Anderson et al. [35]

Fig. 4

Schematic of STH results. The schematic includes results from: a) a deterministic transmission model by Truscott et al. applied to Ascaris, Trichuris and hookworm [41]; and b) a stochastic, individual based model of hookworm transmission by Coffeng et al. [40]

Fig. 5

Schematic of trachoma results. The schematic includes results from: a) a transmission model including consideration of immunity by Gambhir et al. [45]; and b) a statistical analysis of the most informative data for forecasting trends in prevalence by Liu et al. [44]

Fig. 6

Schematic of Chagas results. The schematic describes a new transmission model for Chagas disease used to analyse the consequences of varying standard assumptions about the transmission cycle by Peterson et al. [52]

Fig. 7

Schematic of HAT results. The results include a) quantitative estimates of the level of heterogeneity in human exposure and screening participation by Rock et al. [56]; and b) an assessment of strategies combining both human screening and tsetse control by Pandey et al. [55]

Fig. 8

Schematic of leprosy results. The results include: a) a transmission model fitted to national and regional data from India, Brazil and Indonesia to predict future trends in leprosy incidence by Blok et al. [59]; b) statistical modelling of regional case detection data from India by Brook et al. [60]; and c) a back-calculation method to investigate underlying infection dynamics and predict future incidence by Crump and Medley [61]

Fig. 9

Schematic of VL results. The results include: a) new estimates of epidemiological parameters by Chapman et al. [64]; and b). a qualitative investigation of the impact of different life history assumptions on transmission dynamics and intervention efficacy by Le Rutte et al. [65]