Table 2 Summary of modelling techniques used, PCT diseases
Paper | Model | Fitted to data from: | Predictions tested? | Technical advances | Model accounts for | Next steps | |||
|---|---|---|---|---|---|---|---|---|---|
Vector/environment dynamics | Heterogeneity in risk | Access to interventions | |||||||
Lymphatic filariasis | |||||||||
Irvine et al. | Stochastic individual | Kenya and Sri Lanka | Yes | Heterogeneity in transmission and extinction dynamics greatly affects time to elimination | Deterministic vector dynamics. Single pool of vectors | Gamma distributed risk in exposure | Spectrum of access to repeat rounds of MDA and vector control, with cross-correlations with risk | Fit the model to intervention data and understand transmission dynamics at low densities | |
Jambulingam et al. | Stochastic individual | 35 villages in India. | Yes | Association between antigenaemia and presence of adult worms. | Deterministic vector dynamics. single pool of vectors | Age dependent, gamma distributed exposure | Individual’s treatment compliance is semi-systematic | Estimating vector infection thresholds. Estimating probability of elimination | |
Singh et al. | Deterministic | Data from 22 villages from Africa, South East Asia, and Papua New Guine | No | Bayesian fitting including information about model inputs and outputs | Deterministic vector dynamics. single pool of vectors | Negative binomial distribution of worms | Random | Estimating thresholds for true elimination. Further understanding of parameter uncertainty | |
Onchocerciasis | |||||||||
Stolk et al. | ONCHOSIM | Stochastic | Cameroon | Yes | Bringing the two models together and understanding differences in predictions | Deterministic vector dynamics. Single pool of vectors | Age dependent, gamma distributed risk | Age-dependent probability of receiving treatment. Lifelong compliance factor | The two models give different Mf intensities and prevalences after MDA, which needs to be investigated further |
EPIONCHO | Deterministic | Cameroon | Yes | Single vector compartment | Age- and sex-specific exposure to blackfly | Compliant and non-compliant groups | |||
Schistosomiasis | |||||||||
Anderson et al. | Deterministic | Kenya | Yes | Using an age-structured model to assess the feasibility of elimination, and comparing model predictions to reinfection data | Environmental reservoir, constant decay rate. No explicit consideration of snail dynamics | Variability in exposure as a function of age. Negative binomial distribution of worms | Treatment reduced worms by a given fraction in a given proportion of individuals, equivalent to random treatment | Better modelling of transmission by age, immunity, worm mating. Stochastic model | |
Gurarie et al. | Deterministic | Kenya | Yes | Investigating MDA success in different scenarios using a modelling framework | Snail transmission compartments | None | A fraction of adult worms are killed by each treatment | Consideration of snail dynamics. | |
Soil-transmitted helminthiasis | |||||||||
Coffeng et al. (hookworm only) | Stochastic,, individual | Vietnam | Yes | Developed WORMSIM, a new generalised framework for modelling transmission and control of helminths | Environmental reservoir | Gamma distributed total egg output, two scenarios: high or low variation in host susceptibility | Participation is either random, fully systematic or a mix. | Lifespan of eggs in the environment, MDA coverage over different age groups | |
Truscott et al. | Deterministic | India (Ascaris), St Lucia (Trichuris) and Zimbabwe (hookworm) | Yes (Ascaris only) | Fitting against multiple treatment rounds data | Pool of environmental infective material, exponential decay | Negative binomial distribution of worms in individuals | All individuals have a probability of receiving treatment | Understanding spatial and age heterogeneity, systematic non-compliance | |
Trachoma | |||||||||
Gambhir et al. | Deterministic | Tanzania and Gambia | No | Including MDA interventions into the modelling framework | None | None | A subset of the infected group are moved to the susceptible compartment | Validating against multiple datasets, better modelling of immunity | |
Liu et al. | Stochastic compartmental | Niger | No | Constructing a stochastic transmission model including different ways of modelling each observation by fitting to TF only or to TF, TI and PCR | None | None | All individuals have a probability of receiving treatment | Further fitting to intervention data. | |