Table 1.
Models studied and their characteristic features.
| Model | Infection dynamics in humans | Infection dynamics in vectors | Implementation | Total number of parameters (number fitted)a | Intervention (MDA or VC) | Refs |
|---|---|---|---|---|---|---|
| EPIFIL | Deterministic, age-structured Partial differential equations (PDE) | Deterministic Ordinary differential equation (ODE) | A Monte Carlo-based Bayesian Melding framework using a binomial likelihood function to fit data | 28 (24) | Random MDA coverage, with reduction in the biting rate as observed due to VCb where applicable | Gambhir et al. (2010), Chan et al. (1998) and Norman et al. (2000) |
| LYMFASIM | Stochastic, individual-based micro-simulation | Deterministic non-linear | A chi-squared statistic based fitting method | 19 (3) | MDA compliance is neither completely random nor completely systematic, with reduction in the biting rate as observed due to VCc where applicable | Jambulingam et al. (2016), Plaisier et al. (1998) and Subramanian et al. (2004b) |
| TRANSFIL | Individual-based micro-simulation | Deterministic ODE | An Approximate Bayesian Computation based fitting procedure | 14 (3) | Systematic non-compliance of MDA, with reduction in the biting rate as observed due to VCc where applicable | Irvine et al. (2015) |
a
Those parameters that are not fitted to data have fixed values.
b
In EPIFIL, the impact of IVM in Pondicherry was modelled using the equation: MBRVC = MBR0 exp[a1t], with a1 < 0 for ∀t when VC is ON, otherwise a1 > 0. Details can be found in part A of the SI text describing EPIFIL.
c
In both LYMFASIM and TRANSFIL, IVM in Pondicherry was modelled as the observed reduction in the average MBRs during the period 1981–1985.