Sensitivity analysis in an Immuno-Epidemiological Vector-Host Model

TL;DR

This paper introduces a sensitivity analysis framework for an immuno-epidemiological vector-host model, linking within-host immune dynamics to population-level disease spread, exemplified through Rift Valley Fever.

Contribution

It develops a novel sensitivity analysis approach for models connecting within-host immune parameters to epidemiological outcomes, aiding disease control strategy design.

Findings

A 1% increase in pathogen growth rate raises R0 by up to 8%.

Within-host immune changes significantly affect epidemic size.

Control strategies targeting pathogen growth can effectively reduce disease prevalence.

Abstract

Sensitivity Analysis (SA) is a useful tool to measure the impact of changes in model parameters on the infection dynamics, particularly to quantify the expected efficacy of disease control strategies. SA has only been applied to epidemic models at the population level, ignoring the effect of within-host virus-with-immune-system interactions on the disease spread. Connecting the scales from individual to population can help inform drug and vaccine development. Thus the value of understanding the impact of immunological parameters on epidemiological quantities. Here we consider an age-since-infection structured vector-host model, in which epidemiological parameters are formulated as functions of within-host virus and antibody densities, governed by an ODE system. We then use SA for these immuno-epidemiological models to investigate the impact of immunological parameters on population-level disease dynamics such as basic reproduction number, final size of the epidemic or the infectiousness at different phases of an outbreak. As a case study, we consider Rift Valley Fever Disease (RFVD) utilizing parameter estimations from prior studies. SA indicates that 1% increase in within-host pathogen growth rate can lead up to 8% increase in R0; up to 1% increase in steady-state infected host abundance, and up to 4% increase in infectiousness of hosts when the reproduction number R0 is larger than one. These significant increases in population-scale disease quantities suggest that control strategies that reduce the within-host pathogen growth can be important in reducing disease prevalence.