Renewal equations for vector-borne diseases

TL;DR

This paper develops a renewal equation framework for vector-borne diseases like dengue, capturing multi-stage transmission dynamics between humans and vectors to improve transmissibility estimates.

Contribution

It introduces a novel renewal equation model derived from first principles that accounts for multi-stage, human-vector transmission cycles.

Findings

Framework tracks multi-stage transmission over time and age.

Provides a basis for estimating R(t) and r(t) in vector-borne diseases.

Enhances understanding of complex transmission dynamics.

Abstract

During infectious disease outbreaks, estimates of time-varying pathogen transmissibility, such as the instantaneous reproduction number R(t) or epidemic growth rate r(t), are used to inform decision-making by public health authorities. For directly transmitted infectious diseases, the renewal equation framework is a widely used method for measuring time-varying transmissibility. The framework uses information on the typical time elapsing between an infection and the offspring infections (quantified by the generation time distribution), and R(t), to describe the rate at which currently infected individuals generate new infections. For diseases with transmission cycles involving hosts and vectors, however, renewal equation models have been far less used. This is likely due to difficulties in mechanistically defining generation times that can capture the complexity of multi-stage, human-vector relationships. Here, using dengue as an example, we provide general renewal equations that are derived from first principles using age-structured systems of coupled partial differential equations across human and vector sub-populations. Our framework tracks the multi-stage transmission cycle over calendar time and across stage-specific ages, resulting in governing renewal equations that quantify how the rate at which new infections are generated from existing infections depends on stage-specific processes. The framework provides a foundation on which to base inferential frameworks for estimating R(t) and r(t) for infectious diseases with multiple stages in the transmission cycle