2022-04-19 14:19:44

Talk Overview

  • Part 1: Introduction
  • Part 2: Past work
  • Part 3: Norovirus
  • Part 4: Influenza
  • Part 5: More stuff

Part 1 - Introduction

What we know

  • Inoculum dose is an important determinant of infection and vaccination outcomes.
  • For infection, higher dose is often associated with greater risk of infection (ID50) and more severe outcomes (LD50).
  • For vaccines, dose is thought to impact both immunogenicity/efficacy and side effects (morbidity).

What we don’t know

  • Not much is known about the impact of dose given infection.
  • There might be non-monotone relations between infection or vaccination dose and outcomes.
  • There might be trade-offs between vaccine availability/safety and efficacy.

Part 2 - Past work

Modeling dose and virus load

Adenovirus type 5 (ADV) infections of cotton rats.

\[ \begin{aligned} \dot U & = - bUV \\ \dot I & = bUV - dI \\ \dot V & = pI - cV \end{aligned} \]

Modeling dose and virus load

Modeling dose and immune response

Human parainfluenza virus (HPIV) in cotton rats

Human parainfluenza virus (HPIV) in cotton rats

Modeling dose and immune response

Modeling dose and immune response

\[ \begin{aligned} \textrm{Uninfected cells} \qquad \dot{U} & = - bUV \\ \textrm{Infected cells} \qquad \dot{I} & = bUV - d_I I \\ \textrm{Dead cells} \qquad \dot{D} & = d_I I \\ \textrm{Virus} \qquad \dot{V} & = \frac{pI}{1+s_F F} - (d_V V + k^{'}_{A}A + b^{'} U)V\\ \textrm{Innate response} \qquad \dot{F} & = p_F - d_F F + \frac{g_F (F_{max} - F)V}{V+h_V} \\ \textrm{B cells} \qquad \dot{B} & = \frac{F V}{FV+h_F} g_B B \\ \textrm{Antibodies} \qquad \dot{A} & = r_A B - d_A A - k_{A}AV \\ \end{aligned} \]

Modeling dose and immune response