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Dynamical Fever

Mutono Nyamai,

Centre for Epidemiological Modelling and Analysis,

University of Nairobi

Paul G Allen School for Global Health,

Washington State University

MMED 2025

000

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Goals

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To access the tutorial

ICI3D::DynamicalFever()

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Summary

This model world represents Julie’s ideal world

Everyone has a dog (same population size for dogs and humans)

All dogs and all people get along and spend most of their time at the dog park (ie, contacts are well mixed)

For the most part, everyone is super happy in DAIDD county so the population doesn’t change throughout the year; however, there is one dog whose owner takes it to visit grandma every year over Christmas break, and that dog always comes back on 1 Jan infected with DF

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Part 1: Epidemics Dynamics

In the first two years, the outbreaks were similar ~800 cases in humans, dogs
In 2011, there was no outbreak (1 case in humans, 1 case in dogs)
In 2012, there was an outbreak in both humans and dogs.

Based on the data (2009-2012), what have you learnt about DF?
What might determine the differences in epidemic size and duration from year to year?
Why might the epidemic die out each spring?

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Part 2: Introduction of a veterinary vaccine

In 2011, vaccine is approved for dogs
By 2013, 40% of dogs had been vaccinated
Health department employees torn between the effectiveness of the vaccine in the reduction in the 2013 outbreak
By 2014, 50% of dogs had been vaccinated, but people were still debating on the vaccine effectiveness.
What arguments might each team make to support their findings?
What additional info would be useful to help determine to what extent the vaccine is responsible for the difference in cases before and after vaccine introduction?

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Part 3: Introduction of a human vaccine

By beginning of 2016, 80% of the people in Daaid county had been vaccinated, with dog vaccine falling below 20%
At the end of 2016, there was an outbreak in dogs (~800 cases), and ~150 human cases

What potential DF transmission patterns could explain all of the observed data?
What would you advise the AD to do in order to prepare for the 2017 DF season?

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Part 4&5: Moving forward & Vaccination outcomes

Decide on target levels of vaccination for dogs and people in 2017
Keep in mind that it is unlikely that you will be able to achieve 100 percent vaccination of either population.

Why does vaccinating 50 percent of dogs appear to eliminate cases in dogs when vaccinating 50 percent of people only reduces the number of human cases by about 50 percent
What do you think vaccinating 50 percent of dogs would do to the number of human cases, on average

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Assumptions

Dogs can infect dogs and people, and contact/transmission rates are the same to both
People can’t infect dogs or people
Infected/infectious period is 1 week; no incubation or latency
The vaccine is 100% efficacious
Immunity (natural or vaccine-derived) is short-lived; everyone’s always completely susceptible again by 1 Jan
No differences of any kind between individuals, other than the one that always starts the epidemic
DF doesn’t do any long-term damage to humans or pups; it just turns them blue for a week. So it’s not desirable… but not killing anyone!

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Model specification

The model world is implemented as:

Stochastic
Discrete time
Compartmental model

�Specifically, a Reed-Frost-like chain binomial:

Non-overlapping generations

R₀ = 2

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Model taxonomy

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This presentation is made available through a Creative Commons Attribution-Noncommercial license. Details of the license and permitted uses are available at� http://creativecommons.org/licenses/by/3.0/

Attribution:

Clinic on Meaningful Modeling of Epidemiological Data

Source URL:

For further information please contact figshare@ici3d.org.