Sheep update

Discussion goals

Sheep study progress
Objectives under current funding
How to package / approach existing data

What we've done

(2013-Present)

1. Basic description

Cassirer et al. 2013 JAE

2. Acquired immunity

Plowright et al., 2013 PLoS One

3. Social compartmentalization

Manlove et al., 2014 PRSB

4. Persistence / phase transition

Manlove et al., almost in review

5. Who carries in the wild?

Plowright et al., in prep

6. New strain produces all-age die-off in infected herd

Cassirer et al., in prep

Current MAF Objectives

Objective 1

-ID groups with poor outcomes (Manlove et al., PRSB)

-Inference on carriage rates through time

-ID individual M.ovi carriers (Raina @ Lostine; Frances @ Asotin, Black Butte, Mtn View

- Ewe-group structure / contact patterns (Manlove field data)

-> "vert" vs. "horizontal" transmission

-> force of infection from different demographic groups

- Risk factor analysis

Objective 2: Mechanistic models of carrier etiology

Objective 3: Modeling control strategies

(Plowright et al., 2012 - May 2016)

Sheep society in the wild

Overview

Goal

Quantify social dynamics of bighorn nursery groups with disease

Associations (who's in a group together)
Interactions (who touches who)
Disease progression (symptoms + mortalities)

Methods

Marked/sampled as many animals as possible overwinter (Frances)
Followed up from ~April 20 - July 15
- Locate everybody every day
- Determine lamb status
- Focal follows & scan samples
- Symptoms & carcass recovery

Data collected

Pop	Year	# Marked (total)	# relocations	# follows / ID'd animals followed	Disease Status
Asotin	2013	12 / 32	554		infected no transmission
	2014	17 / 25	1135	191 / 883	infected no transmission
	2015	23 / 28	1444	239 / 1095	infected no transmission
Black Butte	2013	4 / 13	124		Outbreak
	2014	11 / 13 (all recognizable)	718	155 / 581	New strain
Mtn View	2015	13 / 30	516	191 / 548	infected / no transmission
Breaks	2015	31 / 300	303		infected / no transmission

Asotin sampling intensity, 2014

Association networks

(edges = 2 animals in same place at same time)

Interaction network

(direct contacts)

Associations always high, but non-lamb interactions rare

Fission-fusion dynamics

Group sizes and structures

Lamb epidemics

Can we use social network data to draw inference about strength of different potential reservoirs?

Naive model

R = 1 + \lambda\nu + f(1-f)(\lambda\mu)^{2}

R = 1 + \lambda\nu + f(1-f)(\lambda\mu)^{2}

\lambda = \frac{ln[Y(t)]}{t} = \text{rate of exp.growth}

\lambda = \frac{ln[Y(t)]}{t} = \text{rate of exp.growth}

\nu = \text{serial interva: latent + infectiousl}

\nu = \text{serial interva: latent + infectiousl}

f = \text{ratio of mean of latent period to serial interval}

f = \text{ratio of mean of latent period to serial interval}

\nu = \text{Serial interval = latent + infectious}

\nu = \text{Serial interval = latent + infectious}

Slope ~ 0.32

Serial interval ~ 25 days (10 + 15)

f ~ 2/5

P(mort) \sim \beta_0 + \beta_{Ewes} X_{ewes} + \beta_{YrEwes}X_{YrEwes} + \beta_{DryEwes}X_{DryEwes} + \epsilon

P(mort) \sim \beta_0 + \beta_{Ewes} X_{ewes} + \beta_{YrEwes}X_{YrEwes} + \beta_{DryEwes}X_{DryEwes} + \epsilon

X_{Ewes} = \text{Sum of edgeweights to infected ewes}

X_{Ewes} = \text{Sum of edgeweights to infected ewes}

P(mort) \sim \beta_0 + \beta_{Ewes} X_{ewes} + \beta_{YrEwes}X_{YrEwes} + \beta_{DryEwes}X_{DryEwes} + \epsilon

P(mort) \sim \beta_0 + \beta_{Ewes} X_{ewes} + \beta_{YrEwes}X_{YrEwes} + \beta_{DryEwes}X_{DryEwes} + \epsilon

X_{Ewes} = \text{Sum of edgeweights to infected ewes}

X_{Ewes} = \text{Sum of edgeweights to infected ewes}

Can we use information on time-varying contact rates to separate infection risk from chronic adults vs. infection risk from acute lambs?

Bipartite networks

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