Risk, epidemiology, and modeling for strategic disease control

www.epixanalytics.com

Risk, epidemiology, and modeling for strategic disease control

Francisco Zagmutt, DVM, MPVM, PhD

August 26, 2019

Hanoi, Vietnam

http://www.epixanalytics.com/

© EpiX Analytics LLC

Outbreak decision makingStages Support tools

Identify (short term)Key drivers of outbreak spread

Risk factor analysis, risk analysis, epidemiology, spatial analysis, network modeling, etc

Control (short to mid-term)How to limit spread

Risk analysis, epi modeling, cost-benefit, optimization

Plan (mid to long-term)How to manage/eradicate

Compartmentalization, zoning, risk analysis, decision analysis, risk management, public-private partnerships, certification

We’ll discuss key analytical support tools and relationship to ASF control in Vietnam


What is risk analysis?

Risk is usually defined as a triplet:1. What can go wrong (event)?

2. How likely is it (probability)?

3. How big is the impact?

Provides informative assessment of probability -> more informative than simply “possible” events/impacts

Opportunities (risks that we would like to happen) or benefits can also be quantified

Quantitatively balancing risk and benefits requires a common “currency”

From: http://goo.gl/0COq7T


The risk analysis process (e.g. OIE)

Modified from: Copyright © 2013 by Sidney Harris.

Hazard identification

Risk assessment

Risk management

Risk communication

Also useful to quantify benefits

Or to assess the tradeoffs between risks and benefits


Risk and trade

Uruguay Round resulted in WTO -> SPS Agreement: Goal: To avoid use of sanitary & phytosanitary measure as unjustified (“technical”) barriers to trade, while

recognizing the right of countries to protect human, animal and plant life and health

WTO international

standards

IPPC

(plant health)

CODEX

(food safety)

OIE

(animal health/zoonosis)


“Traditional” OIE risk assessmentOIE International Animal Health Code and International Aquatic Animal Health Code

Principles from Covello-Merkhofer system: Covello, V.T. and Merkhofer M.W. (1993). Risk assessment methods: Approaches for assessing health and environmental risks. Plenum Publishing, New York.

Entry (release) assessment

Exposure assessment Consequence assessment

Risk estimation


Qualitative Semi-quantitative

▪ Option when little data & time are available

▪ Translation of qualitative estimates (Probabilities and Impacts) to “scores”

▪ Easy to understand, appear more objective than qualitative approach

▪ But, statistically inconsistent and open to abuse

▪ Risk categories and combination matrices

▪ Structured approach to manage limited information, often in a short time

▪ But, many issues:o Can’t combine effect of uncertainty and variability

o No consensus on approach

o support discussion of EES estimates, OR

o combined using matrices

o Can’t combine with reasonable transparency and consistency

o Scaling statistically incorrect0.1-1 1 6 5 4 3 2

0.01-0.1 2 7 6 5 4 3

Probability 0.001-0.01 3 8 7 6 5 4

0.0001-0.001 4 9 8 7 6 5

0.00001-0.0001 5 10 9 8 7 6

Score 5 4 3 2 1

V.Low Low Medium High V. High

ImpactFig source: Peeler, E. J., et. al (2015), Animal Disease Import Risk Analysis – a Review of Current Methods and Practice. Transbound Emerg Dis, 62: 480-490.


Quantitative risk analysis (QRA)

▪ Quantify probability of adverse effect(s) and magnitude(s)

▪ Use probability theory, usually modeled with simulation

▪ Requires data

▪ Provides most objective assessment given sufficient data and meaningful approach

▪ No two models the same, sometimes requires entirely different approach

Ni

Number of infected

animals in exporting

country

A=Ni/Nt Probability individual animal has

disease

Nt

Total number of

animals in exporting

country

B

Probability that an animal will pass

inspection in exporting country given it

is infected

Expert opinions C

Probability that an animal will pass

inspection in importing country given it

is infected and has passed previous

inspection

n

Estimate of number of

animals that will be

imported from this

source

P=A*B*C Probability that an imported animal is

infected

N=Binomial(n,P)

Estimate of the number

of infected animals that

will be importedR1=1-(1-P)

n Probability that any infected animals will

enter the country

Expert opinions D

Probability that an animal will infect the

native livestock given that it is infected

and has passed inspections

R2=1-(1-D*P)n Total probability that disease will infect

native livestock from this route


Cobb SP, Pharo H, Stone M, Groenendaal H, Zagmutt F.J. (2015) Quantitative risk assessment of the likelihood of introducing porcine reproductive and respiratory syndrome virus into New Zealand through the importation of pig meat. Rev. Sci. Tech. 2015 Dec. 34(3):961-75

Prob > 0 outbreaks/yr Years to observe outbreak

0.3898% (0.0153% - 1.9075%) 1,226 (52 - 6,200)


ModelAssist – EpiX Analytics’ free risk analysis reference and training tool

https://modelassist.epixanalytics.com/

https://modelassist.epixanalytics.com/


Also many non-trade applications Farm-level risk assessment – e.g. bTB, paraTB, BSE

Farm/zone re-introduction risks

Risk-based surveillance

Re-importation (even during control phase)

Sanitary certification/zoning/compartments – e.g. ASF eradication Spain

Value of Information (VOII) - $ of additional data collection


Risk factor analysis NOT the same as risk analysis

Risk factor analysis – use historical data and statistical models (usually regression-based) to infer main disease drivers, e.g.

Are foot mats more effective than movement restrictions?

Is disease spread influenced by herd size?

Can environmental factors and seasonality affect disease?

Risk analysis/assessment – predict impact of (future) intervention: historical data (such as risk factor analysis) + structural changes in system. e.g.

Can I eradicate the disease if switch disinfecting protocol and increase herd size?

What’s the optimal combination of biosecurity measures?

Yearly probability of a reintroduction from neighboring country?


Risk factors for HPAI in farms from the Mekong Delta

Case-control study, 2006-2007

Significant risk drivers: none or only one vaccination,

visitors to farms,

presence of geese on farms

sharing of scavenging areas

with ducks from other farms


Disease spread (aka ‘epidemic’) modeling

(Better) understand: patterns of disease/infection, transmission drivers, value of additional data collection

Examine scenarios Experiments not feasible/too expensive in “real world” Test epi hypothesis

Predict: introduction / spread, persistence or eradication, scale of outbreaks, efficacy of control options e.g.

Vaccinate or cull Feasibility of disease compartment (e.g. Hagenaars TJ, Boender GJ,

Bergevoet RHM, van Roermund HJW (2018) Risk of poultry compartments for transmission of Highly Pathogenic Avian Influenza. PLoS ONE 13(11): e0207076)


State transition models

time

Nu

mb

er o

f in

div

idu

als

𝑑𝑆

𝑑𝑡= −𝛽𝑆𝐼

𝑑𝐼

𝑑𝑡= 𝛽𝑆𝐼 − 𝛾𝐼

𝑑𝑅

𝑑𝑡= 𝛾𝐼

S = #SusceptiblesI = #InfectedR = #Recovered

𝛽 = Transmission Coefficient𝛾 = Recovery Coefficient1/𝛽 =Average Latency1/𝛾 = Average Infectious Time

Susceptible (S) Infectious (I) Removed (R)

Probabilistic (Stochastic) results

Source: Espejo L.A., Costard S, Zagmutt F.J.

(2015) Modelling canine leishmaniasis spread

to non-endemic areas of Europe. Epidemiology

and infection. 143(9):1936-49Deterministic results


ASF spread modeling Madagascar Within-farm spread: Stochastic, individual-based, discrete time (1d), state transition model

Action by farmer when disease suspected:

At decision (detection and sale) time t,

removal of animals that farmers consider diseased

sale of others to traders

Susceptible(S)

Infected not infectious

(E)

Infectious No clinical signs

(L)

Infectious & Clinical signs

(I)

Dead or Recovered

(DR)

Removed (slaughtered)

Decision time & (1-specificity) Decision time & sensitivity

Pig

Clinical signs noticed and pig removed

Clinical signs not noticed and pig sold to traders

Se

1 -Se

Clinical signs perceived and pig removed

No clinical sign perceived and pig sold to traders

1 -Sp

Sp


Example tools available Epidemix, a shiny app from the RVC:

https://royalveterinarycollege.shinyapps.io/epidemi

x/ and paper by Muellner et al (2018)

R package for epidemic modeling: EpiModel

NAADSM, already applied to Vietnam: Lee, HS, Thakur, KK, Bui, VN, Bui, AN, Dang, MV, Wieland, B. Simulation of control scenarios of porcine reproductive and respiratory syndrome in Nghe An Province in Vietnam. Transbound Emerg Dis. 2019; 00: 1–9. https://doi.org/10.1111/tbed.13278

https://royalveterinarycollege.shinyapps.io/epidemix/

https://doi-org./10.1016/j.epidem.2017.12.003

http://www.epimodel.org/

https://doi.org/10.1111/tbed.13278


Keeping a disease-free subpopulation in affected country

Zoning and compartmentalization: “aim at establishing animal populations with a distinct health status based on effective separation of these populations and application of biosecurity measures to prevent the reintroduction of the infection”*

Development by OIE motivated by spread of H5N1 avian influenza in early 2000’s

Examples: ASF Zimbabwe, AI Thailand. AI Indonesia (zoning), several pig DZ Chile

*Zepeda C, Jones J.B., Zagmutt .FJ. (2008) Compartmentalisation in aquaculture production systems. Rev Sci Tech. 2008 Apr;27(1):229-41

Zoning Compartmentalization

Relies on geographical barriers Management/biosecurity in establishment(s)

Costs born by competent authority Born largely by producers

Surveillance key Surveillance, auditing and certification key

Industry structure can vary Usually vertical integration


Risk Management

A broad set of tools to identify, analyze and reduce, monitor and/or manage certain risks

RM tools for ASF control/eradication might include:

Risk management tool Object/effect on disease

Increase hygiene Reduces the spread of disease and its losses

Disease surveillance Detects disease, and reduces probability of spread

Disease spread modeling Analyzing key factors that can help reduce the spread and losses due to the disease

Reimbursement of producers for affected animals

Reduces the spread of disease, and reduce financial losses to individual producers

Other public-private partnership options


Certification and public-private partnership

Using latest disease/epidemiological knowledge and combination of best risk management tools/practices (hygiene, monitoring, etc.), minimum requirements can be defined for producers

Public sector:

- Regulatory environment and oversight- Technical expertise- Funding / loans

Private sector:

- Administering certification scheme- Offer insurance policies to reduce

producers’ financial losses

Objectives:- Improve public health - Access to international

trade:- Higher prices- Greater demand

- Increase producers’ income- Reduced financial risk to

producers

Public-private partnership >>


Conclusions Numerous tools for disease control: suitability depends on outbreak phases

(Identification, Control, Planning)

Tools used must be pragmatic and consider available resources, data, and information

Risk assessment, epi and modeling key to strategic disease control, can leverage information from surveillance, risk factor studies, socioeconomic studies, diagnostics, etc.

Compartmentalization might be option to consider for ASF in Vietnam

Risk management beyond just technical tools, also certification and public-private partnership important to long term strategic goals


Dr. Francisco J ZagmuttManaging Director

EpiX [email protected]

www.epixanalytics.com

mailto:[email protected]

http://www.epixanalytics.com/

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Risk, epidemiology, and modeling for strategic disease control