Week 3: Discussion on model interventions

[Wirichada Pan-ngum]

What intervention(s) you are considering in your modelling and how it will be added to the model structure. What are the characteristics of the intervention(s) (e.g. coverage, efficacy etc.) (10 points)

[Zarni Lynn Kyaw]

Building up from Week 2 Discussion, to answer the first question,

What intervention(s) you are considering in your modeling and how it will be added to the model structure?

Safe sex practices: This can be represented in the model by reducing the transmission rate, beta.

Needle safety: This can be represented in the model by reducing the rate of progression from the exposed to the infected compartment, delta.

Universal precautions: This can be represented in the model by increasing the recovery rate, gamma.

I can add these interventions to the model structure by modifying the equations for beta, delta, and gamma. I could reduce beta by a factor of 0.5 to represent the effect of safe sex practices, same could be done for needle safety and universal precautions. By playing around with the model and if I incorporate cost effectiveness analysis into the model, I could calculate ICER and provide best-buy to the policy makers.

What are the characteristics of the intervention(s)?
The characteristics of the interventions for hepatitis prevention and control vary depending on the specific intervention.

Coverage: This is the proportion of the population that is reached by the intervention. In my case, it could be safe-sex practices like condom distribution coverage.

Efficacy: This is the effectiveness of the intervention in treating the disease. In SEIR model, effectiveness of drugs for hepatitis can influence Recovered population.

Cost-effectiveness: This is the cost of the intervention compared to the benefits it provides. For example, Mass Media Campaign cost-effectiveness can be a deciding factor when comparing with programs like needle safety programs.

Acceptability: This is the willingness of people to use the intervention. This factor depend on the culture and context of the region.

If I were to design the intervention, I would make sure to consider cost-effectiveness and acceptability in Myanmar as I’ve seen some well-designed program with high efficacy and coverage fail because it’s either too expensive or not being accepted by the community.

So, I would focus on needle safety and universal precautions for a hepatitis program in Myanmar. Plus HIV program already did a good job of safe sex practices in Myanmar.

[Wirichada Pan-ngum]

So if you are going to put in those controls in the model, how would you do it? My suggestion is to have a term multiply to beta. That term could consist of parameters informing the coverage and efficacy of the intervention. That term is taking the value between 0 and 1, for example we can say
let x be the effective coverage of needle sharing prevention scheme.
Let beta be the transmission rate of Hep C

(beta)(1-x) is updated transmission as a result of intervention

[Wirichada Pan-ngum]

Some good literatures on this:

Cousien A, Tran VC, Deuffic-Burban S, Jauffret-Roustide M, Dhersin JS, Yazdanpanah Y. Dynamic modelling of hepatitis C virus transmission among people who inject drugs: a methodological review. J Viral Hepat. 2015 Mar;22(3):213-29. doi: 10.1111/jvh.12337. Epub 2014 Oct 1. PMID: 25270261.

Fraser H, Mukandavire C, Martin NK, Goldberg D, Palmateer N, Munro A, Taylor A, Hickman M, Hutchinson S, Vickerman P. Modelling the impact of a national scale-up of interventions on hepatitis C virus transmission among people who inject drugs in Scotland. Addiction. 2018 Nov;113(11):2118-2131. doi: 10.1111/add.14267. Epub 2018 Jul 10. PMID: 29781207; PMCID: PMC6250951.

Unfortunately I have not got access at the moment. I will try to see if I can later. Or if you can please share and we can discuss this online or onsite.

[Zarni Lynn Kyaw]

Dear Arjan,

I’m available for both online or onsite discussion.

Your comment is very helpful. Here is my thought taking account of your suggestion, let me know my understanding is correct.

If the coverage of the needle safety intervention is 50% and the efficacy is 90%, then the updated transmission rate would be:

beta = beta_0 * (1 – 0.5 * 0.9) = 0.45 * beta_0

I can adjust the values of x and e to represent different levels of coverage and efficacy. I can also explore the effects of different combinations of interventions.

At the same time a few more question pop up in my mind

How does the intervention affect the transmission rate?
A needle safety intervention might reduce the transmission rate by reducing the number of people who share needles. A universal precautions intervention might reduce the transmission rate by reducing the risk of exposure to contaminated blood.

How does the intervention affect the recovery rate?
A treatment intervention might increase the recovery rate by helping people to recover from the infection more quickly.

Are my thought along the same line of your suggestions?

[Hazem Abouelfetouh]

What intervention(s) you are considering in your modelling and how it will be added to the model structure. What are the characteristics of the intervention(s) (e.g. coverage, efficacy etc.)

We can incorporate many interventions into the SEIR (Susceptible-Exposed-Infectious-Recovered) model for hepatitis C and adapt the model to evaluate their impact and simulate the transmission dynamics of hepatitis C in the population.

Screening Campaigns and Preventive Measures (e.g. Blood Safety and Needle Exchange Programs) can have an impact on the transition from the susceptible (S) to the exposed (E) compartment including the probability of being diagnosed as exposed based on screening.

Public Awareness and Education programs can have an impact on population behavior that reduces the risk of moving from exposed (E) to infected (I) compartment and can be added as a factor for σ (sigma).

Treatment Programs and Treatment Adherence Monitoring can impact treatment success and increase the rate at which infectious individuals move to the recovered compartment and can be added as a factor for γ (gamma).

[Panyada Cholsakhon]

*What intervention(s) you are considering in your modelling and how it will be added to the model structure?

Since I chose dengue as my topic, I would like to describe about vaccine that I chose for the intervention of my modelling.

Providing dengue vaccine to the susceptible individual will help induce immunity against the dengue virus in those who receive it. This immune can prevents or reduces the severity of dengue infection in vaccinated people. Therefore it can lower the incidence of the disease, decrease its spread within communities, and reduce the burden of dengue-related illness and complications.

The model structure that I discuss in the previous assignment was SEIR. Providing vaccine will add the intervention to the susceptible population. When the intervention added, it can either slowdown or stop the transition from S to E which means that the vaccine can finally helps protect people to be infected and also can help infected people to recover faster and decrease by severity of the disease.

*What are the characteristics of the intervention(s) (e.g. coverage, efficacy etc.)?

From the CDC, the Dengvaxia dengue vaccine requires three doses administered subcutaneously and given 6 months apart for full protection.
The characteristic of the intervention in my opinion can be vaccine efficacy (the effectiveness of a vaccine in preventing disease or how much transmissibility can be reduced), coverage rates (the proportion of the target population that receives the vaccine), and cost-effectiveness (Cost-effectiveness typically assess by weighing the costs of vaccination against the benefits gained in terms of prevented illnesses, hospitalizations, deaths, and associated economic losses.)

*Reference:
https://www.cdc.gov/dengue/vaccine/hcp/schedule-dosing.html

[ABDILLAH FARKHAN]

I personally argue that in addition to determining suitable intervention programs, we also need to classify the targets of intervention because the susceptible populations (and the populations at risk) for mpox disease are specific. Thus, we cannot make a blanket claim that everyone is at risk.

In the context of the mpox epidemic in Indonesia, we highlight that mpox is considered a sexually-transmitted illness where the disease morbidity is high among the homosexual and bisexual groups but low in the heterosexual population. Here are the possible interventions we could apply to control mpox epidemic which could be included in the models:

To prevent people from becoming susceptible to virus exposure
– Interventions: health promotion with particular focus on chain transmission, including safe sex practice
– Characteristics of intervention: coverage, effectiveness

For the susceptible population, including those who may have been exposed:
– Interventions: two-dose mpox vaccination, integrated surveillance (HIV-mpox), contact tracing,
– Characteristics of intervention: coverage, vaccine efficacy, surveillance sensitivity, cost-effectiveness

To increase recovery rate and reduce fatality
– Interventions: intensive treatment and health facility-based isolation (not home isolation)
– Characteristics of intervention: coverage, drug efficacy

[Author]

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