Mingkhwan Vithayaveroj

[Mingkhwan Vithayaveroj]

What intervention(s) you are considering in your modelling?

– I chose the lockdown intervention. It is the intervention that most governments around the world chose to deal with the Covid-19 pandemic and is not difficult to understand for a simple model one.

How it will be added to the model structure?
– by the different beta for the SIRD model, I pick the base number from Luangwilai et al. (2020), they have beta = 0.2169, then I assume that lockdown has 20 and 25 per cent efficiency in the same situation. so beta will decrease with 20 and 25 per cent.

What are the characteristics of the intervention(s)?
– lockdown is the intervention that targets to decrease beta.

[Mingkhwan Vithayaveroj]

#Infection and death rate of COVID-19 with and without intervention.
#I think I will choose lockdown intervention for this report.

library(deSolve)

SIRD.dyn <- function(t,var,par) {

S <- var[1]
I <- var[2]
R <- var[3]
D <- var[4]

N <- S+I+R+D

beta <- par[1]
gamma <- par[2]
alpha <- par[3]

dS <- -beta*S*I/N
dI <- beta*S*I/N – gamma*I – alpha*I
dR <- gamma*I
dD <- alpha*I

list(c(dS,dI,dR,dD))}

#There will be 2 groups of these below data for with and without intervention.

beta <- 0.2169 #infection rate
gamma <- 0.0267 #recovery rate
alpha <- 0.00065353 #death rate

SIRD.par <- c(beta,gamma,alpha)
SIRD.init <- c(2762, 1, 0,0)
SIRD.t <- seq(0, 500, by = 1)
SIRD.sol <- lsoda(SIRD.init,SIRD.t,SIRD.dyn,SIRD.par)

TIME <- SIRD.sol[,1]

S <- SIRD.sol[,2]
I <- SIRD.sol[,3]
R <- SIRD.sol[,4]
D <- SIRD.sol[,5]

N <- S+I+R+D

library(ggplot2)

SIRD.sol2 <- as.data.frame(SIRD.sol)

ggplot(SIRD.sol2,aes(x = TIME))+
geom_line(aes(y = S,colour=”Susceptible”))+
geom_line(aes(y = I,colour=”Infected”))+
geom_line(aes(y = R,colour=”Recovered”))+
geom_line(aes(y = D,colour=”Death”))+
xlab(label = “Time”)+

theme_classic()+

theme(legend.justification=c(1,0), legend.position = c(1,0.5))+ theme(legend.title = element_blank(), legend.background = element_blank(), legend.text = element_text(size = 10), legend.key = element_rect(colour=”#FFFFFF”, fill = ‘#C2C2C2’, size = 0.25, linetype = “solid”))+

scale_colour_manual(breaks=c(“Susceptible”,”Infected”,”Recovered”,”Death”), values=c(“blue”,”red”,”darkgreen”,”purple”))

[Mingkhwan Vithayaveroj]

Topic:
Cost-effectiveness of COVID-19 lockdown on nation economy: difference-in-differences method.

Rationale:
In March-April 2020, both UK and Thailand have many similar situations in the COVID-19 pandemic, such as the trend in the proportion of new case infectious/population. They also have nation lockdown but are different in detail. The United Kingdom forces all-day lockdown, except for some necessary activities. At the same time, Thailand closed some dangerous places but ordered the prevention to leave the residence just from 10 pm to 4 am. This difference leads to the question: Are differences in lockdown between The United Kingdom and Thailand cost-effective, both on COVID-19 prevention and economy?

Research Question:
Are differences in lockdown between The United Kingdom and Thailand cost-effective, both on COVID-19 prevention and economy?

Rough Framework:
Rough Framework

References:
#Difference-in-Differences Method
Goodman-Bacon and Marcus (2020). Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies.

#Covid prevention and Economy
Gathergood and Guttman-Kenney (2021). The English Patient: Evaluating Local Lockdowns Using Real-Time COVID-19 & Consumption Data.

Allen (2021). Covid Lockdown Cost/Benefits: A Critical Assessment of the Literature.

Zhang et al. (2021) Modeling coupling dynamics between the transmission, intervention of COVID-19 and economic development.

[Mingkhwan Vithayaveroj]

Thank you so much for your advice. I have reviewed the lecture again and get a better understanding. 😀

I do some plot again here >> incidence

I think that I will increase more efficiency variations, maybe it can see more about impact.

[Mingkhwan Vithayaveroj]

thank you so much ka ajarn.

[Mingkhwan Vithayaveroj]

yes, I think lockdown can simply reduce beta, but with many assumptions for a simple model.

here I try to plot Infecteds with the different beta, maybe it can explain the concept.

compare I

I plot with assumptions that lockdown has 20 and 25 per cent efficiency.

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