Publications by Binh Thang
Health Blief Model 1
Concept of study Theoretical concept for study data_clean <- read_sav("C:/Users/binht/Dropbox/Hue/data/2022_data_consultant/01 DD 1/data_clean.sav") There are some specific variables using in this analysis names(data_clean) [1] "ID" "C1.Name" "C2.Medical_record_code" ...
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Health Belief Model 2
Concept of study Theoretical concept for study library(haven) data_clean <- read_sav("C:/Users/binht/Dropbox/Hue/data/2022_data_consultant/01 DD 1/data_clean.sav") There are some specific variables using in this analysis names(data_clean) ## [1] "ID" ## [2] "C1.Name" ## ...
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Document
Theoretical concept for study library(foreign) data <- read.csv("C:/Users/binht/Dropbox/Hue/data/2022_data_consultant/01 DD 1/Rcode/data2_1.csv") BMA model library(BMA) ## Loading required package: survival ## Loading required package: leaps ## Loading required package: robustbase ## ## Attaching package: 'robustbase' ## The following objec...
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Project 2: BSE
library(haven) dd2=read_sav("C:/Users/binht/Dropbox/Hue/data/2022_data_consultant/2 dd2/R/spss_r.sav") class(dd2) ## [1] "tbl_df" "tbl" "data.frame" data.label.table <- attr(dd2, "label.table") missings <- attr(dd2, "missings") names(dd2) ## [1] "MaID" "NhomNC" "A1" "A2" "A3" "A4" "A5" ## [8] "A6.1" ...
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Simulating the effective reproduction number Reff (restudy)
1 LOAD THE PACKAGES: library(deSolve) library(reshape2) library(ggplot2) 2 MODEL INPUTS: 2.1 Vector storing the initial number of people in each compartment (at timestep 0) initial_state_values <- c(S = 1000000-1, # the whole population we are modelling is susceptible to infection I = 1, # the epidemic s...
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SIR 2 model with a dynamic force of infection -restudy
library(deSolve) library(reshape2) library(ggplot2) 1 MODEL INPUTS: 1.1 Vector storing the initial number of people in each compartment (at timestep 0) initial_state_values <- c(S = 1000000-1, # the whole population we are modelling is susceptible to infection I = 1, # the epidemic starts with a single inf...
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SIR model with a constant force of infection (W1)
library(deSolve) # package to solve the model library(reshape2) # package to change the shape of the model output library(ggplot2) # package for plotting #1.1 The input data from the instructions were as follows: initial_state_values <- c(S = 999999, # the whole population we are modelling is susceptible to infection ...
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W1 Simulating competing hazards
library(deSolve) # package to solve the model library(reshape2) # package to change the shape of the model output library(ggplot2) # package for plotting 1 initial_number_infected <- 100000000 # Number of population in Vietnam initial_number_recovered <- 0 # the initial number of people in "0" ...
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Choi cho vui voi COVID
library(deSolve) # package to solve the model library(reshape2) # package to change the shape of the model output library(ggplot2) # package for plotting initial_number_infected <- 100000000 # Number of population in Vietnam initial_number_recovered <- 0 # the initial number of people in "0" ...
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W2 SIR dynamic with vary parametters
1 SIR dynamics with varying parameters In the first week, you gained first experience with coding simple models in R using the deSolve package. Last week, you went into further details about the drivers of an epidemic and the dynamics of the SIR model. This week, we are bringing this together to think more deeply about the roles of \(\beta\) and ...
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