How to use ChatGPT for Epidemiologist Job Role

R

   library(ggplot2)
   library(dplyr)
   

R

   data <- read.csv('path_to_your_file.csv')
   

R

   head(data)
   summary(data)
   

R

   data$date <- as.Date(data$date)
   

R

   monthly_data <- data %>%
     group_by(month = format(date, '%Y-%m')) %>%
     summarise(average_incidence = mean(disease_incidence))
   

R

   model <- lm(average_incidence ~ month, data = monthly_data)
   summary(model)
   

R

   ggplot(monthly_data, aes(x = month, y = average_incidence)) +
     geom_line() +
     geom_smooth(method = 'lm', col = 'red') +
     labs(title = 'Trends in Disease Incidence', x = 'Month', y = 'Average Incidence')
   

python

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint

# SIR model differential equations.
def deriv(y, t, N, beta, gamma):
    S, I, R = y
    dSdt = -beta * S * I / N
    dIdt = beta * S * I / N - gamma * I
    dRdt = gamma * I
    return dSdt, dIdt, dRdt

# Initial conditions: S0, I0, R0.
N = 1000  # Total population
I0 = 1    # Initial number of infected individuals
R0 = 0    # Initial number of recovered individuals
S0 = N - I0 - R0  # Everyone else is susceptible to infection

# Contact rate and mean recovery rate (in 1/days).
beta = 0.3  # Transmission rate
gamma = 0.1  # Recovery rate

# A grid of time points (in days).
t = np.linspace(0, 160, 160)

# Initial conditions vector.
y0 = S0, I0, R0

# Integrate the SIR equations over the time grid, t.
ret = odeint(deriv, y0, t, args=(N, beta, gamma))
S, I, R = ret.T

# Plot the data on three separate curves for S(t), I(t) and R(t)
plt.figure(figsize=(10, 6))
plt.plot(t, S, 'b', label='Susceptible')
plt.plot(t, I, 'r', label='Infected')
plt.plot(t, R, 'g', label='Recovered')
plt.title('SIR Model of Viral Infection Spread')
plt.xlabel('Days')
plt.ylabel('Number of Individuals')
plt.legend(loc='best')
plt.grid()
plt.show()