Add SEIR model template

templates/seir/WRITEUP.md

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+# TITLE HERE
+
+## Abstract
+
+## BACKGROUND
+
+## Experiment Descriptions
+
+## Results Summary
+
+Here is how to add a figure.
+
+![figure description](figure_1.png)
+[[TODO: Please add a caption beneath every figure.]]
+
+## CONCLUSIONS
+
+## FUTURE WORK

templates/seir/experiment.py

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+import os
+import json
+import numpy as np
+import argparse
+from scipy.integrate import odeint
+
+# -----------------------------------------------------------------------------
+# SEIR model is a differential equation model that describes the dynamics of infectious diseases such as COVID-19.
+# The model divides the population into four compartments: S (susceptible), E (exposed), I (infectious), and R (recovered).
+# -----------------------------------------------------------------------------
+
+
+parser = argparse.ArgumentParser(description="Run experiment")
+parser.add_argument("--out_dir", type=str, default="run_0", help="Output directory")
+args = parser.parse_args()
+
+if __name__ == "__main__":
+    out_dir = args.out_dir
+    os.makedirs(out_dir, exist_ok=True)
+
+    def seir_eq(v, t, beta, lp, ip):
+        """Differential equation of SEIR model
+        v: [S, E, I, R] Distribution of people in each state
+        t: Time
+        beta: Infection rate
+        lp: Latent period
+        ip: Infectious period
+        """
+        dS = -beta * v[0] * v[2]
+        dE = beta * v[0] * v[2] - (1 / lp) * v[1]
+        dI = (1 / lp) * v[1] - (1 / ip) * v[2]
+        dR = (1 / ip) * v[2]
+        return np.array([dS, dE, dI, dR])
+
+    # Solve SEIR model
+    init_state = np.array([3000, 0, 5, 0])
+    solution = odeint(
+        seir_eq,
+        init_state,
+        t=np.arange(0, 100, 1),
+        args=(0.001, 14, 7),
+    )
+
+    final_info = {
+        "solution": solution.tolist(),
+        "infected_peak_day": np.argmax(solution[:, 2]).item(),
+        "infected_peak": np.max(solution[:, 2]).item(),
+        "tolal_infected": solution[-1, 3].item(),
+    }
+    with open(os.path.join(out_dir, "final_info.json"), "w") as f:
+        json.dump(final_info, f)

templates/seir/ideas.json

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+[
+    {
+        "Name": "threshold_behavioral_response_seir",
+        "Title": "Modeling Threshold-Based Behavioral Responses in SEIR Dynamics",
+        "Experiment": "Modify the seir_eq function to implement multiple thresholds for adjusting the contact rate based on the proportion of infected individuals. Define specific behavior change scenarios (e.g., reduced contact rates at 1%, 5%, 10% infection levels) and analyze their impacts on peak infections and total infections. This will allow for a more detailed understanding of how varying public health responses can influence disease spread.",
+        "Interestingness": 9,
+        "Feasibility": 8,
+        "Novelty": 9,
+        "novel": true
+    },
+    {
+        "Name": "vaccination_strategy_seir",
+        "Title": "Incorporating Vaccination Strategies into SEIR Dynamics",
+        "Experiment": "Modify the seir_eq function to add a vaccination compartment (V). Implement various vaccination strategies with defined vaccination rates (e.g., 0.1% per day) and specify vaccine efficacy duration (e.g., 6 months). Conduct sensitivity analyses to assess how varying vaccination rates and durations affect peak infections and total infections over time. Compare immediate, phased, and delayed vaccination strategies in terms of their effectiveness in controlling the outbreak.",
+        "Interestingness": 9,
+        "Feasibility": 8,
+        "Novelty": 8,
+        "novel": true
+    },
+    {
+        "Name": "dynamic_social_distancing_seir",
+        "Title": "Modeling Dynamic Social Distancing Responses in SEIR Dynamics",
+        "Experiment": "Modify the seir_eq function to include a social distancing factor that reduces the infection rate based on the number of infections. Define three levels of social distancing (mild, moderate, strict) triggered at specific infection thresholds. Analyze the outcomes in terms of peak infections, total infections, and the duration of the epidemic, comparing results with and without these distancing measures.",
+        "Interestingness": 9,
+        "Feasibility": 9,
+        "Novelty": 9,
+        "novel": true
+    }
+]