AI for Epidemiological Modeling and Public Health
Module: Healthcare AI | Difficulty: Advanced
SIR Model
Basic Reproduction Number
SEIR Model
Epidemiological AI Applications
| Task | Model | Accuracy | Time Horizon |
|---|---|---|---|
| Case Forecasting | LSTM | MAE 12.3 | 7 days |
| Outbreak Detection | Transformer | F1 0.89 | Real-time |
| Contact Tracing | GNN | AUC 0.85 | Retrospective |
| Variant Surveillance | CNN | Acc 0.94 | Continuous |
| Hospitalization预测 | XGBoost | MAPE 8.2% | 14 days |
import torch
import torch.nn as nn
class SIRNeuralODE(nn.Module):
def __init__(self, hidden_dim=64):
super().__init__()
self.S_to_I = nn.Sequential(
nn.Linear(3, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, 1), nn.Sigmoid())
self.I_to_R = nn.Sequential(
nn.Linear(3, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, 1), nn.Sigmoid())
self.dt = 0.1
def forward(self, S, I, R):
N = S + I + R
beta = self.S_to_I(torch.cat([S, I, R], dim=1)) * 0.5
gamma = self.I_to_R(torch.cat([S, I, R], dim=1)) * 0.3
dS = -beta * S * I / N
dI = beta * S * I / N - gamma * I
dR = gamma * I
return S + dS * self.dt, I + dI * self.dt, R + dR * self.dt
class OutbreakPredictor(nn.Module):
def __init__(self, input_dim=10, hidden_dim=64, forecast_days=14):
super().__init__()
self.lstm = nn.LSTM(input_dim, hidden_dim, batch_first=True, num_layers=2)
self.forecast_head = nn.Sequential(
nn.Linear(hidden_dim, 32), nn.ReLU(),
nn.Linear(32, forecast_days))
def forward(self, historical_data):
_, (h_n, _) = self.lstm(historical_data)
forecast = self.forecast_head(h_n[-1])
return forecast
sir_model = SIRNeuralODE()
S = torch.tensor([999.0])
I = torch.tensor([1.0])
R = torch.tensor([0.0])
for _ in range(10):
S, I, R = sir_model(S, I, R)
print(f'S: {S.item():.1f}, I: {I.item():.1f}, R: {R.item():.1f}')
predictor = OutbreakPredictor(input_dim=10, forecast_days=14)
historical = torch.randn(1, 30, 10)
forecast = predictor(historical)
print(f'Forecast shape: {forecast.shape}')
Research Insight: AI-based epidemiological models can now incorporate real-time data streams (social media, mobility data, wastewater surveillance) to detect outbreaks earlier than traditional reporting systems. The key challenge is model interpretability: public health officials need to understand why a model predicts an outbreak, not just that one is predicted. Hybrid mechanistic-data-driven models that combine SIR dynamics with neural networks offer the best balance of accuracy and interpretability.