Editing Ai Epidemiology (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Epidemic curve forecasting with LSTM:'''
<syntaxhighlight lang="python">
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_absolute_error

class EpidemicLSTM(nn.Module):
    """LSTM for epidemic incidence forecasting."""
    def __init__(self, input_dim, hidden_dim=128, n_layers=2, output_horizon=14):
        super().__init__()
        self.lstm = nn.LSTM(input_dim, hidden_dim, n_layers, batch_first=True, dropout=0.2)
        self.head = nn.Linear(hidden_dim, output_horizon)

    def forward(self, x):
        out, _ = self.lstm(x)
        return self.head(out[:, -1, :])  # Forecast 14 days ahead

# Features: new_cases, hospitalizations, mobility, vaccination_rate, temperature
# Use CDC FluView, WHO FluNet, or COVID-19 surveillance data
def prepare_sequences(df, window=28, horizon=14):
    X, y = [], []
    for i in range(len(df) - window - horizon + 1):
        X.append(df.iloc[i:i+window].values)
        y.append(df['new_cases'].iloc[i+window:i+window+horizon].values)
    return np.array(X), np.array(y)

# Load surveillance data
df = pd.read_csv("surveillance_data.csv", parse_dates=['date']).sort_values('date')
scaler = MinMaxScaler()
scaled = pd.DataFrame(scaler.fit_transform(df[['new_cases', 'hospitalizations',
                                                'mobility', 'vaccination_rate']]),
                      columns=['new_cases', 'hospitalizations', 'mobility', 'vaccination_rate'])
X, y = prepare_sequences(scaled, window=28, horizon=14)
X = torch.FloatTensor(X); y = torch.FloatTensor(y)
# Train/test split: chronological (never shuffle time series!)
split = int(0.8 * len(X))
model = EpidemicLSTM(input_dim=4, output_horizon=14)
optimizer = torch.optim.Adam(model.parameters())
criterion = nn.MSELoss()
</syntaxhighlight>

; Epidemiology AI tools
: '''Surveillance''' → HealthMap (NLP news), ProMED AI, Nextstrain (genomic)
: '''Wastewater''' → Biobot Analytics, WastewaterSCAN + ML trend detection
: '''Forecasting''' → CDC Forecast Hub, EU COVID-19 Forecast Hub, FluSight
: '''Contact tracing''' → TraceTogether, NOVID, state health department apps
: '''EHR analytics''' → Trinetx, TrialSpark, Aetion (causal inference)
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