An agent-based model with antibody dynamics information in COVID-19 epidemic simulation

. 2023 Nov 10;8(4):1151–1168. doi: 10.1016/j.idm.2023.11.001

for i = 1:M (M represents the overall steps)

for j = 1:N (N represents the overall number of people in the population)

x_{(i + 1, j)} = (β y_{(i, j)} z_{(i, j)} - γ x_{(i, j)} - δ x_{(i, j)}) Δ t + x_{(i, j)}

y_{(i + 1, j)} = (γ x_{(i, j)} - β y_{(i, j)} z_{(i, j)} + ε x_{(i, j)} - η y_{(i, j)} - θ p_{(i, j)} y_{(i, j)} + λ q_{(i, j)} + ε q_{(i, j)}) Δ t + y_{(i, j)}

z_{(i, j)}

> C (C is the nucleic acid testing threshold)

M_{i n t e r a c t i o n} (j, :) = 0 f o r 14 d a y s

M_{i n t e r a c t i o n} (j, m) \sim = 0

M_{i n t e r a c t i o n} (m, :) = 0 f o r 14 d a y s

end

r e s t o r e t o o r i g i n a l M_{i n t e r a c t i o n} after 14 days

end

z_{(i + 1, j)} = (γ x_{(i, j)} - β y_{(i, j)} z_{(i, j)} - ζ z_{(i, j)} + α z_{(i, j)} + \sum_{j = 1}^{N} φ (z_{(k, j)})) Δ t + z_{(i, j)}

φ (z_{(k, j)}) = μ z_{j} M_{i n t e r a c t i o n} (i, j)

p_{(i + 1, j)} = p_{(i, j)}

q_{(i + 1, j)} = (θ p_{(i, j)} y_{(i, j)} - λ q_{(i, j)} - δ q_{(i, j)}) Δ t + q_{(i, j)}

end