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Several discrete simulation models have been created to study the spread of human immunodeficiency virus type 1 (HIV-1) within a human body. This is motivated both by the prevalence of the virus, and by the possibility of asking questions in simulations that would be unethical to test in trials. Among discrete simulation techniques, cellular automata (CA) have been particularly used in HIV-1 research. CA commonly assume that a cell is almost exclusively infected by neighboring cells (i.e., cell-to-cell transmission), and that more distal cells (i.e., cell-free transmission) have an extremely small probability to transmit the disease. The mechanisms are more nuanced in recent biological research, suggesting that cell-to-cell transmission may account for about 60% of all transmissions. We show that a representative sample of five previously validated CA models of HIV-1 can all be altered (by changing neighborhood structures and infection probabilities) to produce a realistic share of cell-to-cell and cell-free viral transmissions. Increasing the realism for modes of transmission, however, has mixed consequences on preserving the models' validity: their predictions at 600 weeks are generally unchanged, but viral dynamics are markedly different. We offer several suggestions to create CA models of HIV-1 with realistic infections and plausible viral dynamics.

Mechanisms for Cell-to-cell and Cell-free Spread of HIV-1 in Cellular Automata Models