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

Abstract

Accurate prediction of the temporal and spatial characteristics of COVID-19 infection is of paramount importance for effective epidemic prevention and control. In order to accomplish this objective, we incorporated individual antibody dynamics into an agent-based model and devised a methodology that encompasses the dynamic behaviors of each individual, thereby explicitly capturing the count and spatial distribution of infected individuals with varying symptoms at distinct time points. Our model also permits the evaluation of diverse prevention and control measures. Based on our findings, the widespread employment of nucleic acid testing and the implementation of quarantine measures for positive cases and their close contacts in China have yielded remarkable outcomes in curtailing a less transmissible yet more virulent strain; however, they may prove inadequate against highly transmissible and less virulent variants. Additionally, our model excels in its ability to trace back to the initial infected case (patient zero) through early epidemic patterns. Ultimately, our model extends the frontiers of traditional epidemiological simulation methodologies and offers an alternative approach to epidemic modeling.

Keywords: Agent-based method; Antibody dynamics; COVID-19; Epidemic prediction; Epidemiological investigation; Targeted epidemic-control measures.

Fig. 1

An illustration of our agent-based model.

Fig. 2

A diagram of host-virus interaction.

Fig. 3a

the dynamic behavior of antibodies in the overall population through time. (The blue zone around mean curve stands for 95% confidence interval).

Fig. 3b

The protection performance of antibodies in the overall population through time.

Fig. 4a

The development of epidemic in ideal condition (n = 1000).

Fig. 4b

The development of epidemic in actual case (n = 1000).

Fig. 5a

the landscape of virus dynamics in the actual scenario (n = 1000).

Fig. 5b

the landscape of antibody dynamics in the actual scenario (n = 1000).

Fig. 5c

an illustration of the antibody and virus dynamics in specific agent (500th person in this case).

Fig. 6a

the effect of 80% lock-down on the epidemic development.

Fig. 6b

the effect of positive case quarantine on the epidemic development.

Fig. 6c

the effect of targeted-control policy on the epidemic development.

Fig. 7

A comparison of those three different prevention policies.

Fig. 8a

Location of patients with symptom information.

Fig. 8b

the probability of patient zero for each individual in the overall population. X represents people's location in X-axis. Y represents people's location in Y-axis.