Human activity pattern implications for modeling SARS-CoV-2 transmission

Abstract

Background and objectives: SARS-CoV-2 emerged in December 2019 and rapidly spread into a global pandemic. Designing optimal community responses (social distancing, vaccination) is dependent on the stage of the disease progression, discovery of asymptomatic individuals, changes in virulence of the pathogen, and current levels of herd immunity. Community strategies may have severe and undesirable social and economic side effects. Modeling is the only available scientific approach to develop effective strategies that can minimize these unwanted side effects while retaining the effectiveness of the interventions.

Methods: We extended the agent-based model, SpatioTemporal Human Activity Model (STHAM), for simulating SARS-CoV-2 transmission dynamics.

Results: Here we present preliminary STHAM simulation results that reproduce the overall trends observed in the Wasatch Front (Utah, United States of America) for the general population. The results presented here clearly indicate that human activity patterns are important in predicting the rate of infection for different demographic groups in the population.

Conclusions: Future work in pandemic simulations should use empirical human activity data for agent-based techniques.

Keywords: Agent-Based Modeling; COVID-19; Epidemiological Modeling; Human Activity patterns; SARS-CoV-2; SpatioTemporal Human Activity Model; Transmission Dynamics.

Fig. 1

STHAM agent creation process. Demographic properties from the Census Block tables are assigned to each agent, and then each agent is assigned to a household, which is given a home location. Additional locations for employment, school enrollment, and other regularly attended sites are then assigned. From Ref. .

Fig. 2

Comparison of the total cumulative number of STHAM predicted cases compared with the cumulative number of cases reported by the UDOH for the Utah Wasatch Front. Day Zero corresponds to March 15^th, 2020, which is the day that the total count of infected individuals in the Wasatch Front exceeded ten individuals.

Fig. 3

Comparison of the total cumulative number of STHAM predicted cases with the cumulative number of cases predicted by the model simulations using EpiModel .

Fig. 4

Temporal increase of predicted cases as a percentage of the population and the relative percentages for the different types of agents considered here. The agent simulation was performed using the STHAM modeling approach [36,37]. The left panel corresponds to the initial 21 days of the simulation, while the right panel extends up to 56 days (8 weeks).

Fig. 5

Spatial distribution of the increase of the cumulative number of predicted cases as a percentage of the population and the relative percentages for the different types of agents considered here. The agent simulation was performed using the STHAM modeling approach [36,37].