Examining Different Disease Transmission Approaches in Data-Driven Agent-Based Models

Disease spread simulations are critical in evaluating and aiding policy-making during pandemics. However, traditionally, disease spread simulations have poor representations of human mobility. In agent-based models, human mobility is often assumed to be random due to a lack of data. In other types of models, mobility is not included, such as the case in traditional SEIR models, which use differential equations to model disease spread. Furthermore, disease spread simulations constructed by non-experts may have poorly justified disease parameters. In this study, we compare the effect of different representations of mobility in an agent-based model of COVID-19 spread for Fairfax County, Virginia. We use two representations: 1) random mobility and 2) a mobility model that is calibrated using data of foot traffic from SafeGraph. We also compare two different types of agent interaction, the dynamic that drives disease transmission. In one version of the model, an interaction parameter is used to determine the probability that two agents interact at a Place of Interest (POI). Another version of the model uses POI density (square footage per agent) to determine interaction probability. To validate the model, we seed the simulations with empirical case counts of Covid-19 in Fairfax County from April-1-2020 and run the simulation for a year. Our best-fitting simulation, which was one with random mobility, had a Residual Mean Square Error of ~36700 cases compared to simulation versions with empirically-based mobility that had RMSE over 100,000 cases. Our disease parameters, however, replicate average household COVID-19 transmission numbers. Future agent-based models should incorporate mask use and model human risk assessment on compliance with public health guidelines.

00:00 hello everyone my name is fahad aloraini 00:03 and this is our summer team impact 00:05 projects of 00:06 2021 examining different disease 00:09 transmission approaches and data-driven 00:11 agent-based models to reduction 00:17 disease modeling is critical for policy 00:20 making during pandemics 00:21 it helps policy makers make decisions 00:24 predict the outcome of of different 00:27 policies 00:28 and predict a number of cases 00:35 traditionally disease models do not 00:37 include mobility 00:38 such as the case in seir models are 00:41 assumed to be random due to lack of data 00:44 in the current study we create an 00:46 agent-based model with two different 00:48 mobility sub-models 00:49 random or data-driven mobility based on 00:52 safegraph data 00:53 we also create two agents interaction 00:55 models 00:56 one that is interaction parameter based 00:59 and the other 00:59 is density based or determines the 01:02 interaction of 28 based on that 01:05 square footage divided by the number of 01:07 people at that 01:09 location in the simulation at for that 01:12 specific uh time step 01:17 here are the model parameters uh real 01:19 quick interaction probabilities either 01:21 from one percent to fifty percent and 01:24 it’s random and one version of them all 01:26 and another which is a density base the 01:28 version two it’s based on the square 01:30 footage per agent and it uses an 01:32 equation that describes 01:34 fixing chance as a function of test 01:36 distance 01:38 incubation period uh is based on a 01:41 ra it’s random based on a gamma 01:43 distribution 01:45 uh and here are the different 01:49 disease stages 01:53 the transmission probability is based on 01:57 a binomial distribution where n is 02:01 the number of possible interactions at a 02:04 place of interest 02:08 and the dwell times the time it takes or 02:11 a person or an agent stays at a 02:15 location is based on safegraph data 02:18 number of agents is based on the data 02:20 data from uh 02:24 the census and its census data of 02:28 fairfax county in 2018 location choice 02:32 is random or data driven and the random 02:37 as i said it’s people 02:40 or agents choose the locations randomly 02:44 and the data driven model 02:48 they choose the locations based on 02:52 a calibrated model 02:56 based on safe graph data simulation time 02:58 we ran the simulation for 365 03:02 days uh simulation time 03:05 and uh and it takes two and a half days 03:09 to run this 03:10 and the we started 03:13 at the first of april 2021 03:18 2020 verification validation 03:21 code checks and reviews were done 03:23 periodically to verify the model we use 03:25 a residual muse current error to compare 03:27 against covet 19 data 03:28 from the new york times data set 03:32 2021 preliminary results here are our 03:36 results 03:37 and we see that uh 03:41 in many of the simulations a lot of 03:43 people 03:45 become effect infected real quick and 03:49 the simulation stops even before 03:52 the 100th day is reached in other 03:55 simulations uh 03:57 we see that they continue for the whole 03:59 365 days 04:01 uh the steps in those graphs 04:04 are because we average some simulations 04:07 this with same parameters 04:10 because they have a stochastic nature 04:12 they 04:14 are end or terminate 04:18 early because everyone gets infected or 04:20 the infection 04:21 dies out real quickly so you get this 04:25 step function here 04:28 the the line that is well it’s 04:32 it’s easier to see here the green line 04:34 the 04:36 uh at the bottom is the 04:39 empirical data and the other 04:44 lines are the 04:48 simulations and we see that the closest 04:51 thing for the empirical 04:55 is the simulations that are random 04:59 and end early 05:03 here are the aram rmses 05:06 and we see that 36 000 05:10 is our best uh 05:13 simulation in terms of the average 05:16 number of cases it’s all from the 05:18 empirical data 05:20 uh and it’s an interaction based at 05:23 one percent for the 05:26 density based version we see that it is 05:29 37 05:30 000 and both are random mobility 05:34 and we will discuss why in a second 05:37 all other simulations with high 05:39 interaction or lda based mobilities 05:41 had uh rmses of 05:44 100 000 and more they were all from the 05:48 empirical data 05:49 on average 100 000 per day 05:52 uh so the possible reason why 05:56 the empirical based mobility had worst 05:58 fit in comparison with the empirical 06:00 data 06:00 is because it replicates hot spots 06:04 uh 06:08 the the empirical data replicates 06:10 hotspots 06:11 while the random mobility does not 06:13 future work might implement mask use 06:15 this work is still in progress uh so 06:19 this is these results are preliminary 06:21 and we are running 06:22 currently newer versions of the model 06:26 here are my references and here is an 06:29 acknowledgment to 06:30 the nsf the summer team impact 06:35 uh grant of george ma of the jordan 06:37 mason university of office of the 06:39 provost and executive 06:40 vice president this work is supported by 06:43 the george mason university aspiring 06:45 scientist summer internship program 06:48 thank you very much