# How Human Mobility Models Can Help to Deal with COVID-19
## 7 Evaluation of the Infection Risk
### ERQ Evaluation
As detailed in Section 4, the PedSim simulator can be parametrised in order to generate mobility traces with different social distances. All previous experiments were done using a social force of 7, which was adjusted to avoid close contacts (less than 2 m). We centre our evaluation on the spatial risk of the Station scenario using ERQ (that is, as shown in Figure 10b). We repeated the same experiment considering two different social forces. If the social force is set to a low value (2), the simulator will reduce the distance between pedestrians. In contrast, if this value is set very high (9), it will increase the physical distance between pedestrians (whenever it is possible). Note that a very low value (0-1) is unreal, and actually, it will simulate collisions between pedestrians. The results are shown in Figure 11, and we compare them with the previous results of Figure 10b). On the one hand, we can see in Figure 11a that, when no social distance measures are taken, the exposure risk significantly increases throughout all the station and not only on the expected hot spots (platforms and turnstiles). On the other hand, if this distance is increased, we can clearly see in Figure 11b that the exposure risk is greatly reduced, although there are spots where the distance cannot be reduced, so the risk is still there. This experiment evidences the flexibility of our proposed model since it can study how different human mobility patterns can affect the spreading of the COVID-19 disease. Summing up, the previous experiments show that using a proper expression of exposure risk we can obtain the spots where the risk is high, and also, compare the results of the different scenarios (that is, outdoor vs. indoor). Particularly, in the scenarios  
Figure 11: Evaluation of the exposure risk depending on social distancing (**a**) No social distancing (social force = 2); (**b**) Increased social distancing (social force = 9). Figure 10: Temporal evolution of the exposure risk measured in MEMs. (**a**) Plaza; (**b**) Station. evaluated, the Plaza outdoor scenario exhibits a very reduced risk exposure compared to the Station indoor scenario. Based on these results, health authorities could make effective risk management plans to force pedestrians to avoid these risky spots with the final objective of minimising the risk of transmission. For example, the map can be used in the station scenario to mark with arrows and lines enter and exit routes, places to stay, etc. Furthermore, this plan could be modelled again in the pedestrian simulator (for example, defining the safe routes) in order to test if the proposed plan is correct or not (that is, no hot spots are generated). This can be an iterative process until a good plan is obtained. This is similar to the primary use of pedestrian simulators, in which architects and civil engineers assess the impact of different levels of pedestrian demand, and test and validate emergency evacuation plans. Finally, a different approach would be to increase ventilation in the place or even using an autonomous robot to disinfect the risky spots continuously.