Modeling the impact of human mobility and travel restrictions on the potential spread of SARS-CoV-2 in Taiwan

Meng-Chun Chang; Rebecca Kahn; Yu-An Li; Cheng-Sheng Lee; Caroline O Buckee; Hsiao-Han Chang

To assess the impact of travel on the overall dynamics, we performed deterministic simulations. Although the location of initial infections influenced the overall dynamics, with well-connected cities like Taipei City leading to higher total infection numbers and shorter time to peak than less-connected cities like Changhua County (Figure S9) , the difference was minimal and therefore we only showed the results from Taipei ( Figure 5 and Figure S10 ). Intracity travel reduction reduced the total number of infections and delayed time to peak in both the contact and residence models ( Figure 5 and Figure S10 ), while intercity travel reduction had only minimal impact on the overall dynamics in both models. Similarly, increased intercity and intracity colocation probabilities during the Lunar New Year ( Figure S6B ) led to increased total number of infections and decreased time to peak in the contact model, but increasing only intercity movements during Lunar New Year in the residence model only shortened time to the peak minimally ( Figure 5 and Figure S10 ), suggesting reducing large gatherings during holiday times is more important than reducing intercity travel.