題 目：The use of disease modeling to inform COVID-19 response
The ongoing novel coronavirus disease (COVID-19) pandemic has already infected hundreds of millions worldwide and interventions to mitigate transmission are urgently needed. We used mathematical modeling to explore the impact of two intervention strategies, travel restrictions and mask wearing. In the first study, in collaboration with Facebook Data for Good, we built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. In the second study, we examined the epidemiological impact of face masks, considering resource limitations and a range of supply and demand dynamics. Even with a limited protective effect, face masks can reduce total infections and deaths, and can delay the peak time of the epidemic. However, random distribution of masks is generally suboptimal; prioritized coverage of the elderly improves outcomes, while retaining resources for detected cases provides further mitigation under a range of scenarios. Face mask use, particularly for a pathogen with relatively common asymptomatic carriage, is an effective intervention strategy, while optimized distribution is important when resources are limited.