{'fraction_non_alphanumeric': 0.02872260015117158, 'fraction_numerical': 0.008314436885865457, 'mean_word_length': 4.781659388646288, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
STSIR: A Spatial Temporal Pandemic Model with Mobility Data - A COVID-19 Study

With the outbreak of COVID-19, how to mitigate and suppress its spread is a big issue to the government. Department of public health need powerful models to analyze and predict the trend and scale of such pandemic. And models that could evaluate the effect of the public policy are also essential to the fight with COVID-19. A main limitation of existing models is that they can only evaluate the policy by calculating R0 after infection happens instead of giving observable index. To tackle this, based on the transmission characteristics of the COVID-19, we propose a novel framework Spatial-Temporal-Susceptible-Infected-Removed (STSIR) model. In particular, we combine both intra-city and inter-city mobility indices with the traditional SIR dynamics and make it a dynamic system. And we prove that the STSIR system is a closed system which makes the system self-consistent. And finally we proposed a Multi-Stage Simulated Annealing (MSSA) algorithm to find the optimal parameters of the system. In our experiments, based on Baidu Mobility dataset [1], and China pandemic dataset provided by Dingxiangyuan [2], our model can effectively predict the total scale of the pandemic and also give clear policy analysis with the observable index.