This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2. On 19 March, the World Health Organisation declared a pandemic. Through this global spread, many nations have witnessed exponential growth of confirmed cases brought under control by severe mass quarantine or lockdown measures. However, some nations have, through a different timeline of actions, prevented this exponential growth. Currently whilst some continue to tackle exponential growth, others attempt to safely lift restrictions whilst avoiding a resurgence. To quantify the impact of government actions and support existing theories as well as policy makers, a data-driven model is developed . Data for two nations, Italy and Taiwan, is gathered and used to train several neural networks. The results of the best-performing model , the Long Short Term Neural Network, are optimized to investigate a different scenario of mitigation measures for both nations . It is found  a larger and earlier testing campaign with tighter border control benefit both nations by resulting in significantly less confirmed cases. Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan's lack of a nationwide lockdown
On 19th March, the World Health Organisation declared a pandemic. Through this global spread, many nations have witnessed exponential growth of confirmed cases brought under control by severe mass quarantine or lockdown measures. However, some have, through a different timeline of actions, prevented this exponential growth. Currently as some continue to tackle growth, others attempt to safely lift restrictions whilst avoiding a resurgence. This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by means of training neural network models to predict the daily slope increase of cumulative infected and by using such models in an optimisation study aimed at understanding the best set of mitigating actions . Data for two nations, Italy and Taiwan, have been gathered to model government restrictions in traveling, testing and enforcement of social distance measures as well as people connectivity and adherence to government actions . It is found that a larger and earlier testing campaign with tighter entry restrictions benefit both nations , resulting in significantly less confirmed cases. Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan's lack of nationwide lockdown. The results, found with a purely data-driven approach, are in line with the main findings of mathematical epidemiological models, proving that the proposed approach has value and that the data alone contains valuable knowledge to inform decision makers.
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and support existing theories as well as policy makers, a data-driven model is developed
in mitigating viral transmission of SARS-CoV-2 by means of training neural network models to predict the daily slope increase of cumulative infected and by using such models in an optimisation study aimed at understanding the best set of mitigating actions
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is gathered and used to train several neural networks. The results of the best-performing model , the Long Short Term Neural Network, are optimized to investigate a different scenario of mitigation measures for both nations
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nationwide lockdown. The results, found with a purely data-driven approach, are in line with the main findings of mathematical epidemiological models, proving that the proposed approach has value and that the data alone contains valuable knowledge to inform decision makers.