Internal migration during COVID-19 pandemic: an analysis through the lens of social organisations

In March 2020, a large number of people moved from cities to their homes in rural areas, and a few months later, many returned to cities. These journeys were undertaken not only during the COVID-19 pandemic against the advisory of governments and public health experts, but the circumstances of travel were also under extreme hardship. How may we understand this intense response by people? By drawing on the migration theory and the roles of social ties or social organisation, we can better explain peoples' reactions during this pandemic. Notably, we find non-material values, such as the dignity of labour or responsibilities to family, are significant to decision -making, and there is a desire not to compromise on these values. Further, our analyses find that the distinction between pre-disaster and post-disaster situations may not be helpful.

Spatiotemporal disease case prediction using contrastive predictive coding

Time series prediction models have played a vital role in guiding effective policymaking and response during the COVID-19 pandemic by predicting future cases and deaths at the country, state, and county levels. However, for emerging diseases, there is not sufficient historic data to fit traditional supervised prediction models. In addition, such models do not consider human mobility between regions. To mitigate the need for supervised models and to include human mobility data in the prediction, we propose Spatial Probabilistic Contrastive Predictive Coding (SP-CPC) which leverages Contrastive Predictive Coding (CPC), an unsupervised time-series representation learning approach. We augment CPC to incorporate a covariate mobility matrix into the loss function, representing the relative number of individuals traveling between each county on a given day. The proposal distribution learned by the algorithm is then sampled by the Metropolis-Hastings algorithm to give a final prediction of the number of COVID-19 cases. We find that the model applied to COVID-19 data can make accurate short-term predictions, more accurate than ARIMA and simple time-series extrapolation methods, one day into the future. However, for longer-term prediction windows of seven or more days into the future, we find that our predictions are not as competitive and require future research. © 2022 ACM.