Data driven covid-19 spread prediction based on mobility and mask mandate information

Abstract

COVID-19 is one of the largest spreading pandemic diseases faced in the documented history of mankind. Human to human interaction is the most prolific method of transmission of this virus. Nations all across the globe started to issue stay at home orders and mandating to wear masks or a form of face-covering in public to minimize the transmission by reducing contact between majority of the populace. The epidemiological models used in the literature have considerable drawbacks in the assumption of homogeneous mixing among the populace. Moreover, the effect of mitigation strategies such as mask mandate and stay at home orders cannot be efficiently accounted for in these models. In this work, we propose a novel data driven approach using LSTM (Long Short Term Memory) neural network model to form a functional mapping of daily new confirmed cases with mobility data which has been quantified from cell phone traffic information and mask mandate information. With this approach no pre-defined equations are used to predict the spread, no homogeneous mixing assumption is made, and the effect of mitigation strategies can be accounted for. The model learns the spread of the virus based on factual data from verified resources. A study of the number of cases for the state of New York (NY) and state of Florida (FL) in the USA are performed using the model. The model correctly predicts that with higher mobility the cases would increase and vice-versa. It further predicts the rate of new cases would see a decline if a mask mandate is administered. Both these predictions are in agreement with the opinions of leading medical and immunological experts. The model also predicts that with the mask mandate option even a higher mobility would reduce the daily cases than lower mobility without masks. We additionally generate results and provide RMSE (Root Mean Square Error) comparison with ARIMA based model of other published work for Italy, Turkey, Australia, Brazil, Canada, Egypt, Japan, and the UK. Our model reports lower RMSE than the ARIMA based work for all eight countries which were tested. The proposed model would provide administrations with a quantifiable basis of how mobility, mask mandates are related to new confirmed cases; so far no epidemiological models provide that information. It gives fast and relatively accurate prediction of the number of cases and would enable the administrations to make informed decisions and make plans for mitigation strategies and changes in hospital resources.

Modeling mobility, risk, and pandemic severity during the first year of COVID

“…Importantly, the results presented in it are only to be used to qualitatively establish that our models are at least as good as the models we found in the literature. As we see from Table 1 , [16] ’s LSTM neural network model quality is the best of the compared models, albeit, as we discussed in Literature Review, it may be an overfit for some communities (New York, 0.995). With our approach, the model quality exceeds two out of three cited models for 4 out of 5 metrics, with one metric in the same ballpark as [17] and better than [15] .…”

“… TIME AS AN EXPLANATORY VARIABLE As we discussed in Section 4.1 , time is an important parameter affecting how the relationships between the explanatory and the dependent variables evolve. Now that we have a way to reduce the scope of the modeling problems by highlighting the statistically important features via nonparametric modeling, we can incorporate time as an explanatory variable - either (i) by parametric modeling of the relationships we found in the current work and analyzing the time dependency of the parameters of such models, or (b) by incorporating modern developments in multivariate time series analysis and deep learning, as proposed, e.g., by [16] . …”

“…On the other hand, [16] propose a model for predicting the spread of COVID based on mobility and mask mandate information. Their work is important for our research: by using nonparametric model (LSTM neural network), they have achieved predictions significantly better than obtained with the standard ARIMA method.…”

A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States

“…The flexibility of the time-varying latent process is critical because the intensity of precautionary behaviors varied over time. Some analyses have sought to address this issue using additional data streams such as the presence of mask mandates over space and time [18]. In contrast, our flexible time-evolving process avoids the need for additional data streams.…”