A Real Time and Interactive Web-Based Platform for Visualizing and Analyzing COVID-19 in Canada

Dan, Liu; Du, Yuan; Charvadeh, Yasin Khadem; Cui, Jingyu; Chen, Li‐Pang; Deng, Gansen; Zhang, Qihuang; Cai, Kaiming; He, Joy Wei; He, Wenqing; Yi, Grace Y.

doi:10.5539/ijsp.v9n5p23

Cited by 2 publications

(2 citation statements)

References 6 publications

(5 reference statements)

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“…While Ontario is the most populated, its infection rate is not the highest and is even lower than the overall infection rate in Canada. More detailed explorations and descriptive statistics of the Canadian data are available in the dashboard created by the GW-DS-COVID-19 research group: https://covid-19-canada.uwo.ca/index.html which was introduced by Liu et al [ 12 ].…”

Section: Data and Frameworkmentioning

confidence: 99%

Model-based forecasting for Canadian COVID-19 data

Chen

Zhang

et al. 2021

PLoS ONE

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Background Since March 11, 2020 when the World Health Organization (WHO) declared the COVID-19 pandemic, the number of infected cases, the number of deaths, and the number of affected countries have climbed rapidly. To understand the impact of COVID-19 on public health, many studies have been conducted for various countries. To complement the available work, in this article we examine Canadian COVID-19 data for the period of March 18, 2020 to August 16, 2020 with the aim to forecast the dynamic trend in a short term. Method We focus our attention on Canadian data and analyze the four provinces, Ontario, Alberta, British Columbia, and Quebec, which have the most severe situations in Canada. To build predictive models and conduct prediction, we employ three models, smooth transition autoregressive (STAR) models, neural network (NN) models, and susceptible-infected-removed (SIR) models, to fit time series data of confirmed cases in the four provinces separately. In comparison, we also analyze the data of daily infections in two states of USA, Texas and New York state, for the period of March 18, 2020 to August 16, 2020. We emphasize that different models make different assumptions which are basically difficult to validate. Yet invoking different models allows us to examine the data from different angles, thus, helping reveal the underlying trajectory of the development of COVID-19 in Canada. Finding The examinations of the data dated from March 18, 2020 to August 11, 2020 show that the STAR, NN, and SIR models may output different results, though the differences are small in some cases. Prediction over a short term period incurs smaller prediction variability than over a long term period, as expected. The NN method tends to outperform other two methods. All the methods forecast an upward trend in all the four Canadian provinces for the period of August 12, 2020 to August 23, 2020, though the degree varies from method to method. This research offers model-based insights into the pandemic evolvement in Canada.

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Section: Data and Frameworkmentioning

confidence: 99%

Model-based forecasting for Canadian COVID-19 data

Chen

Zhang

et al. 2021

PLoS ONE

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“…We perceive that sentiment patterns may be considerably different before and after the advent of vaccines. It is interesting to develop a real-time and interactive platform (e.g., [33]) to reveal dynamic changes in sentiments over time. It is useful to examine how sentiments may be influenced by major associated factors, such as the advent of vaccines, the availability of easily accessed rapid tests, changes in preventative policies, emerging of new virus variants, and so on.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccines

Zhang

Chen

et al. 2023

PLoS ONE

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While the impact of the COVID-19 pandemic has been widely studied, relatively fewer discussions about the sentimental reaction of the public are available. In this article, we scrape COVID-19 related tweets on the microblogging platform, Twitter, and examine the tweets from February 24, 2020 to October 14, 2020 in four Canadian cities (Toronto, Montreal, Vancouver, and Calgary) and four U.S. cities (New York, Los Angeles, Chicago, and Seattle). Applying the RoBERTa, Vader and NRC approaches, we evaluate sentiment intensity scores and visualize the results over different periods of the pandemic. Sentiment scores for the tweets concerning three anti-epidemic measures, “masks”, “vaccine”, and “lockdown”, are computed for comparison. We explore possible causal relationships among the variables concerning tweet activities and sentiment scores of COVID-19 related tweets by integrating the echo state network method with convergent cross-mapping. Our analyses show that public sentiments about COVID-19 vary from time to time and from place to place, and are different with respect to anti-epidemic measures of “masks”, “vaccines”, and “lockdown”. Evidence of the causal relationship is revealed for the examined variables, assuming the suggested model is feasible.

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A Real Time and Interactive Web-Based Platform for Visualizing and Analyzing COVID-19 in Canada

Cited by 2 publications

References 6 publications

Model-based forecasting for Canadian COVID-19 data

Model-based forecasting for Canadian COVID-19 data

Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccines

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