How to Best Predict the Daily Number of New Infections of COVID-19

Skiera, Bernd; Jürgensmeier, Lukas; Stowe, Kevin; Gurevych, Iryna

doi:10.2139/ssrn.3571252

Cited by 11 publications

(6 citation statements)

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“…This can be valuable for countries with less developed reporting infrastructure or those that experience delays in reporting final official numbers. For example, it can take Germany several days before final COVID-19 cases are reported, and they are significantly underreported on the weekends [31]. Social media sentiment can be used to complement official numbers in these instances.…”

Section: Discussionmentioning

confidence: 99%

Nowcasting and Forecasting COVID-19 Cases and Deaths Using Twitter Sentiment

Askay¹,

Molony²,

Glanz³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Real-time access to information during a pandemic is crucial for mobilizing a response. A sentiment analysis of Twitter posts from the first 90 days of the COVID-19 pandemic was conducted. In particular, 2 million English tweets were collected from users in the United States that contained the word 'covid' between January 1, 2020 and March 31, 2020. Sentiments were used to model the new case and death counts using data from this time. The results of linear regression and knearest neighbors indicate that sentiments expressed on social media accurately predict both same-day and near future counts of both COVID-19 cases and deaths. Public health officials can use this knowledge to assist in responding to adverse public health events. Additionally, implications for future research and theorizing of social media's impact on health behaviors are discussed.

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Section: Discussionmentioning

confidence: 99%

Nowcasting and Forecasting COVID-19 Cases and Deaths Using Twitter Sentiment

Askay¹,

Molony²,

Glanz³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…In addition, the delays are also related to the lack of personnel available to report cases on weekends, which creates a seasonality structure in the cases and death reports, generating an additional aggregation problem on the time series. 10 To bypass these problems, we propose to estimate the long-term movements of deaths by COVID-19 on a global scale through a structural decomposition. 11 The main idea is to decompose the temporal variability observed in the data into the trend, seasonal and cycle components, which allows identifying permanent movements, and cyclical and seasonal effects, in the presence of measurement errors.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Estimating spatiotemporal patterns of deaths by COVID-19 outbreak on a global scale

Valente

Laurini

2021

BMJ Open

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ObjectiveOur main objective is to estimate the trend of deaths by COVID-19 on a global scale, considering the six continents.Study designThe study design was a retrospective observational study conducted using the secondary data provided by the Our World in Data project on a public domain.SettingThis study was conducted based on worldwide deaths by COVID-19 recorded for the Our World in Data project from 29 February 2020 to 17 February 2021.MethodsEstimating the trend in COVID-19 deaths is not a trivial task due to the problems associated with the COVID-19 data, such as the spatial and temporal heterogeneity, observed seasonality and the delay between the onset of symptoms and diagnosis, indicating a relevant measurement error problem and changing the series’ dependency structure. To bypass the aforementioned problems, we propose a method to estimate the components of trend, seasonality and cycle in COVID-19 data, controlling for the presence of measurement error and considering the spatial heterogeneity. We used the proposed model to estimate the trend component of deaths by COVID-19 on a global scale.ResultsThe model was able to capture the patterns in the occurrence of deaths related to COVID-19, overcoming the problems observed in COVID-19 data. We found compelling evidence that spatiotemporal models are more accurate than univariate models to estimate the patterns of the occurrence of deaths. Based on the measures of dispersion of the models’ prediction in relation to observed deaths, it is possible to note that the models with spatial component are significantly superior to the univariate model.ConclusionThe findings suggested that the spatial dynamics have an important role in the COVID-19 epidemic process since the results provided evidence that spatiotemporal models are more accurate to estimate the general patterns of the occurrence of deaths related to COVID-19.

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“…Gharavi et al [16] discovered there was a temporal lag of from five to nineteen days between the rising number of symptom-reported tweets and officiallyreported confirmed positive cases. Shiera et al [17] exploit Google searches and Tweets which gave an accurate estimation of COVID-19 cases three days before public health officials released confirmed case numbers. In contrast, our focus is studying and predicting the temporal dynamics of users on social media platforms in the context of a disaster preparedness system based on the official announcements and their affect on PPE acquisition and distribution.…”

Section: Background and Related Workmentioning

confidence: 99%

Pandemic Response and Crisis Informatics: An Imperative for Public Health Messaging

Lai

Liu

Chang

et al. 2020

2020 International Computer Symposium (ICS)

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The focus of this research is to examine the usage patterns exhibited by users of online search engines in the midst of COVID-19. We aim to understand how the queries are structured and their timing on the various platforms that citizens are using to check the availability of Personal Protective Equipment (PPE) since the outbreak of the COVID-19 public health crisis. Understanding and analyzing peak volume for information platforms is critical, especially for public health policy, with a mind toward crisis informatics. In this study, we collect all the data of users querying data from Face Mask Map (FMM), a real-time application which displays the inventory status for all stores selling PPE. This data is from the point at which the public health crisis became widely known to the time at which PPE availability saturated the market. As COVID-19 continues to proliferate and affect people around the globe, official organizations such as Department of Health and World Health Organization (WHO) utilize Web or Social Media (Facebook or Twitter) to announce up-to-date news, e.g. daily confirmed cases or in order to update policy regarding resource management. We then correlate the significant announcements from public health officials, specifically published by Ministry of Health and Welfare (MoHW) in Taiwan, that are concerning usage and distribution of PPE. We find that the temporal dynamics of aggregated users behavior are consistent with the events. For the practitioner of disaster management, it is critical to be able to identify when the public will consistently react to public health announcements for the purpose of ensuring proper supply distribution and avoid misallocation. It is our hope that the study can help to build an effective online disaster preparedness information system, in the consideration of computing and public psychology, to better respond to disaster with a greater corpus of data.

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How to Best Predict the Daily Number of New Infections of COVID-19

Cited by 11 publications

References 0 publications

Nowcasting and Forecasting COVID-19 Cases and Deaths Using Twitter Sentiment

Nowcasting and Forecasting COVID-19 Cases and Deaths Using Twitter Sentiment

Estimating spatiotemporal patterns of deaths by COVID-19 outbreak on a global scale

Pandemic Response and Crisis Informatics: An Imperative for Public Health Messaging

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