Wisdom of Crowds Detects COVID-19 Severity Ahead of Officially Available Data

During the unfolding of a crisis, it is crucial to determine its severity, yet access to reliable data is challenging. We investigate the relation between geolocated Tweet Intensity of initial COVID-19 related tweet at the beginning of the pandemic across Italian, Spanish and USA regions and mortality in the region a month later. We find significant proportionality between early social media reaction and the cumulative number of COVID-19 deaths almost a month later. Our findings suggest that "the crowds" perceived the risk correctly. This is one of the few examples where the "wisdom of crowds" can be quantified and applied in practice. This can be used to create real-time alert systems that could be of help for crisis-management and intervention, especially in developing countries. Such systems could contribute to inform fast-response policy making at early stages of a crisis.Comment: 14 pages, 3 figures, 3 table

Predicting the Spread of the Corona Virus (COVID-19) in Indonesia: Approach Visual Data Analysis and Prophet Forecasting

The development trend of the coronavirus pandemic (COVID-19) in various countries has become a global threat, including in Southeast Asia, such as Indonesia, the Philippines, Brunei, Malaysia, and Singapore. In this paper, we propose an Exploratory Data Analysis (EDA) model approach and a time series forecasting model using the Prophet method to predict the number of confirmed cases and cases of death in Indonesia in the next thirty days. We apply the EDA model to visualize and provide an understanding of this pandemic outbreak in various countries, especially in Indonesia. We present the trends in the spread of epidemics from the countries of China from which the virus originates, then mark the top ten countries and their development and also present the trends in Asian countries. We present an analytical framework comparing the predicted results with the actual data evaluated using the MAPE and MAE models, where the prophet algorithm produces good performance based on the evaluation results, the relative error rate of our estimate (MAPE) is around 6.52%, and the model average false 52.7% (MAE) for confirmed cases, while case mortality was 1.3% for the MAPE and MAE models around 236.6%. The results of the analysis can be used as a reference for the Indonesian government in making decisions to prevent its spread in order to avoid an increase in the number of death

A Comparison of Aggregation Methods for Probabilistic Forecasts of COVID-19 Mortality in the United States

The COVID-19 pandemic has placed forecasting models at the forefront of health policy making. Predictions of mortality and hospitalization help governments meet planning and resource allocation challenges. In this paper, we consider the weekly forecasting of the cumulative mortality due to COVID-19 at the national and state level in the U.S. Optimal decision-making requires a forecast of a probability distribution, rather than just a single point forecast. Interval forecasts are also important, as they can support decision making and provide situational awareness. We consider the case where probabilistic forecasts have been provided by multiple forecasting teams, and we aggregate the forecasts to extract the wisdom of the crowd. With only limited information available regarding the historical accuracy of the forecasting teams, we consider aggregation (i.e. combining) methods that do not rely on a record of past accuracy. In this empirical paper, we evaluate the accuracy of aggregation methods that have been previously proposed for interval forecasts and predictions of probability distributions. These include the use of the simple average, the median, and trimming methods, which enable robust estimation and allow the aggregate forecast to reduce the impact of a tendency for the forecasting teams to be under- or overconfident. We use data that has been made publicly available from the COVID-19 Forecast Hub. While the simple average performed well for the high mortality series, we obtained greater accuracy using the median and certain trimming methods for the low and medium mortality series. It will be interesting to see if this remains the case as the pandemic evolves.Comment: 32 pages, 11 figures, 5 table

Data-Centric Epidemic Forecasting: A Survey

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing previously unobservable facets and also due to initiatives from government public health and funding agencies. This has resulted, in particular, in a spate of work on 'data-centered' solutions which have shown potential in enhancing our forecasting capabilities by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This survey delves into various data-driven methodological and practical advancements and introduces a conceptual framework to navigate through them. First, we enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting, capturing various factors like symptomatic online surveys, retail and commerce, mobility, genomics data and more. Next, we discuss methods and modeling paradigms focusing on the recent data-driven statistical and deep-learning based methods as well as on the novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline.Comment: 67 pages, 12 figure

Causal Modeling of Twitter Activity During COVID-19

Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g. number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and sentiment. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.Comment: 13 pages, 3 figure

Rural Tourism Destination Spatial Interventions Face the Risk of COVID-19 Infection

The Kampoeng Boenga Grangsil (KBG) Tourism Destination development faces significant design challenges in the face of the COVID-19 pandemic. The challenges of the COVID-19 pandemic require adjustments to the design of KBG tourist facilities through physical intervention. Prevention of the spread of the SARS-CoV-2 virus through health protocols is one of the criteria for facility design interventions in KBG tourist destinations. The uncertainty of the COVID-19 pandemic ending has forced us to adapt to new conditions, new requirements (social and physical distancing), and new arrangements (physical, social, and health), which are considered in developing spatial intervention design criteria. Community participation is the potential of local wisdom in developing rural tourism destinations. Some basic questions include: (1) What is the role of the Grangsil community in the preparation of health protocols as criteria for the design of physical interventions in KBG Destinations? (2) What are the spatial implications of the need for social and physical distance in rural tourism activities? (3) What are the physical design intervention criteria for tourist destinations to reduce the risk of COVID-19 transmission for visitors? The descriptive exploration method was used to obtain design criteria for the physical intervention of tourist destinations. A participatory approach is essential in exploring these non-physical aspects of planning and design criterion preparations. The study results are the criteria for spatial intervention for KBG destinations facing the risk of COVID-19 infection. This study enriches the spatial design requirements of rural tourist destinations based on the risk mitigation of COVID-19 transmission

Examining the Impact of a Reasoning Aid to Help People Evaluate the Evidentiary Weight of Consensus

This item is only available electronically.Social media is a vortex of information and people may see distorted views of consensus, where the independence of information and sources is unclear. A tool that summarises consensus information might help people to navigate these important cues. This study examined whether a reasoning aid (in the form of a diagram) visually illustrating both the number of independent people supporting/disagreeing with a claim and the diversity of arguments would persuade people to change their original beliefs. Participants (n=605) were recruited through Amazon’s Mechanical Turk to evaluate 24 claims on a mock Twitter interface. Participants were randomly assigned to conditions with either tweets only, diagram only or tweets with a diagram. Participants rated their initial agreement level (0-100) with each claim and then saw the diagram and/or set of tweets, then were able to update their agreement level if their original opinion had now changed. The findings of this study show that without assistance, people mostly rely on cues of argument quantity, such as the number of tweets for a given stance. However, when presented with a diagram, people were able to utilise cues of argument quality, such as when there were different sources providing the information and when multiple arguments were used.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 202

Sentiment Analysis of COVID-19 Pandemic on the Stock Market

COVID-19 is a dreadful infectious disease, morphed into an economic crisis causing extensive and longstanding ramifications across global markets. Investors continue to hear about COVID-19 and its impact in one corner of the globe or the other for a long time. Though the effects of COVID19 started in December 2019 in Wuhan, China, global markets did not respond actively till W.H.O officially declared on March 11, 2020, that the COVID19 outbreak is a global pandemic. These multi-channel events have eroded investor sentiment, tanking the global stock markets. This article uses a machine learning approach to Twitter to analyze and follow investor sentiment that has guided the market to the new low during the first 150 days of the COVID-19 era. The only respite for recovery of financial markets is the lowering of COVID-19 infected cases for the time being till a vaccine is developed for the virus

Language and COVID-19: A discourse analysis of resistance to lockdown in Indonesia

Communities in Indonesia were resistant to lockdown policies, Large-Scale Social Restrictions (PSBB) and the Enactment of Restrictions on Community Activities (PPKM). Both policies were implemented numerous times in the country during the COVID-19 pandemic, and these caused widespread unrest. Language with the terms PSBB and PPKM, which several times extended suddenly, not informed to the community, inconsistent in its implementation, makes the community feel mad, neglected the needs of their life, and severe rejections. This research was conducted with a qualitative approach sourced from primary and secondary data. Primary data were obtained from electronic media news that shows public resistance and government policies published through the official government web. Meanwhile, secondary data were obtained from journal articles discussing community resistance related to policies to prevent the spread of the COVID-19 pandemic. The results showed that various terms translated from the term lockdown to the time PSBB and PPKM had consequences for policy misalignment with community expectations. The switching of language from lockdown to PSBB and PPKM has caused resistance in the community because it has allowed the government to be economically irresponsible. Therefore, the government needs to inform and be responsible, so that policies can run effectively

Mail-Voting During COVID-19: Protecting Public Health and Expanding Voting Accessibility

The 2020 presidential election will be held during the COVID-19 public health crisis. In response to the COVID-19 situation, there is an immediate need to universalize mail-voting. However, President Donald Trump consistently downplays COVID-19 and asserts that mail- voting causes voter fraud and favors the Democratic Party. This Article examines modifications made in prior elections held during or in the wake of crises. Though these modifications are not applicable for the COVID-19 pandemic, they provide two important distinctions: crisis-specific measures should prioritize protecting public health and, if there is an opportunity to preserve the right to vote through election modification, that modification should be implemented. The Article highlights the origins and development through election law of absentee voting. Once this foundation is established, it differentiates between perceived and true threats during the 2020 election. Perceived threats are based on unfounded claims that mail-voting leads to greater election fraud or benefits one political party over the other. True threats are COVID-19 and the impact of universal mail-voting on historically disenfranchised voters. However, the COVID-19 situation provides opportunity to implement tangible solutions offered by advocates to expeditiously improve voting accessibility to minority voters. The Article highlights recent challenges to the established absentee voter laws that attempt to fit the pandemic into excuse categories but have failed. It then argues that rather than fitting into laws that dictate a normal election year, mail-voting should be implemented as a necessary and contemporary countermeasure for protecting public health. This argument is supported by the logic applied in Jacobson v. Massachusetts. The Article concludes that mail-voting during the COVID-19 pandemic is not only a case-specific solution, it is also a catalyst for necessary improvements to voting accessibility