ABSTRACT
The COVID-19 epidemic continues to have a negative impact on the economy and public health. There is a correlation between certain limits (meteorological factors and air pollution statistics) and verified fatal instances of Corona Virus Disease 2019 (COVID-19), according to several researchers. It has not yet been determined how these elements affect COVID-19. Using air pollution data and meteorological data from 15 cities in India from 2020 to 2022, Convergent Cross Mapping (CCM) is utilized to set up the causal link with new confirmed and fatal cases of COVID-19 in this study. Our experimental results show that the causal order of the factors influencing the diagnosis of COVID-19 is: humidity, PM25, temperature, CO, NO2, O3, PM10. In contrast to other parameters, temperature, PM25, and humidity are more causally associated with COVID-19, while data on air pollution are less causally related to the number of new COVID-19 cases. The causal order of the factors affecting the new death toll is as follows: temperature, PM25, humidity, O3, CO, PM10, NO2. The causality of temperature with new COVID-19 fatalities in India was higher than the causation of humidity with new COVID-19 deaths, and O3 also showed higher causality with it. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
With the rapid development of industrialization, the environmental pollution issue is becoming increasingly serious, especially the air pollution problem. As the core of the prevention and control of air pollution, air pollution prediction plays a very significant role in human survival and development. Therefore, it is highly essential to develop an accurate air pollution prediction model for mass rallies (e.g., playground and bazaar). Recent studies have suggested that multiple air contaminants, e.g., PM2.5 and PM10, which belong to a kind of aerosol, can carry the Covid-19 virus and spread it rapidly through the atmosphere, and this dramatically increases the risk of Covid-19 infection, particularly in the crowded and enclosed environment. Nevertheless, most existing air pollution prediction methods, which rely on large amounts of historical data for modeling and assume that the crowd flows relatively slow, are difficult to apply well to predict air pollution in mass rallies. To solve the aforementioned problem and better assist the decision-makers in managing environmental risk to human beings, in this article, we come up with a novel air pollution prediction model for mass rallies. More specifically, we first propose a temporally weighting matrix to differentiate the significance of training samples in the time domain. Then, we construct a temporal support vector regressor (TSVR), which puts more emphasis on the adjacent samples by considering the fact that the crowd usually flows promptly and disorderly in mass rallies. Finally, based on the extended TSVR, we develop a multitask TSVR (MTSVR) that simultaneously considers the related tasks. Since different air contaminants are correlated with each other, all the tasks can benefit by sharing information. The results of comparison experiments demonstrate that our presented MTSVR outperforms state-of-the-art single-task learners, multitask learners, and air pollution predictors when applied for air pollution prediction in mass rallies. Particularly, when under the six-task condition, the error values of the prediction of PM2.5, PM10, and O-3 obtained by our proposed method are relatively lower, outperforming the most advanced method tested by 15.2%, 6.1%, and 4.3%, and the precision values of the predicted values outperform the advanced method tested by 28.3%, 25.1%, and 24.8%.
ABSTRACT
The ability to quickly learn fundamentals about a new infectious disease, such as how it is transmitted, the incubation period, and related symptoms, is crucial in any novel pandemic. For instance, rapid identification of symptoms can enable interventions for dampening the spread of the disease. Traditionally, symptoms are learned from research publications associated with clinical studies. However, clinical studies are often slow and time intensive, and hence delays can have dire consequences in a rapidly spreading pandemic like we have seen with COVID-19. In this article, we introduce SymptomID, a modular artificial intelligence-based framework for rapid identification of symptoms associated with novel pandemics using publicly available news reports. SymptomID is built using the state-of-the-art natural language processing model (Bidirectional Encoder Representations for Transformers) to extract symptoms from publicly available news reports and cluster-related symptoms together to remove redundancy. Our proposed framework requires minimal training data, because it builds on a pre-trained language model. In this study, we present a case study of SymptomID using news articles about the current COVID-19 pandemic. Our COVID-19 symptom extraction module, trained on 225 articles, achieves an F1 score of over 0.8. SymptomID can correctly identify well-established symptoms (e.g., "fever" and "cough") and less-prevalent symptoms (e.g., "rashes," "hair loss," " brain fog") associated with the novel coronavirus. We believe this framework can be extended and easily adapted in future pandemics to quickly learn relevant insights that are fundamental for understanding and combating a new infectious disease.