ABSTRACT
Each gender is having its special behaviour which can be reflected in every field of social media. During the pandemic of COVID-19, people used twitter to discuss the issues caused by COVID-19 disease. As Twitter does not disclose the gender of the user, in this study we have discussed different kinds of approaches used to identify the gender. From the literature review, it is found that the dictionary-based approaches are the best suitable approach when we are working with the sentiment analysis of unlabelled data. This study is about the analysis of ten kinds of emotions of males and females by which we can observe how they reacted in this pandemic. The research proposes a dictionary-based approach to identify the gender and then analyzed sentiments using the cluster-based approach is applied onto word vectors after multiplying them with sentence’s polarity. The proposed approach is compared with the existing approaches with different data set and found that our proposed approach depicts good accuracy of sentiment analysis of unlabelled gendered data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Objectives: There is no consensus regarding the impact of population density on the transmission of respiratory viral infections such as COVID-19 and seasonal influenza. Our study aimed to determine the correlation between population density and the incidence and duration of COVID-19 transmission. Methods: Publicly available data for confirmed COVID-19 cases in Japan, from January 2020 through November 2021, were retrospectively collected. The average numbers of seasonal influenza cases reported in the national database from 2013-2014 through 2019-2020 were identified. Using data for COVID-19 and seasonal influenza population density and incidence rates (age-adjusted), the Pearson's correlation coefficient was determined. Results: A significant positive correlation between log population density and length of outbreak period was observed for COVID-19 (r = 0.734; p < 0.001) but not for seasonal influenza. Additionally, a significant linear correlation was observed between population density and age-adjusted incidence rate for COVID-19 (r = 0.692; p < 0.001) but not for seasonal influenza. Conclusions: In Japan, areas with high population density experienced a prolonged and more intense COVID-19 outbreak compared with areas with low population density. This was not observed with seasonal influenza, suggesting that public health measures against COVID-19 should be tailored according to population density.
ABSTRACT
The characteristics of coronavirus disease 2019 (COVID-19) clusters in medical and social welfare facilities and the factors associated with cluster size are still not yet fully understood. We reviewed COVID-19 cases in Japan identified from January 15 to April 30, 2020 and analyzed the factors associated with cluster size in medical and social welfare facilities. In this study, COVID-19 clusters were identified in 56 medical and 34 social welfare facilities. The number of cases in those facilities peaked after the peak of the general population. The duration of occurrence of new cases in clusters was positively correlated with the number of cases in both types of facilities (rho = 0.44, P < 0.001; and rho = 0.69, P < 0.001, respectively). However, the number of days between the first case in a prefecture and the onset of clusters was negatively correlated with the number of cases only in clusters in social welfare facilities (rho = - 0.4, P = 0.004). Our results suggest that COVID-19 cases in those facilities were prevalent in the latter phase of the disease's community transmission, although the underlying mechanisms for such a trend could differ between medical and social welfare facilities.
Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Japan/epidemiology , Social WelfareABSTRACT
Public health interventions have played an important role in controlling coronavirus disease 2019 (COVID-19), which is a rapidly spreading infectious disease. To contribute to future COVID-19 countermeasures, we aimed to verify the results of the countermeasures employed by public health centers (PHCs) against the first wave of COVID-19 in Yamagata Prefecture, Japan (Yamagata). Between January and May 2020, 1,253 patients suspected of SARS-CoV-2 infection were invited for testing. Simultaneously, based on retrospective contact tracings, PHCs investigated the infection sources and transmission routes of laboratory-confirmed COVID-19 cases and tested 928 contacts. Consequently, 69 cases were confirmed between March 31 and May 4, 58 of whom were from among the contacts (84.1%; 95% confidence interval [CI] 75.5-92.7). The spread of infection was triggered in cases harboring epidemiological links outside Yamagata. Subsequently, the number of cases rapidly increased. However, PHCs identified epidemiological links in 61 (88.4%; 95% CI 80.8-96.0) of the 69 cases, and transmission chains up to the fifth generation. Finally, the spread of infection ended after approximately one month. Our results indicate that the identification of infection sources and active case finding from contacts based on retrospective contact tracing was likely to be an effective strategy in ending the first wave of COVID-19 in Yamagata.