Dynamics of emotion trends in Canadian Twitter users during COVID-19 confinement in relation to caseloads: Artificial intelligence-based emotion detection approach.

Multiple waves of COVID-19 have significantly impacted the emotional well-being of all, but many were subject to additional risks associated with forced regulations. The objective of this research was to assess the immediate emotional impact, expressed by Canadian Twitter users, and to estimate the linear relationship, with the vicissitudes of COVID caseloads, using ARIMA time-series regression. We developed two Artificial Intelligence-based algorithms to extract tweets using 18 semantic terms related to social confinement and locked down and then geocoded them to tag Canadian provinces. Tweets (n = 64,732) were classified as positive, negative, and neutral sentiments using a word-based Emotion Lexicon. Our results indicated: that Tweeters were expressing a higher daily percentage of negative sentiments representing, negative anticipation (30.1%), fear (28.1%), and anger (25.3%), than positive sentiments comprising positive anticipation (43.7%), trust (41.4%), and joy (14.9%), and neutral sentiments with mostly no emotions, when hash-tagged social confinement and locked down. In most provinces, negative sentiments took on average two to three days after caseloads increase to emerge, whereas positive sentiments took a slightly longer period of six to seven days to submerge. As daily caseloads increase, negative sentiment percentage increases in Manitoba (by 68% for 100 caseloads increase) and Atlantic Canada (by 89% with 100 caseloads increase) in wave 1(with 30% variations explained), while other provinces showed resilience. The opposite was noted in the positive sentiments. The daily percentage of emotional expression variations explained by daily caseloads in wave one were 30% for negative, 42% for neutral, and 2.1% for positive indicating that the emotional impact is multifactorial. These provincial-level impact differences with varying latency periods should be considered when planning geographically targeted, time-sensitive, confinement-related psychological health promotion efforts. Artificial Intelligence-based Geo-coded sentiment analysis of Twitter data opens possibilities for targeted rapid emotion sentiment detection opportunities.

Final size and partial distance estimate for a two-group SEIRD model.

In this paper we consider a SEIRD epidemic model for a population composed by two groups of individuals with asymmetric interaction. Given an approximate solution for the two-group model, we estimate the error of this approximation to the unknown solution to the second group based on the known error that the approximation has with respect to the solution to the first group. We also study the final size of the epidemic for each group. We illustrate our results with the spread of the coronavirus disease 2019 (COVID-19) pandemic in the New York County (USA) for the initial stage of the contamination, and in the cities of Petrolina and Juazeiro (Brazil).

COVID-19 vaccinations in pregnancy: Save mother and baby from COVID-19 pandemic.

OBJECTIVE: The current study investigated the immune response of maternal coronavirus disease 2019 (COVID-19) vaccination and vertical transmission of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) and nucleocapsid (N) proteins. STUDY DESIGN: This retrospective study included pregnant women in Bahrain Defense Force Hospital from March 2021 to September 2021 who were vaccinated with Sinopharm or Pfizer/BioNTech. Testing of anti-N and -S levels from paired samples of maternal and umbilical cord blood was performed at the time of delivery. The immune response to vaccination, association with maternal and fetal factors, and vertical transmission of antibodies were studied. RESULTS: The current study included 79 pregnant women. The median gestational age for those vaccinated with Sinopharm was 28 weeks and those vaccinated with Pfizer was 31 weeks, with 100% of the vaccinated population generating antibodies and showing vertical transmission. The anti-N and -S titers and interval frequencies varied in both vaccinations. The anti-N and -S and transfer ratio statistically correlated with maternal age, gestational age at delivery, latency period, and birth weight of the neonates differently in both vaccines. In addition, the peak level of antibodies and transfer ratios varied. CONCLUSION: Although variations are exhibited in both types of vaccination, the vaccinated pregnant population generated a significant level of anti-N and -S and showed vertical transmission.

HOW DOES THE LATENCY PERIOD IMPACT THE MODELING OF COVID-19 TRANSMISSION DYNAMICS?

We introduce two mathematical models based on systems of differential equations to investigate the relationship between the latency period and the transmission dynamics of COVID-19. We analyze the equilibrium and stability properties of these models, and perform an asymptotic study in terms of small and large latency periods. We fit the models to the COVID-19 data in the U.S. state of Tennessee. Our numerical results demonstrate the impact of the latency period on the dynamical behaviors of the solutions, on the value of the basic reproduction numbers, and on the accuracy of the model predictions.