Thermal Conditions and Hospital Admissions: Analysis of Longitudinal Data from Cyprus (2009–2018)

The state of the thermal environment can affect human health and well-being. Heat stress is associated with a wide range of health outcomes increasing morbidity and mortality and is recognized as an important health risk posed by climate change. This study aims at examining the effect of thermal conditions on the daily number of hospital admissions in Cyprus. Data from eight public hospitals located in five districts of Cyprus were analyzed from 2009 to 2018. Meteorological hourly gridded data were extracted by the ERA-5 Land reanalysis database with a spatial horizontal resolution of 0.1° × 0.1°. The Physiologically Equivalent Temperature (PET) and the Universal Thermal Climate Index (UTCI) were calculated as measures of the integrated effect of meteorological variables. Negative binomial regression was fitted to examine associations between the daily number of hospital admissions and meteorological variables, PET, and UTCI. The results showed that the mean daily temperature (Tair) was positively associated with hospital admissions from any cause. Hospital admissions increased by 0.6% (p < 0.001) for each 1 °C increase of Tair and by 0.4% (p < 0.001) for each 1 °C increase of PET and UTCI. Ozone and nitrogen oxides act as confounding factors. An effect of particulate matter (less than 10 μm in diameter) was observed when the analysis focused on April to August. Thresholds above which hospital admissions are likely to increase include daily mean Tair = 26.1 °C, PET = 29 °C, and UTCI = 26 °C. Studies on heat-related health effects are necessary to monitor health patterns, raise awareness, and design adaptation and mitigation measures.

A Fermented Milk Product Containing B. lactis CNCM I-2494 Improves the Tolerance of a Plant-Based Diet in Patients with Disorders of Gut–Brain Interactions

Healthy, plant-based diets, rich in fermentable residues, may induce gas-related symptoms. The aim of this exploratory study was to assess the effects of a fermented milk product, containing probiotics, on the tolerance of a healthy diet in patients with disorders of gut–brain interactions (DGBI), complaining of excessive flatulence. In an open design, a 3-day healthy, mostly plant-based diet was administered to patients with DGBI (52 included, 43 completed) before and at the end of 28 days of consumption of a fermented milk product (FMP) containing Bifidobacterium animalis subsp. lactis CNCM I-2494 and lactic acid bacteria. As compared to a habitual diet, the flatulogenic diet increased the perception of digestive symptoms (flatulence score 7.1 ± 1.6 vs. 5.8 ± 1.9; p < 0.05) and the daily number of anal gas evacuations (22.4 ± 12.5 vs. 16.5 ± 10.2; p < 0.0001). FMP consumption reduced the flatulence sensation score (by –1.6 ± 2.2; p < 0.05) and the daily number of anal gas evacuations (by –5.3 ± 8.2; p < 0.0001). FMP consumption did not significantly alter the overall gut microbiota composition, but some changes in the microbiota correlated with the observed clinical improvement. The consumption of a product containing B. lactis CNCM I-2494 improved the tolerance of a healthy diet in patients with DGBI, and this effect may be mediated, in part, by the metabolic activity of the microbiota.

Vaccine efficacy for COVID-19 outbreak in New York City

This article aims to study the COVID-19 data for New York City. We use both the daily number of second does vaccination and the daily number of reported cases for New York City. This article provides a method to combine an epidemic model and such data. We explore the influence of vaccine efficacy on our results.

A Time Series Model on the Occurrence of COVID-19 Pandemic in Nigeria

Coronaviruses belong to a large family of viruses which affect the hepatic, gastrointestinal, neurological and respiratory systems. The increase in the daily number of COVID-19 confirmed and deaths cases from different countries of the world has brought social, economic and political activities to a standstill, affecting individuals, government, public and private sectors. In this study, autoregressive integrated moving average (ARIMA) time series model for modeling and forecasting daily confirmed, recovered, and deaths cases of COVID-19 in Nigeria was used with data on daily cases of confirmed, recovered and deaths due to COVID-19 in Nigeria from 27/02/2020-31/07/2020 obtained from Nigeria Centre for Disease Control (NCDC) website. The data from 27/02/2020-16/07/2020 were used for model building while 15 observations from 17/07/2020-31/07/2020 were used for training and forecast evaluations. Time plots and Dickey-Fuller Generalized Least Squares unit root test were used to investigate the stationarity properties of the data. Schwarz Information Criterion (SIC) in conjunction with log likelihood were used to search for optimal ARIMA models while Mean Absolute Percentage Error (MAPE) was used for forecast evaluation. Results showed that all the study variables were differenced stationary and hence integrated of order one, I (1). ARIMA (2,1,4), ARIMA (2,1,2) and ARIMA (2,1,3) models were selected as the best candidates for modeling and forecasting the confirmed, recovered and deaths cases of COVID-19 in Nigeria respectively. The study found an approximate COVID-19 life cycle of 12 days among the infected population. The 15 days’ forecasts from ARIMA (2,1,4) and ARIMA (2,1,2) models showed increases in the daily number of confirmed and recovered cases of COVID-19 in Nigeria. The forecasts from ARIMA (2,1,3) model however showed fluctuating trend with decline in the number of deaths cases due to the disease. The result of the study further showed that improving on the present approach to treatment will further decrease the number of casualties due to COVID-19 in Nigeria.

Mass Media as a Mirror of the COVID-19 Pandemic

The media plays an important role in disseminating facts and knowledge to the public at critical times, and the COVID-19 pandemic is a good example of such a period. This research is devoted to performing a comparative analysis of the representation of topics connected with the pandemic in the internet media of Kazakhstan and the Russian Federation. The main goal of the research is to propose a method that would make it possible to analyze the correlation between mass media dynamic indicators and the World Health Organization COVID-19 data. In order to solve the task, three approaches related to the representation of mass media dynamics in numerical form—automatically obtained topics, average sentiment, and dynamic indicators—were proposed and applied according to a manually selected list of search queries. The results of the analysis indicate similarities and differences in the ways in which the epidemiological situation is reflected in publications in Russia and in Kazakhstan. In particular, the publication activity in both countries correlates with the absolute indicators, such as the daily number of new infections, and the daily number of deaths. However, mass media tend to ignore the positive rate of confirmed cases and the virus reproduction rate. If we consider strictness of quarantine measures, mass media in Russia show a rather high correlation, while in Kazakhstan, the correlation is much lower. Analysis of search queries revealed that in Kazakhstan the problem of fake news and disinformation is more acute during periods of deterioration of the epidemiological situation, when the level of crime and poverty increase. The novelty of this work is the proposal and implementation of a method that allows the performing of a comparative analysis of objective COVID-19 statistics and several mass media indicators. In addition, it is the first time that such a comparative analysis, between different countries, has been performed on a corpus in a language other than English.

Comparing the first and second waves of COVID-19 in a tertiary university hospital in Barcelona

Background: Hospitals have constituted the limiting resource of the healthcare systems for the management of the COVID-19 pandemic. As the pandemic progressed, knowledge of the disease improved, and healthcare systems were expected to be more adapted to provide a more efficient response. The objective of this research was to compare the flow of COVID-19 patients in emergency rooms and hospital wards, between the pandemic's first and second waves at the University Hospital of Vall d’Hebron (Barcelona, Spain), and to compare the profiles, severity and mortality of COVID-19 patients between the two waves. Methods: A retrospective observational analysis of COVID-19 patients attending the hospital from February 24 to April 26, 2020 (first wave) and from July 24, 2020, to May 18, 2021 (second wave) was carried out. We analysed the data of the electronic medical records on patient demographics, comorbidity, severity, and mortality. Results: The daily number of COVID-19 patients entering the emergency rooms (ER) dropped by 65% during the second wave compared to the first wave. During the second wave, patients entering the ER were significantly younger (61 against 63 years old p<0.001) and less severely affected (39% against 48% with a triage level of resuscitation or emergency; p<0.001). ER mortality declined during the second wave (1% against 2%; p<0.000). The daily number of hospitalised COVID-19 patients dropped by 75% during the second wave. Those hospitalised during the second wave were more severely affected (20% against 10%; p<0.001) and were referred to the intensive care unit (ICU) more frequently (21% against 15%; p<0.001). Inpatient mortality showed no significant difference between the two waves. Conclusions: Changes in the flow, severity and mortality of COVID-19 patients entering this tertiary hospital during the two waves may reflect a better adaptation of the health care system and the improvement of knowledge on the disease.

The elections and campaign styles influence the spread of COVID-19 pandemic: lessons learned from Kerala, India

Background: The pandemic caused by SARS-Cov-2 and its variants whack the world with overlapping waves. Kerala is the Indian state which successfully curbed the first wave of COVID-19, getting noticed when daunted by the second wave. The aim of this study was to assess the impact of two elections held in Kerala on the transmission of COVID-19 from October 1st, 2020 to May 5th, 2021.Methods: The study employed a retrospective cross-sectional design with publicly available data. The test positivity (TPR) and daily number of cases (DNC) collected from governmental websites of Kerala, India and COVID-19 dashboards entered in MS Excel 2007 and analysed using IBM SPSS version 25. Biweekly average of TPR and DNC was analysed in descriptive statistics and DNC at different periods in the context of elections were analysed using repeated-measures ANOVA with Greenhouse-Geisser correction and post hoc test of Bonferroni correction.Results: The findings showed that the daily number of COVID-19 cases increased after both local body and assembly elections, but a statistically significant increase was found after the assembly election [mean difference= 1069 (357.047-1782.419) at p=0.002 from the pre-election period].Conclusions: The study revealed that the conduct of elections in stages and organizing campaigns limiting to the local area following COVID protocols had a demonstrable positive effect against the potential of pandemic spread.

Forecasting for the Optimal Numbers of COVID-19 Infection to Maintain Economic Circular Flows of Thailand

We evaluated the movement in the daily number of COVID-19 cases in response to the real GDP during the COVID-19 pandemic in Thailand from Q1 2020 to Q1 2021. The aim of the study was to find the number of COVID-19 cases that could maintain circulation of the country’s economy. This is the question that most of the world’s economies have been facing and trying to figure out. Our theoretical model introduced dynamic stochastic general equilibrium (DSGE) models with a special emphasis on Bayesian inference. From the results of the study, it was found that the most reasonable number of COVID-19 cases that still maintains circulation of the country’s economy is about 3000 per month or about 9000 per quarter. This demonstrates that the daily number of COVID-19 cases significantly affects the growth of Thailand’s real GDP. Economists and policymakers can use the results of empirical studies to come up with guidelines or policies that can be implemented to reduce the number of infections to satisfactory levels in order to avoid Thailand lockdown. Although the COVID-19 outbreak can be suppressed through lockdown, the country cannot be locked down all the time.

Impact of meteorological factors on number of new COVID-19 cases in Pune

Background: Coronaviruses are a family of viruses that can result in different types of illnesses, most commonly, as Severe acute respiratory syndrome (SARS). Researches have shown that the atmospheric variables and the density of population have affected the transmission of the disease. Meteorological variables like temperature, humidity among others have found to affect the rise of pandemic in positive or negative ways. Respiratory virus illnesses have shown seasonal variability since the time they have been discovered and managed. This study investigated the relationship between the meteorological variables of temperature, humidity and precipitation in the spread of COVID-19 disease in the city of Pune.Methods: This record based descriptive study is conducted after secondary data analysis of number of new cases of COVID-19 per day from the period 01 May to 24 December 2020 in Pune. Meteorological data of maximum (Tmax), minimum (Tmin) and daily average temperature (Tavg), humidity and precipitation were daily noted from Indian meteorological department website. Trend was identified plotting the daily number of clinically diagnosed cases over time period. Pearson’s correlation was used to estimate association between meteorological variables and daily detected fresh cases of COVID-19 disease. Results: Analysis revealed significant negative correlation (r=-0.3563, p<0.005) between daily detected number of cases and maximum daily temperature. A strong positive correlation was seen between humidity and daily number of cases (r=0.5541, p<0.005).Conclusions: The findings of this study will aid in forecasting epidemics and in preparing for the impact of climate change on the COVID epidemiology through the implementation of public health preventive measures.