Negative-Binomial and quasi-poisson regressions between COVID-19, mobility and environment in São Paulo, Brazil

Impacts of government social media on public engagement in low-carbon practices focusing on Japan

This study investigates the role of Government Social Media (GSM) in enhancing public engagement with Low-Carbon Practices (LCP) in Japan. Motivated by the need to foster sustainable development and mitigate climate change impacts, this research utilizes negative binomial regression model analyzing 1022 posts from nine Japanese government social media accounts. Our findings reveal that increased media richness negatively correlates with engagement, suggesting that content depth over visual appeal is more effective for LCP-related communication. Surprisingly, the dialogic loop reduces engagement, indicating complex public reactions to governmental initiatives. Content themes related to governmental actions and LCP information significantly enhance engagement, while emotional valence shows minimal impact. The study introduces 'social media capital' as a moderating factor, which mitigates the negative effects of dialogic loops and media richness on engagement, and influences the impact of content themes. These insights provide a foundation for future research and guide the development of effective public engagement strategies in environmental policy. The study highlights the need for nuanced GSM strategies that prioritize information quality and relevance to increase public participation in low-carbon initiatives.

Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India

The spatio-temporal distribution of COVID-19 across India's states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space-time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space-time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062-6.205) and deaths (RR: 2.477; 95% CI: 1.361-4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers' rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space-time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.

Global association between air pollution and COVID-19 mortality: A systematic review and meta-analysis

BACKGROUND

The COVID-19 pandemic presents unprecedented challenge for global public health systems and exacerbates existing health disparities. Epidemiological evidence suggested a potential linkage between particulate and gaseous pollutants and COVID-19 mortality. We aimed to summarize the overall risk of COVID-19 mortality associated with ambient air pollutants over the short- and long-term.

METHODS

For the systematic review and meta-analysis, we searched five databases for studies evaluating the risk of COVID-19 mortality from exposure to air pollution. Inclusion of articles was assessed independently on the basis of research topic and availability of effect estimates. The risk estimates (relative risk) for each pollutant were pooled with a random-effect model. Potential heterogeneity was explored by subgroup analysis. Funnel plots and trim-and-fill methods were employed to assess and adjust for publication bias.

FINDINGS

The systematic review retrieved 2059 records, and finally included 43 original studies. PM2.5 (RR: 1.71, 95 % CI: 1.40-2.08, per 10 μg/m3 increase), NO2 (RR: 1.33, 1.07-1.65, per 10 ppb increase) and O3 (RR: 1.61, 1.00-2.57, per 10 ppb increase) were positively associated with COVID-19 mortality for long-term exposures. Accordingly, a higher risk of COVID-19 mortality was associated with PM2.5 (1.05, 1.02-1.08), PM10 (1.05, 1.01-1.08), and NO2 (1.40, 1.04-1.90) for short-term exposures. There was some heterogeneity across subgroups of income level and geographical areas.

CONCLUSION

Both long-term and short-term exposures to ambient air pollution may increase the risk of COVID-19 mortality. Future studies utilizing individual-level information on demographics, exposures, outcome ascertainment and confounders are warranted to improve the accuracy of estimates.

Local protection bubbles: an interpretation of the slowdown in the spread of coronavirus in the city of São Paulo, Brazil, in July 2020

After four months of fighting the pandemic, the city of São Paulo, Brazil, entered a phase of relaxed social distancing measures in July 2020. Simultaneously, there was a decline in the social distancing rate and a reduction in the number of cases, fatalities, and hospital bed occupancy. To understand the pandemic dynamics in the city of São Paulo, we developed a multi-agent simulation model. Surprisingly, the counter-intuitive results of the model followed the city's reality. We argue that this phenomenon could be attributed to local bubbles of protection that emerged in the absence of contagion networks. These bubbles reduced the transmission rate of the virus, causing short and temporary reductions in the epidemic curve - but manifested as an unstable equilibrium. Our hypothesis aligns with the virus spread dynamics observed thus far, without the need for ad hoc assumptions regarding the natural thresholds of collective immunity or the heterogeneity of the population's transmission rate, which may lead to erroneous predictions. Our model was designed to be user-friendly and does not require any scientific or programming expertise to generate outcomes on virus transmission in a given location. Furthermore, as an input to start our simulation model, we developed the COVID-19 Protection Index as an alternative to the Human Development Index, which measures a given territory vulnerability to the coronavirus and includes characteristics of the health system and socioeconomic development, as well as the infrastructure of the city of São Paulo.

Interregional mobility in different age groups is associated with COVID-19 transmission in the Taipei metropolitan area, Taiwan

Before vaccines were introduced, mobility restriction was one of the primary control measures in the early stage of the coronavirus disease 2019 (COVID-19) pandemic. Because different age groups face disproportionate health risks, differences in their mobility changes affect the effectiveness of pandemic control measures. This study aimed to investigate the relationship between multiscale mobility patterns in different age groups and COVID-19 transmission before and after control measures implementation. Data on daily confirmed case numbers, anonymized mobile phone data, and 38 socioeconomic factors were used to construct negative binomial regression models of these relationships in the Taipei metropolitan area in May 2021. To avoid overfitting, the socioeconomic factor dimensions were reduced by principal component analysis. The results showed that inter-district mobility was a greater promoter of COVID-19 transmission than was intra-district mobility (coefficients: pre-alert, 0.52 and 0.43; post-alert, 0.41 and 0.36, respectively). Moreover, both the inter-district mobility of people aged 15-59 and ≥ 60 years were significantly related to the number of confirmed cases (coefficients: pre-alert, 0.82 and 1.05; post-alert, 0.48 and 0.66, respectively). The results can help agencies worldwide formulate public health responses to emerging infectious diseases.

Effect of ambient air pollution and meteorological factors on the potential transmission of COVID-19 in Turkey

There is a need to improve the understanding of air quality parameters and meteorological conditions on the transmission of SARS-CoV-2 in different regions of the world. In this preliminary study, we explore the relationship between short-term air quality (nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter (PM_2.5, PM₁₀)) exposure, temperature, humidity, and wind speed on SARS-CoV-2 transmission in 41 cities of Turkey with reported weekly cases from February 8 to April 2, 2021. Both linear and non-linear relationships were explored. The nonlinear association between weekly confirmed cases and short-term exposure to predictor factors was investigated using a generalized additive model (GAM). The preliminary results indicate that there was a significant association between humidity and weekly confirmed COVID-19 cases. The cooler temperatures had a positive correlation with the occurrence of new confirmed cases. The low PM_2.5 concentrations had a negative correlation with the number of new cases, while reducing SO₂ concentrations may help decrease the number of new cases. This is the first study investigating the relationship between measured air pollutants, meteorological factors, and the number of weekly confirmed COVID-19 cases across Turkey. There are several limitations of the presented study, however, the preliminary results show that there is a need to understand the impacts of regional air quality parameters and meteorological factors on the transmission of the virus.

Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data

Our exploratory research focuses on the possible relations between tourism and the mobility of people, using short longitudinal data for mobility dimensions during the COVID-19 pandemic. One of these is real-time, exhaustive type data, published by Google, about the mobility of people in six different dimensions, (retail, parks, residential, workplace, grocery, and transit). The aim is to analyze the directional, intensity, causal, and complex interplay between the statistical data of tourism and mobility data for Romanian counties. The main objective is to determine if real-world big data can be linked with tourism arrivals in the first 14 months of the pandemic. We have found, using correlations, factorial analysis (PCA), regression models, and SEM, that there are strong and/or medium relationships between retail and parks and overnights, and weak or no relations between other mobility dimensions (workplace, transit). By applying factorial analysis (PCA), we have regrouped the six Google Mobility dimensions into two new factors that are good predictors for Romanian tourism at the county location. These findings can help provide a better understanding of the relationship between the real movement of people in different urban areas and the tourism phenomenon: the GM parks dimension best predicts tourism indicators (overnights), the GM residential dimension correlates inversely with the tourism indicator, and the rest of the GM indices are generally weak predictors for tourism. A more complex analysis could signal the potential and the character of tourism in different destinations, by territorially and chronologically determining the GM indices that are better linked with the tourism statistical indicators. Further research is required to establish forecasting models using Google Mobility data.

Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review

COVID-19 is deemed as the most critical world health calamity of the 21st century, leading to dramatic life loss. There is a pressing need to understand the multi-stage dynamics, including transmission routes of the virus and environmental conditions due to the possibility of multiple waves of COVID-19 in the future. In this paper, a systematic examination of the literature is conducted associating the virus-laden-aerosol and transmission of these microparticles into the multimedia environment, including built environments. Particularly, this paper provides a critical review of state-of-the-art modelling tools apt for COVID-19 spread and transmission pathways. GIS-based, risk-based, and artificial intelligence-based tools are discussed for their application in the surveillance and forecasting of COVID-19. Primary environmental factors that act as simulators for the spread of the virus include meteorological variation, low air quality, pollen abundance, and spatial-temporal variation. However, the influence of these environmental factors on COVID-19 spread is still equivocal because of other non-pharmaceutical factors. The limitations of different modelling methods suggest the need for a multidisciplinary approach, including the 'One-Health' concept. Extended One-Health-based decision tools would assist policymakers in making informed decisions such as social gatherings, indoor environment improvement, and COVID-19 risk mitigation by adapting the control measurements.

Analysis of Particulate Matter Concentration Changes before, during, and Post COVID-19 Lockdown: A Case Study from Victoria, Mexico

The lockdown measures implemented due to the SARS-CoV-2 pandemic to reduce the epidemic curve, in most cases, have had a positive impact on air quality indices. Our study describes the changes in the concentration levels of PM2.5 and PM10 during the lockdown and post-lockdown in Victoria, Mexico, considering the following periods: before the lockdown (BL) from 16 February to 14 March, during the lockdown (DL) from 15 March to 2 May, and in the partial lockdown (PL) from 3 May to 6 June. When comparing the DL period of 2019 and 2020, we document a reduction in the average concentration of PM2.5 and PM10 of −55.56% and −55.17%, respectively. Moreover, we note a decrease of −53.57% for PM2.5 and −51.61% for PM10 in the PL period. When contrasting the average concentration between the DL periods of 2020 and 2021, an increase of 91.67% for PM2.5 and 100.00% for PM10 was identified. Furthermore, in the PL periods of 2020 and 2021, an increase of 38.46% and 31.33% was observed for PM2.5 and PM10, respectively. On the other hand, when comparing the concentrations of PM2.5 in the three periods of 2020, we found a decrease between BL and DL of −50.00%, between BL and PL a decrease of −45.83%, and an increase of 8.33% between DL and PL. In the case of PM10, a decrease of −48.00% between BL and DL, −40.00% between BL and PL, and an increase of 15.38% between the DL and PL periods were observed. In addition, we performed a non-parametric statistical analysis, where a significant statistical difference was found between the DL-2020 and DL-2019 pairs (x2 = 1.204) and between the DL-2021 and DL-2019 pairs (x2 = 0.372), with a p<0.000 for PM2.5, and the contrast between pairs of PM10 (DL) showed a significant difference between all pairs with p<0.01.

Measuring the mobility impact on the COVID-19 pandemic

This assessment aims at measuring the impact of different location mobility on the COVID-19 pandemic. Data over time and over the 27 Brazilian federations in 5 regions provided by Google's COVID-19 community mobility reports and classified by place categories (retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences) are autoregressed on the COVID-19 incidence in Brazil using generalized linear regressions to measure the aggregate dynamic impact of mobility on each socioeconomic category. The work provides a novel multicriteria approach for selecting the most appropriate estimation model in the context of this application. Estimations for the time gap between contagion and data disclosure for public authorities' decision-making, estimations regarding the propagation rate, and the marginal mobility contribution for each place category are also provided. We report the pandemic evolution on the dimensions of cases and a geostatistical analysis evaluating the most critical cities in Brazil based on optimized hotspots with a brief discussion on the effects of population density and the carnival.

Effects of Evaporative Emissions Control Measurements on Ozone Concentrations in Brazil

In this work, the possible benefits obtained due to the implementation of evaporative emissions control measures, originating from vehicle fueling processes, on ozone concentrations are verified. The measures studied are: (1) control at the moment when the tank trucks supply the fuel to the gas stations (Stage 1); (2) control at the moment when the vehicles are refueled at the gas stations, through a device installed in the pumps (Stage 2); (3) same as the previous control, but through a device installed in the vehicles (ORVR). The effects of these procedures were analyzed using numerical modeling with the VEIN and WRF/Chem models for a base case in 2018 and different emission scenarios, both in 2018 and 2031. The results obtained for 2018 show that the implementation of Stages 1 and 2 would reduce HCNM emissions by 47.96%, with a consequent reduction of 19.9% in the average concentrations of tropospheric ozone. For 2031, the greatest reductions in ozone concentrations were obtained with the scenario without ORVR, and with Stage 1 and Stage 2 (64.65% reduction in HCNM emissions and 31.93% in ozone), followed by the scenario with ORVR and with Stage 1 and Stage 2 (64.39% reduction in HCNM emissions and 32.98% in ozone concentrations).

Effects of the COVID-19 Pandemic on the Air Quality of the Metropolitan Region of São Paulo: Analysis Based on Satellite Data, Monitoring Stations and Records of Annual Average Daily Traffic Volumes on the Main Access Roads to the City

This paper presents an analysis of the effects of the COVID-19 pandemic on the air quality of the Metropolitan Region of São Paulo (MRSP). The effects of social distancing are still recent in the society; however, it was possible to observe patterns of environmental changes in places that had adhered transportation measures to combat the spread of the coronavirus. Thus, from the analysis of the traffic volumes made on some of the main access highways to the MRSP, as well as the monitoring of the levels of fine particulate matter (PM2.5), carbon monoxide (CO) and nitrogen dioxide (NO2), directly linked to atmospheric emissions from motor vehicles–which make up about 95% of air polluting agents in the region in different locations–we showed relationships between the improvement in air quality and the decrease in vehicles that access the MRSP. To improve the data analysis, therefore, the isolation index parameter was evaluated to provide daily information on the percentage of citizens in each municipality of the state that was effectively practicing social distancing. The intersection of these groups of data determined that the COVID-19 pandemic reduced the volume of vehicles on the highways by up to 50% of what it was in 2019, with the subsequent recovery of the traffic volume, even surpassing the values from the baseline year. Thus, the isolation index showed a decline of up to 20% between its implementation in March 2020 and December 2020. These data and the way they varied during 2020 allowed to observe an improvement of up to 50% in analyzed periods of the pollutants PM2.5, CO and NO2 in the MRSP. The main contribution of this study, alongside the synergistic use of data from different sources, was to perform traffic flow analysis separately for light and heavy duty vehicles (LDVs and HDVs). The relationships between traffic volume patterns and COVID-19 pollution were analyzed based on time series.