ABSTRACT
OBJECTIVES: This study aimed to explore the 1-year temporal change in prevalence, variety, and potential risk factors of long COVID symptoms, and to further predict the prognostic trends of long COVID. METHODS: We searched electronic databases for related studies published from January 2020 to February 2022, and conducted one group meta-analysis and locally weighted regression explore the monthly temporal change in the prevalence of each long COVID symptom in 1-year follow-up period. RESULTS: A total of 137 studies were included in meta-analysis, including 134,093 participants. The temporal change of any long COVID symptom showed a steep decrease initially (from 92% at acute phase to 55% at 1-month follow-up), followed by stabilization at approximately 50% during 1-year follow-up. Six months or more after the acute phase, the odds ratio (OR) of population characteristic factors increased, such as female gender (from 1.62 to 1.82), while the OR value of acute phase-related factors (severe/critical and hospitalization) decreased. As for specific symptoms, about two-thirds of the symptoms did not significantly reduce during the 1-year follow-up, and the neuropsychiatric symptoms showed a higher long-term prevalence (approximately 25%) and longer persistence than physical-symptoms. CONCLUSIONS: The temporal changes in the prevalence and characteristics speculate that long COVID may persist longer than expected. In particular, we should pay more attention to neuropsychiatric symptoms and other symptoms for which there is no significant downward trend in prevalence. The influence of acute phase-related factors for long COVID gradually decreases over time, while the influence of population characteristic-related gradually increases.
ABSTRACT
Identifying and understanding risk perceptions—"how bad are the harms” to humans or to what they value that people see as potentially or actually arising from entities or events—has been critical for risk analysis, both for its own sake, and for expected associations between risk perceptions and subsequent outcomes, such as risky or protective behavior, or support for hazard management policies. Cross-sectional surveys have been the dominant method for identifying and understanding risk perceptions, yielding valuable data. However, cross-sectional surveys are unable to probe the dynamics of risk perceptions over time, which is critical to do while living in a dynamically hazardous world and to build causal understandings. Building upon earlier longitudinal panel studies of Americans' Ebola and Zika risk perceptions using multi-level modeling to assess temporal changes in these views and inter-individual factors affecting them, we examined patterns in Americans' COVID-19 risk perceptions in six waves across 14 months. The findings suggest that, in general, risk perceptions increased from February 2020 to April 2021, but with varying trends across different risk perception measures (personal, collective, affective, affect, severity, and duration). Factors in baseline risk perceptions (Wave 1) and inter-individual differences across waves differed even more: baseline ratings were associated with how immediate the threat is (temporal distance) and how likely the threat would affect people like oneself (social distance), and following the United States news about the pandemic. Inter-individual trend differences were shaped most by temporal distance, whether local coronavirus infections were accelerating their upward trend, and subjective knowledge about viral transmission. Associations of subjective knowledge and risk trend with risk perceptions could change signs (e.g. from positive to negative) over time. These findings hold theoretical implications for risk perception dynamics and taxonomies, and research design implications for studying risk perception dynamics and their comparison across hazards. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
The Covid-19 epidemic has adversely affected the world in terms of health, education, economic, tourism, social and psychological. During to the epidemic, different measures were taken to prevent the epidemic, such as travel bans, curfews, stopping in production. These measures have reduced and improved air pollution. Within the scope of this study, the change in air pollution in Kocaeli between 2019 and 2021 was examined monthly. PM10 and SO2 maps were created with inverse distance weighted (IDW) technique using geographic information systems technology (GIS). The year 2020, when Covid-19 measures were taken, was compared with 2019 and 2021. Change maps were created by taking the difference between 2020-2019 and 2021-2020 with GIS technology. As a result of the research, it was determined that the level of air pollution decreased in 2020. On the contrary, in 2021, an increase in air pollution levels was observed. In the study, a decrease was observed in PM10 concentration during the Covid-19 lockdowns, however a decrease was not observed for SO2. © Author(s) 2023.
ABSTRACT
COVID-19 has been the most widespread and far-reaching public health emergency since the beginning of the 21st century. The Chinese COVID-19 lockdown has been the most comprehensive and strict in the world. Based on the Shanghai COVID-19 outbreak in 2022, we analyzed the heterogeneous impact of the COVID-19 lockdown on human activities and urban economy using monthly nighttime light data. We found that the impact of lockdown on human activities in the Yangtze River Delta is very obvious. The number of counties in Shanghai, Jiangsu, Zhejiang and Anhui showing a downward trend of MNLR (Mean of Nighttime Light Radiation) is 100%, 97%, 99% and 85%, respectively. Before the outbreak of COVID-19, the proportion of counties with a downward trend of MNLR was 19%, 67%, 22% and 33%, respectively. Although the MNLR of some counties also decreased in 2019, the scope and intensity was far less than 2022. Under regular containment (2020 and 2021), MNLR in the Yangtze River Delta also showed a significant increase (MNLR change > 0). According to NLRI (Nighttime Light Radiation Influence), the Shanghai lockdown has significantly affected the surrounding provinces (Average NLRI < 0). Jiangsu is the most affected province other than Shanghai. At the same time, Chengdu-Chongqing, Guangdong–Hong Kong–Macao and the Triangle of Central China have no obvious linkage effect.
ABSTRACT
Documenting how human pressure on wildlife changes over time is important to minimise potential adverse effects through implementing appropriate management and policy actions;however, obtaining objective measures of these changes and their potential impacts is often logistically challenging, particularly in the natural environment. Here, we developed a modular stochastic model that infers the ratio of actual viewing pressure on wildlife in consecutive time periods (years) using social media, as this medium is widespread and easily accessible. Pressure was calculated from the number of times individual animals appeared in social media in pre-defined time windows, accounting for time-dependent variables that influence them (e.g. number of people with access to social media). Formulas for the confidence intervals of viewing pressure ratios were rigorously developed and validated, and corresponding uncertainty was quantified. We applied the developed framework to calculate changes to wildlife viewing pressure on loggerhead sea turtles (Caretta caretta) at Zakynthos island (Greece) before and during the COVID-19 pandemic (2019–2021) based on 2646 social media entries. Our model ensured temporal comparability across years of social media data grouped in time window sizes, by correcting for the interannual increase of social media use. Optimal sizes for these windows were delineated, reducing uncertainty while maintaining high time-scale resolution. The optimal time window was around 7-days during the peak tourist season when more data were available in all three years, and >15 days during the low season. In contrast, raw social media data exhibited clear bias when quantifying changes to viewing pressure, with unknown uncertainty. The framework developed here allows widely-available social media data to be used objectively when quantifying temporal changes to wildlife viewing pressure. Its modularity allowed viewing pressure to be quantified for all data combined, or subsets of data (different groups, situations or locations), and could be applied to any site supporting wildlife exposed to tourism. © 2022 The Author(s)
ABSTRACT
Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been 100,819,363 confirmed cases and 2,176,159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of the modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R2 values (for cases, R2â¯=â¯0.961; for deaths, R2â¯=â¯0.962), compared to GWR's Adj. R2 values (for cases, R2â¯=â¯0.954; for deaths, R2â¯=â¯0.954). To inform policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory forces and related spatial patterns of the driving factors is of paramount importance. Since it is not the first time humans are facing public health emergency, the findings of the present research on COVID-19 therefore can be used as a reference for policy designing and effective decision making.