Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries

Factors affecting the number of influenza patients before and during COVID-19 pandemic, Thailand

This study was aimed to explore the association between potential factors including public health and social measures and the number of influenza patients in Thailand between 2014-2021. Secondary data from relevant agencies were collected. Generalized Estimating Equation (GEE) and regression coefficient (β) were performed at a significance level of 0.05. We found factors associated with number of influenza patients during the time prior to COVID-19 pandemic were monthly income per household (Adjusted β = -0.02; 95% CI: -0.03, -0.01), population density (Adjusted β = 1.00; 95% CI: 0.82, 1.18), rainy season (Adjusted β = 137.15; 95% CI: 86.17, 188.13) and winter time (Adjusted β = 56.46; 95% CI: 3.21, 109.71). During the time of COVID-19 pandemic, population density (Adjusted β = 0.20; 95% CI: 0.15, 0.26), rainy season (Adjusted β = -164.23; 95% CI: -229.93, -98.52), winter time (Adjusted β = 61.06; 95% CI: 0.71, 121.41), public health control measures (prohibition of entering to into an area with high number of COVID-19 infections (Adjusted β = -169.34; 95% CI: -233.52, -105.16), and restriction of travelling also reduced the number of influenza patients (Adjusted β = -66.88; 95% CI: -125.15, -8.62) were associated with number of influenza patients. This study commends strategies in monitoring influenza patients to focus on the areas with low income, high population density, and in specific seasons. Public health and social measures which can be implemented are prohibition of entering to risk-areas (lock down), and restriction of travelling across provinces which their effectiveness in reducing influenza infections.

Disparities and Outcomes in the First and Second Year of the Pandemic on Events of Acute Myocardial Infarction in Coronavirus Disease 2019 Patients

Background and Objectives: Coronavirus disease 2019 (COVID-19) caused several cardiovascular complications, including acute myocardial infarction (AMI), in infected patients. This study aims to understand the overall trends of AMI among COVID-19 patients during the first two years of the pandemic and the disparities and outcomes between the first and second years. Materials and Methods: The retrospective analysis was conducted via the 2020 and 2021 National Inpatient Sample (NIS) database for hospitalizations between April 2020 and December 2021 being analyzed for adults with a primary diagnosis of COVID-19 who experienced events of AMI. A comparison of month-to-month events of AMI and mortality of AMI patients with concomitant COVID-19 was made alongside their respective patient characteristics. Results: Out of 2,541,992 COVID-19 hospitalized patients, 3.55% experienced AMI. The highest rate of AMI was in December 2021 (4.35%). No statistical differences in trends of AMI mortality were noted over the 21 months. AMI cases in 2021 had higher odds of undergoing PCI (aOR 1.627, p < 0.01). They experienced higher risks of acute kidney injury (aOR 1.078, p < 0.01), acute ischemic stroke (aOR 1.215, p < 0.01), cardiac arrest (aOR 1.106, p < 0.01), need for mechanical ventilation (aOR 1.133, p < 0.01), and all-cause mortality (aOR 1.032, 95% CI 1.001-1.064, p = 0.043). Conclusions: The incidence of AMI among COVID-19 patients fluctuated over the 21 months of this study, with a peak in December 2021. COVID-19 patients reporting AMI in 2021 experienced higher overall odds of multiple complications, which could relate to the exhaustive burden of the pandemic in 2021 on healthcare, the changing impact of the virus variants, and the hesitancy of infected patients to seek care.

Evaluating functionalities of food components by a model simulating human intestinal microbiota constructed at Kobe University

In this era of pandemics, reducing the risk of lifestyle-related diseases (LRD) by functional foods is of paramount importance. The conventional process of functional food development almost invariably involves in vitro, animal, and human intervention trials, but differences in intestinal environments between humans and experimental animals make it difficult to develop functional foods that are truly effective in humans. Thus, it is necessary to construct a model that simulates the human intestinal environment to evaluate the functionality of any food component before subjecting it to a human intervention trial. In this review, we provide an overview of a model simulating human intestinal microbiota constructed at Kobe University and its use as a tool to identify food components that contribute to the prevention and treatment of LRD.

Geographical and practical challenges in the implementation of digital health passports for cross-border COVID-19 pandemic management: a narrative review and framework for solutions

The rapid global spread of infectious diseases, epitomized by the recent COVID-19 pandemic, has highlighted the critical need for effective cross-border pandemic management strategies. Digital health passports (DHPs), which securely store and facilitate the sharing of critical health information, including vaccination records and test results, have emerged as a promising solution to enable safe travel and access to essential services and economic activities during pandemics. However, the implementation of DHPs faces several significant challenges, both related to geographical disparities and practical considerations, necessitating a comprehensive approach for successful global adoption. In this narrative review article, we identify and elaborate on the critical geographical and practical barriers that hinder global adoption and the effective utilization of DHPs. Geographical barriers are complex, encompassing disparities in vaccine access, regulatory inconsistencies, differences across countries in data security and users' privacy policies, challenges related to interoperability and standardization, and inadequacies in technological infrastructure and limited access to digital technologies. Practical challenges include the possibility of vaccine contraindications and breakthrough infections, uncertainties surrounding natural immunity, and limitations of standard tests in assessing infection risk. To address geographical disparities and enhance the functionality and interoperability of DHPs, we propose a framework that emphasizes international collaboration to achieve equitable access to vaccines and testing resources. Furthermore, we recommend international cooperation to establish unified vaccine regulatory frameworks, adopting globally accepted standards for data privacy and protection, implementing interoperability protocols, and taking steps to bridge the digital divide. Addressing practical challenges requires a meticulous approach to assessing individual risk and augmenting DHP implementation with rigorous health screenings and personal infection prevention measures. Collectively, these initiatives contribute to the development of robust and inclusive cross-border pandemic management strategies, ultimately promoting a safer and more interconnected global community in the face of current and future pandemics.

Revealing associations between spatial time series trends of COVID-19 incidence and human mobility: an analysis of bidirectionality and spatiotemporal heterogeneity

BACKGROUND

Using human mobility as a proxy for social interaction, previous studies revealed bidirectional associations between COVID-19 incidence and human mobility. For example, while an increase in COVID-19 cases may affect mobility to decrease due to lockdowns or fear, conversely, an increase in mobility can potentially amplify social interactions, thereby contributing to an upsurge in COVID-19 cases. Nevertheless, these bidirectional relationships exhibit variations in their nature, evolve over time, and lack generalizability across different geographical contexts. Consequently, a systematic approach is required to detect functional, spatial, and temporal variations within the intricate relationship between disease incidence and mobility.

METHODS

We introduce a spatial time series workflow to investigate the bidirectional associations between human mobility and disease incidence, examining how these associations differ across geographic space and throughout different waves of a pandemic. By utilizing daily COVID-19 cases and mobility flows at the county level during three pandemic waves in the US, we conduct bidirectional Granger causality tests for each county and wave. Furthermore, we employ dynamic time warping to quantify the similarity between the trends of disease incidence and mobility, enabling us to map the spatial distribution of trends that are either similar or dissimilar.

RESULTS

Our analysis reveals significant bidirectional associations between COVID-19 incidence and mobility, and we develop a typology to explain the variations in these associations across waves and counties. Overall, COVID-19 incidence exerts a greater influence on mobility than vice versa, but the correlation between the two variables exhibits a stronger connection during the initial wave and weakens over time. Additionally, the relationship between COVID-19 incidence and mobility undergoes changes in direction and significance for certain counties across different waves. These shifts can be attributed to alterations in disease control measures and the presence of evolving confounding factors that differ both spatially and temporally.

CONCLUSIONS

This study provides insights into the spatial and temporal dynamics of the relationship between COVID-19 incidence and human mobility across different waves. Understanding these variations is crucial for informing the development of more targeted and effective healthcare policies and interventions, particularly at the city or county level where such policies must be implemented. Although we study the association between mobility and COVID-19 incidence, our workflow can be applied to investigate the associations between the time series trends of various infectious diseases and relevant contributing factors, which play a role in disease transmission.

Epidemiological Characteristics of COVID-19 during Seven Consecutive Epidemiological Waves (2020-2022) in the North Bačka District, Serbia

The COVID-19 pandemic continues to pose a threat to global public health. The purpose of this research was to determine the epidemiological characteristics of COVID-19 in the North Bačka district while observing seven pandemic waves. The cross-sectional study was based on data from the COVID-19 surveillance database of the Institute for Public Health of Vojvodina during the period from March 2020 to December 2022. A total of 38,685 primary infections and 4067 reinfections caused by SARS-CoV-2 were notified. Pandemic waves caused by the Delta variant (cumulative incidence rate of 2482.37/100,000) and by the Omicron variant (cumulative incidence rate of 2994.45/100,000) emerged as significant focal points during the surveillance period. Over the course of three consecutive years (2020-2022), women were more affected (50.11%, 54.03%, and 55.68%, respectively). The highest incidence rates in age-specific categories were recorded in 2021 for the age group 40-49 (1345.32 per 10,000 inhabitants), while in 2022, they shifted towards the elderly population. Regarding vaccination status at the time of diagnosis, in 2021, around 15% of patients were vaccinated, while in 2022, the number increased to 37%. The most widely received vaccine was BBIBP-CorV (67.45%), followed by BNT162b2 (19.81%), Gam-COVID-Vac (9.31%), and ChAdOx1 nCoV-19 (3.42%) vaccine. The implementation of stringent public health measures and their mitigation, together with the emergence of new variants, influenced the dynamics of COVID-19 pandemic waves in the North Bačka district. Notably, throughout the study period, the working-age population was the most affected, along with females, with a mild clinical presentation dominating. Reinfections were most frequently recorded during the latter pandemic waves. Dealing with this pandemic has provided some valuable lessons for the development of future strategies in the case of a similar public health crisis.

Social Media and Selfie-Related Mortality Amid COVID-19: Interrupted Time Series Analysis

BACKGROUND

COVID-19 had a considerable impact on mortality, but its effect on behaviors associated with social media remains unclear. As travel decreased due to lockdowns during the pandemic, selfie-related mortality may have decreased, as fewer individuals were taking smartphone photographs in risky locations.

OBJECTIVE

In this study, we examined the effect of the COVID-19 pandemic on trends in selfie-related mortality.

METHODS

We identified fatal selfie-related injuries reported in web-based news reports worldwide between March 2014 and April 2021, including the deaths of individuals attempting a selfie photograph or anyone else present during the incident. The main outcome measure was the total number of selfie-related deaths per month. We used interrupted time series regression to estimate the monthly change in the number of selfie-related deaths over time, comparing the period before the pandemic (March 2014 to February 2020) with the period during the pandemic (March 2020 to April 2021).

RESULTS

The study included a total of 332 selfie-related deaths occurring between March 2014 and April 2021, with 18 (5.4%) deaths during the pandemic. Most selfie-related deaths occurred in India (n=153, 46.1%) and involved men (n=221, 66.6%) and young individuals (n=296, 89.2%). During the pandemic, two-thirds of selfie-related deaths were due to falls, whereas a greater proportion of selfie-related deaths before the pandemic were due to drowning. Based on interrupted time series regression, there was an average of 1.3 selfie-related deaths per month during the pandemic, compared with 4.3 deaths per month before the pandemic. The number of selfie-related deaths decreased by 2.6 in the first month of the pandemic alone and continued to decrease thereafter.

CONCLUSIONS

Our findings indicate that the COVID-19 pandemic led to a marked decrease in selfie-related mortality, suggesting that lockdowns and travel restrictions likely prevented hazardous selfie-taking. The decrease in selfie-related mortality occurred despite a potential increase in social media use during the pandemic.

Vaccination, human mobility, and COVID-19 health outcomes: Empirical comparison before and during the outbreak of SARS-Cov-2 B.1.1.529 (Omicron) variant

The B.1.1.529 (Omicron) variant surge has raised concerns about the effectiveness of vaccines and the impact of imprudent reopening. Leveraging over two years of county-level COVID-19 data in the US, this study aims to investigate relationships among vaccination, human mobility, and COVID-19 health outcomes (assessed via case rate and case-fatality rate), controlling for socioeconomic, demographic, racial/ethnic, and partisan factors. A set of cross-sectional models was first fitted to empirically compare disparities in COVID-19 health outcomes before and during the Omicron surge. Then, time-varying mediation analyses were employed to delineate how the effects of vaccine and mobility on COVID-19 health outcomes vary over time. Results showed that vaccine effectiveness against case rate lost significance during the Omicron surge, while its effectiveness against case-fatality rate remained significant throughout the pandemic. We also documented salient structural inequalities in COVID-19-related outcomes, with disadvantaged populations consistently bearing a larger brunt of case and death tolls, regardless of high vaccination rates. Last, findings revealed that mobility presented a significantly positive relationship with case rates during each wave of variant outbreak. Mobility substantially mediated the direct effect from vaccination to case rate, leading to a 10.276 % (95 % CI: 6.257, 14.294) decrease in vaccine effectiveness on average. Altogether, our study implies that sole reliance on vaccination to halt COVID-19 needs to be re-examined. Well-resourced and coordinated efforts to enhance vaccine effectiveness, mitigate health disparity and selectively loosen non-pharmaceutical interventions are essential to bringing the pandemic to an end.

The effect of social movements on COVID-19 case increases and death in Turkey

Objectives

This study aimed to determine whether there is a correlation between COVID-19 cases and deaths because of COVID-19 and community movements in Turkey and to develop a strategy for future outbreaks.

Methods

The study's data covers COVID-19 cases and deaths between March 11, 2020, and December 16, 2021, and Turkey's Google community movements between these dates. The COVID-19 cases and deaths were obtained from Turkey's Ministry of Health COVID-19 Information Platform. Community mobility collated by Google is retail and recreation, supermarket and pharmacy, parks, public transport, workplaces visits, and residential. The data were transferred via "SPSS (Statistical Package for Social Sciences) for Windows 25.0 (SPSS Inc, Chicago, IL" and statistical analysis was performed. The Spearman correlation test was used as a statistical method. In the Kruskal-Wallis Test, categorical variables were created using increases and decreases in community movements based on the baseline.

Results

A weak positive correlation between daily COVID-19 deaths and supermarket and pharmacy activity (r = 0.28 p < 0.01). A weak negative correlation with park activity (r = -0.23 p < 0.01). A weakly positive and significant relationship with workplace visits mobility (r = 0.10 p < 0.05). There was a weak positive significant relationship with public transport mobility (r = 0.10 p < 0.01), including a weak positive significant relationship with residential (r = 0.12 p < 0.01).

Conclusions

Social distancing measures (such as reducing community mobility) and educating people on viral transmission in possible epidemics will save us time developing new diagnostic tests and vaccine studies.

The Effect of Publicly Available COVID-19 Information on the Functioning of Society, Businesses, Government and Local Institutions: A Case Study from Poland

The COVID-19 pandemic has seriously affected many world regions' societies and economies. The pandemic and the restrictions introduced in response to it have impacted social behaviour and the operation of businesses in various ways. The present study aimed to verify the impact of information on the COVID-19 outbreak in Poland on the frequency with which residents of Rybnik and the surrounding areas chose to stay in their houses or apartments, as well as the functioning of businesses. The originality of the study lies in the use of mediation modelling, in which the following were used as variables describing the epidemic in Poland: new cases of SARS-CoV-2 infection in Poland; the number of deaths caused by SARS-CoV-2 infection in Poland; and the number of people quarantined due to the COVID-19 epidemic in Poland. On the other hand, data on tap water consumption were used as variables describing the frequency with which residents of Rybnik and the surrounding areas chose to stay in their houses or apartments, the operation of businesses in and around the city, and the operation of government and local government institutions. The study's conclusions confirm that publicly available information about the COVID-19 outbreak in Poland affected the frequency of residents of Rybnik and the surrounding areas staying in their houses or apartments as well as the functioning of businesses in Rybnik and the surrounding areas. The increasing number of people who died due to SARS-CoV-2 infection was associated with the limited functioning of government and local government institutions in and around Rybnik, which contributed to restrictions on the functioning of service industry companies in the city and surrounding areas.

Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model

The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte-Concord-Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model's predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.