COVID-19 in Asia: Transmission factors, re-opening policies, and vaccination simulation

Targeting Non-Eosinophilic Immunological Pathways in COPD and AECOPD: Current Insights and Therapeutic Strategies

COPD is a multifactorial illness characterized by a long-term restriction of airflow and an inflammatory reaction in the lungs. The associated emphysema leads to the breakdown of alveolar proteins and abnormal expansion of the lung air spaces. Chronic bronchitis caused by the same disease can result in increased deposition of structural proteins, narrowing of the airways, and excessive mucus secretion leading to acute exacerbation of COPD (AECOPD). The most commonly prescribed medications for it, such as glucocorticoids and bronchodilators, provide important therapeutic benefits, but they also have negative side effects, including immunosuppression and infection. Therefore, it is necessary to develop medications for the treatment of COPD that specifically target the immune system and molecular components. This review focuses on non-eosinophilic aspects of immunological modulation in COPD management. Since, existing literature extensively covers eosinophilic inflammation, this review aims to fill the gap by examining alternative immunological pathways and their therapeutic implications. The findings suggest that targeting specific immune responses may enhance treatment efficacy while minimizing adverse effects associated with traditional therapies. In summary, this review emphasizes the importance of advancing research into non-eosinophilic immunological mechanisms in COPD, prescribing for the development of novel therapies that can more effectively manage this disease.

In-depth analysis of ambient air pollution changes due to the COVID-19 pandemic in the Asian Monsoon region

The COVID-19 pandemic has given a chance for researchers and policymakers all over the world to study the impact of lockdowns on air quality in each country. This review aims to investigate the impact of the restriction of activities during the lockdowns in the Asian Monsoon region on the main criteria air pollutants. The various types of lockdowns implemented in each country were based on the severity of the COVID-19 pandemic. The concentrations of major air pollutants, especially particulate matter (PM) and nitrogen dioxide (NO2), reduced significantly in all countries, especially in South Asia (India and Bangladesh), during periods of full lockdown. There were also indications of a significant reduction of sulfur dioxide (SO2) and carbon monoxide (CO). At the same time, there were indications of increasing trends in surface ozone (O3), presumably due to nonlinear chemistry associated with the reduction of oxides of nitrogens (NOX). The reduction in the concentration of air pollutants can also be seen in satellite images. The results of aerosol optical depth (AOD) values followed the PM concentrations in many cities. A significant reduction of NO2 was recorded by satellite images in almost all cities in the Asian Monsoon region. The major reductions in air pollutants were associated with reductions in mobility. Pakistan, Bangladesh, Myanmar, Vietnam, and Taiwan had comparatively positive gross domestic product growth indices in comparison to other Asian Monsoon nations during the COVID-19 pandemic. A positive outcome suggests that the economy of these nations, particularly in terms of industrial activity, persisted during the COVID-19 pandemic. Overall, the lockdowns implemented during COVID-19 suggest that air quality in the Asian Monsoon region can be improved by the reduction of emissions, especially those due to mobility as an indicator of traffic in major cities.

Dispersal history of SARS-CoV-2 variants Alpha, Delta, and Omicron (BA.1) in Spain

Different factors influence the spread of SARS-CoV-2, from the inherent transmission capabilities of the different variants to the control measurements put in place. Here we studied the introduction of the Alpha, Delta, and Omicron-BA.1 variants of concern (VOCs) into Spain. For this, we collected genomic data from the GISAID database and combined it with connectivity data from different countries with Spain to perform a phylodynamic Bayesian analysis of the introductions. Our findings reveal that the introductions of these VOCs predominantly originated from France, especially in the case of Alpha. As travel restrictions were eased during the Delta and Omicron-BA.1 waves, the number of introductions from distinct countries increased, with the United Kingdom and Germany becoming significant sources of the virus. The largest number of introductions detected corresponded to the Delta wave, which was associated with fewer restrictions and the summer period, when Spain receives a considerable number of tourists. This research underscores the importance of monitoring international travel patterns and implementing targeted public health measures to manage the spread of SARS-CoV-2.

Dispersal history of SARS-CoV-2 in Galicia, Spain

The dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission are influenced by a variety of factors, including social restrictions and the emergence of distinct variants. In this study, we delve into the origins and dissemination of the Alpha, Delta, and Omicron-BA.1 variants of concern in Galicia, northwest Spain. For this, we leveraged genomic data collected by the EPICOVIGAL Consortium and from the GISAID database, along with mobility information from other Spanish regions and foreign countries. Our analysis indicates that initial introductions during the Alpha phase were predominantly from other Spanish regions and France. However, as the pandemic progressed, introductions from Portugal and the United States became increasingly significant. The number of detected introductions varied from 96 and 101 for Alpha and Delta to 39 for Omicron-BA.1. Most of these introductions left a low number of descendants (<10), suggesting a limited impact on the evolution of the pandemic in Galicia. Notably, Galicia's major coastal cities emerged as critical hubs for viral transmission, highlighting their role in sustaining and spreading the virus. This research emphasizes the critical role of regional connectivity in the spread of SARS-CoV-2 and offers essential insights for enhancing public health strategies and surveillance measures.

Impact of COVID-19 on essential service provision for reproductive, maternal, neonatal, and child health in the Southeast Asia region: a systematic review

Background

There is increasing evidence that the COVID-19 pandemic has impacted adversely on the provision of essential health services globally. The Southeast Asia region (SEAR) has experienced extremely high rates of COVID-19 infection, with potential adverse impacts on provision of reproductive, maternal, neonatal, and child health (RMNCH) services.

Methods

We conducted a systematic literature review of quantitative evidence to characterise the impact of COVID-19 on the provision of essential RMNCH services across the SEAR. Studies published between December 2019 and May 2022 were included in the study. The quality of studies was evaluated using the Joanna Briggs Institute Critical Appraisal Checklist.

Findings

We reviewed 1924 studies and analysed data from 20 peer-reviewed studies and three reports documenting quantitative pre-post estimates of RMNCH service disruption because of the COVID-19 pandemic. Eleven studies were of low methodological quality, in addition to seven and five studies of moderate and high methodological qualities respectively. Six countries in the region were represented in the included studies: India (11 studies), Bangladesh (4), Nepal (3), Sri Lanka (1), Bhutan (1) and Myanmar (1). These countries demonstrated a wide reduction in antenatal care services (-1.6% to -69.6%), facility-based deliveries (-2.3% to -52.4%), child immunisation provision (-13.5% to -87.7%), emergency obstetric care (+4.0% to -76.6%), and family planning services (-4.2% to -100%).

Interpretation

There have been large COVID-19 pandemic related disruptions for a wide range of RMNCH essential health service indicators in several SEAR countries. Notably, we found a higher level of service disruption than the WHO PULSE survey estimates. If left unaddressed, such disruptions may set back hard-fought gains in RMNCH outcomes across the region. The absence of studies in five SEAR countries is a priority evidence gap that needs addressing to better inform policies for service protection.

Funding

WHO Sri Lanka Country Office.

Dispersal history of SARS-CoV-2 in Galicia, Spain

The dynamics of SARS-CoV-2 transmission are influenced by a variety of factors, including social restrictions and the emergence of distinct variants. In this study, we delve into the origins and dissemination of the Alpha, Delta, and Omicron variants of concern in Galicia, northwest Spain. For this, we leveraged genomic data collected by the EPICOVIGAL Consortium and from the GISAID database, along with mobility information from other Spanish regions and foreign countries. Our analysis indicates that initial introductions during the Alpha phase were predominantly from other Spanish regions and France. However, as the pandemic progressed, introductions from Portugal and the USA became increasingly significant. Notably, Galicia's major coastal cities emerged as critical hubs for viral transmission, highlighting their role in sustaining and spreading the virus. This research emphasizes the critical role of regional connectivity in the spread of SARS-CoV-2 and offers essential insights for enhancing public health strategies and surveillance measures.

Conditioning factors in the spreading of Covid-19 - Does geography matter?

There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective of the paper is to provide an updated literature review on geographical studies analysing the factors which influenced COVID-19 spreading. This literature review took into account not only the geographical aspects but also the COVID-19-related outcomes (infections and deaths) allowing to discern the potential influencing role of the geographic factors per type of outcome. A total of 112 scientific articles were selected, reviewed and categorized according to subject area, aim, country/region of study, considered geographic and COVID-19 variables, spatial and temporal units of analysis, methodologies, and main findings. Our literature review showed that territorial features may have played a role in determining the uneven geography of COVID-19; for instance, a certain agreement was found regarding the direct relationship between urbanization degree and COVID-19 infections. For what concerns climatic factors, temperature was the variable that correlated the best with COVID-19 infections. Together with climatic factors, socio-demographic ones were extensively taken into account. Most of the analysed studies agreed that population density and human mobility had a significant and direct relationship with COVID-19 infections and deaths. The analysis of the different approaches used to investigate the role of geographic factors in the spreading of the COVID-19 pandemic revealed that the significance/representativeness of the outputs is influenced by the scale considered due to the great spatial variability of geographic aspects. In fact, a more robust and significant association between geographic factors and COVID-19 was found by studies conducted at subnational or local scale rather than at country scale.

Impact of vaccination and non-pharmacological interventions on COVID-19: a review of simulation modeling studies in Asia

Introduction

Epidemiological modeling is widely used to offer insights into the COVID-19 pandemic situation in Asia. We reviewed published computational (mathematical/simulation) models conducted in Asia that assessed impacts of pharmacological and non-pharmacological interventions against COVID-19 and their implications for vaccination strategy.

Methods

A search of the PubMed database for peer-reviewed, published, and accessible articles in English was performed up to November 2022 to capture studies in Asian populations based on computational modeling of outcomes in the COVID-19 pandemic. Extracted data included model type (mechanistic compartmental/agent-based, statistical, both), intervention type (pharmacological, non-pharmacological), and procedures for parameterizing age. Findings are summarized with descriptive statistics and discussed in terms of the evolving COVID-19 situation.

Results

The literature search identified 378 results, of which 59 met criteria for data extraction. China, Japan, and South Korea accounted for approximately half of studies, with fewer from South and South-East Asia. Mechanistic models were most common, either compartmental (61.0%), agent-based (1.7%), or combination (18.6%) models. Statistical modeling was applied less frequently (11.9%). Pharmacological interventions were examined in 59.3% of studies, and most considered vaccination, except one study of an antiviral treatment. Non-pharmacological interventions were also considered in 84.7% of studies. Infection, hospitalization, and mortality were outcomes in 91.5%, 30.5%, and 30.5% of studies, respectively. Approximately a third of studies accounted for age, including 10 that also examined mortality. Four of these studies emphasized benefits in terms of mortality from prioritizing older adults for vaccination under conditions of a limited supply; however, one study noted potential benefits to infection rates from early vaccination of younger adults. Few studies (5.1%) considered the impact of vaccination among children.

Conclusion

Early in the COVID-19 pandemic, non-pharmacological interventions helped to mitigate the health burden of COVID-19; however, modeling indicates that high population coverage of effective vaccines will complement and reduce reliance on such interventions. Thus, increasing and maintaining immunity levels in populations through regular booster shots, particularly among at-risk and vulnerable groups, including older adults, might help to protect public health. Future modeling efforts should consider new vaccines and alternative therapies alongside an evolving virus in populations with varied vaccination histories.

Correlation Between Daily PM10, Nitrogen Dioxide, and Ozone Measurements with the Stringency Index in 15 Different Districts of a Big Metropolis

OBJECTIVE

Studies have investigated the effects of lockdowns on air quality around the world and found that fine particulate matter and nitrogen dioxide concentrations decreased due to reduced human activity, while ozone concentrations increased. In this study, we aimed to evaluate the correlation between daily stringency index values of our country and daily PM10, nitrogen dioxide, and ozone measurements in different districts of Istanbul between March 1, 2020, and February 28, 2022.

MATERIAL AND METHODS

Ministry of Environment and Urbanization and National Air Quality Monitoring Network data on Istanbul air quality monitoring stations were used. The analysis included 15 stations that can monitor at least 75% of the days in a year. PM10, nitrogen dioxide, and ozone were the main pollutants analyzed.

RESULTS

There was negative correlation between daily PM10 measurements and daily stringency index values in 3 stations; there was positive correlation in 6 stations. Between daily stringency index values and daily nitrogen dioxide measurements, there was a negative correlation in 3 stations and a positive correlation in 1 station. The daily measurements of 1 station showed a negative correlation with the daily values of stringency index for both PM10 and nitrogen dioxide. In 1 station, while PM10 measures were negatively correlated with stringency index, nitrogen dioxide measurements were positively correlated.

CONCLUSION

This study showed that pandemic limitations could not improve Istanbul's air quality everywhere. For adequate evaluation of impact of the limitations on air quality, it may be more relevant to study the socioeconomic infrastructure of each living area, the sociospatial inequality, industrial employment, the number of households, the density of employee class, and so on with all influencing factors that could have contributed to these various changes.

The Nexus Between COVID-19 Factors and Air Pollution

Background and Objective

There have been significant effects of the current coronavirus-19 (COVID-19) infection outbreak on many facets of everyday life, particularly the environment. Despite the fact that a number of studies have already been published on the topic, an analysis of those studies' findings on COVID-19's effects on environmental pollution is still lacking. The goal of the research is to look into greenhouse gas emissions and air pollution in Bangladesh when COVID-19 is under rigorous lockdown. The specific drivers of the asymmetric relationship between air pollution and COVID-19 are being investigated.

Methods

The nonlinear relationship between carbon dioxide ( C O 2 ) emissions, fine particulate matter ( P M 2 . 5 ) , and COVID-19, as well as its precise components, are also being investigated. To examine the asymmetric link between COVID-19 factors on C O 2 emissions and P M 2 . 5 , we employed the nonlinear autoregressive distributed lag (NARDL) model. Daily positive cases and daily confirmed death by COVID-19 are considered the factors of COVID-19, with lockdown as a dummy variable.

Results

The bound test confirmed the existence of long-run and short-run relationships between variables. Bangladesh's strict lockdown, enforced in reaction to a surge of COVID-19 cases, reduced air pollution and dangerous gas emissions, mainly C O 2 , according to the dynamic multipliers graph.

The Effectiveness of Mobility Restrictions on Controlling the Spread of COVID-19 in a Resistant Population

Human mobility plays an important role in the spread of COVID-19. Given this knowledge, countries implemented mobility-restricting policies. Concomitantly, as the pandemic progressed, population resistance to the virus increased via natural immunity and vaccination. We address the question: "What is the impact of mobility-restricting measures on a resistant population?" We consider two factors: different types of points of interest (POIs)-including transit stations, groceries and pharmacies, retail and recreation, workplaces, and parks-and the emergence of the Delta variant. We studied a group of 14 countries and estimated COVID-19 transmission based on the type of POI, the fraction of population resistance, and the presence of the Delta variant using a Pearson correlation between mobility and the growth rate of cases. We find that retail and recreation venues, transit stations, and workplaces are the POIs that benefit the most from mobility restrictions, mainly if the fraction of the population with resistance is below 25-30%. Groceries and pharmacies may benefit from mobility restrictions when the population resistance fraction is low, whereas in parks, there is little advantage to mobility-restricting measures. These results are consistent for both the original strain and the Delta variant; Omicron data were not included in this work.

Exploring the Role of Industry 4.0 and Simulation as a Solution to the COVID-19 Outbreak: a Literature Review

The COVID-19 pandemic was an unexpected and disruptive event that significantly affected the performance of manufacturing systems and supply chains in various sectors. In this paper, a literature review is provided, which investigates the role that Industry 4.0 technologies and simulation tools have played in addressing the effects of the pandemic crisis. Specifically, a bibliometric analysis provides an overview of the most influential technologies through a study of the most used keywords. While a document analysis, conducted on critical papers that concern real case studies, shows that so far simulation provided support in four main areas: energy consumption, healthcare supply chain & contact tracing, food supply chain, and in general supply chain management. The main outcome of this research work is that Industry 4.0 technologies and simulation models were particularly important during the pandemic crisis and their properties deserve to be deeply exploited in the near future.

Estimating the time-dependent effective reproduction number and vaccination rate for COVID-19 in the USA and India

The effective reproduction number, $ R_t $, is a vital epidemic parameter utilized to judge whether an epidemic is shrinking, growing, or holding steady. The main goal of this paper is to estimate the combined $ R_t $ and time-dependent vaccination rate for COVID-19 in the USA and India after the vaccination campaign started. Accounting for the impact of vaccination into a discrete-time stochastic augmented SVEIR (Susceptible-Vaccinated-Exposed-Infectious-Recovered) model, we estimate the time-dependent effective reproduction number $ (R_t) $ and vaccination rate $ (\xi_t) $ for COVID-19 by using a low pass filter and the Extended Kalman Filter (EKF) approach for the period February 15, 2021 to August 22, 2022 in India and December 13, 2020 to August 16, 2022 in the USA. The estimated $ R_t $ and $ \xi_t $ show spikes and serrations with the data. Our forecasting scenario represents the situation by December 31, 2022 that the new daily cases and deaths are decreasing for the USA and India. We also noticed that for the current vaccination rate, $ R_t $ would remain greater than one by December 31, 2022. Our results are beneficial for the policymakers to track the status of the effective reproduction number, whether it is greater or less than one. As restrictions in these countries ease, it is still important to maintain safety and preventive measures.

Developing a Roadmap for Mass Vaccination of COVID-19 in Iran: A Qualitative Study

BACKGROUND

Nowadays, the Covid-19 pandemic is 1 of the most important challenges worldwide, especially in terms of health. The most important strategy to prevent and control the Covid-19 pandemic is mass vaccination. This study aimed at developing a roadmap for the mass vaccination of COVID-19 in Iran.

METHODS

The current study was conducted using a qualitative approach with a content analysis method. In the first step, the review of literature and documents was carried out by a search in scientific databases. In the next step, the data were amassed via in-depth and semi-structured interviews with experts who were selected purposefully, including policymakers, health care workers, and managers. After this, 3 multidisciplinary expert panels for roadmap development were held.

RESULTS

Based on the literature review, interviews, and 3 stages of an expert panel, the final roadmap was developed with 5 dimensions. These included outcomes, planning and preparation, strategies, and preparation, implementation, monitoring, and evaluation.

CONCLUSIONS

This roadmap was developed to improve mass vaccination during the COVID-19 pandemic. According to our findings, it is strongly recommended that the vaccination roadmap with all the above-mentioned features and comprehensive structure should be applied to mitigate the consequences of the COVID-19 pandemic.

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors

Introduction

The COVID-19 restrictions have a lot of various peripheral negative and positive effects, like economic shocks and decreasing air pollution, respectively. Many studies showed NO2 reduction in most parts of the world.

Methods

Iran and its land and maritime neighbors have about 7.4% of the world population and 6.3% and 5.8% of World COVID-19 cases and deaths, respectively. The air pollution indices of them such as CH4 (Methane), CO_1 (CO), H2O (Water), HCHO (Tropospheric Atmospheric Formaldehyde), NO2 (Nitrogen oxides), O3 (ozone), SO2 (Sulfur Dioxide), UVAI_AAI [UV Aerosol Index (UVAI)/Absorbing Aerosol Index (AAI)] are studied from the First quarter of 2019 to the fourth quarter of 2021 with Copernicus Sentinel 5 Precursor (S5P) satellite data set from Google Earth Engine. The outliers are detected based on the depth functions. We use a two-sample t test, Wilcoxon test, and interval-wise testing for functional data to control the familywise error rate.

Result

The adjusted p value comparison between Q2 of 2019 and Q2 of 2020 in NO2 for almost all countries is statistically significant except Iraq, UAE, Bahrain, Qatar, and Kuwait. But, the CO and HCHO are not statistically significant in any country. Although CH4, O3, and UVAI_AAI are statistically significant for some countries. In the Q2 comparison for NO2 between 2020 and 2021, only Iran, Armenia, Turkey, UAE, and Saudi Arabia are statistically significant. However, Ch4 is statistically significant for all countries except Azerbaijan.

Conclusions

The comparison with and without adjusted p values declares the decreases in some air pollution in these countries.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40808-022-01528-x.

A meta-analysis result: Uneven influences of season, geo-spatial scale and latitude on relationship between meteorological factors and the COVID-19 transmission

Meteorological factors have been confirmed to affect the COVID-19 transmission, but current studied conclusions varied greatly. The underlying causes of the variance remain unclear. Here, we proposed two scientific questions: (1) whether meteorological factors have a consistent influence on virus transmission after combining all the data from the studies; (2) whether the impact of meteorological factors on the COVID-19 transmission can be influenced by season, geospatial scale and latitude. We employed a meta-analysis to address these two questions using results from 2813 published articles. Our results showed that, the influence of meteorological factors on the newly-confirmed COVID-19 cases varied greatly among existing studies, and no consistent conclusion can be drawn. After grouping outbreak time into cold and warm seasons, we found daily maximum and daily minimum temperatures have significant positive influences on the newly-confirmed COVID-19 cases in cold season, while significant negative influences in warm season. After dividing the scope of the outbreak into national and urban scales, relative humidity significantly inhibited the COVID-19 transmission at the national scale, but no effect on the urban scale. The negative impact of relative humidity, and the positive impacts of maximum temperatures and wind speed on the newly-confirmed COVID-19 cases increased with latitude. The relationship of maximum and minimum temperatures with the newly-confirmed COVID-19 cases were more susceptible to season, while relative humidity's relationship was more affected by latitude and geospatial scale. Our results suggested that relationship between meteorological factors and the COVID-19 transmission can be affected by season, geospatial scale and latitude. A rise in temperature would promote virus transmission in cold seasons. We suggested that the formulation and implementation of epidemic prevention and control should mainly refer to studies at the urban scale. The control measures should be developed according to local meteorological properties for individual city.

Social contributors for the rise of COVID-19 infections in South Asia: A large cross-sectional survey

Background

The ongoing global coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was first reported in South Asia on 30^th January 2020 in India. Ever since, certain countries have witnessed multiple waves of COVID-19, requiring attention by public health experts and strategists in the region. The objectives of this study are to assess social contributors to the recurrent waves of COVID-19 in South Asia including first demographic traits, second household characteristics and social measures, third workplace trends and personal protective equipment use, and fourth satisfaction and attitudes concerning public health measures and vaccination status. The study also aims to plan for control strategies focusing on India, Pakistan, Bangladesh, Sri Lanka, and Nepal, countries with the highest burden of COVID-19 in South Asia.

Methods

A population-based large cross-sectional study was conducted from 1st July to August 10th, 2021 using online mediums. The survey consisted of 31 questions divided into sociodemographic and COVID-19 status information, household characteristics and social measures, workplace trends and personal protective measures, satisfaction and attitudes towards public health measures, and vaccination status. Bivariate, receiver operating characteristic (ROC) analysis, and the Kruskal Wallis test was conducted for factors associated to COVID-19 infection and positive vaccination status.

Findings

We enrolled 1046 participants with 57.1% females and 41.8% males, comprising 48.9% healthcare workers. Statistically significant associations were found using ANOVA based on the Kruskal-Wallis test for differences between thoughts towards public health authorities implementing standard operating procedures (SOPs) and HCW status were statistically significant (P = 0.002). The most important social predictors for positive vaccination status based on the ROC analysis were gender (P < 0.001), job role (P < 0.001), income group (P < 0.001), healthcare worker status (P < 0.001), household member tested positive (P = 0.007), personal vehicle ownership (P < 0.001), job requiring close contacts (P < 0.001) and co-worker masking habits (P = 0.02).

Conclusions

Public health experts and strategists are required to focus control strategies on political and religious gatherings, reopening offices, noncompliance of SOPs by the masses, and crowded commuting to limit the reemergence of COVID-19 infections in countries with the highest burden in the region.

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The COVID-19 pandemic has spread across the South Asian region.

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Some countries such as Nepal and India amid others witnessed multiple deadly waves of the pandemic.

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This large cross-sectional survey assesses the social contributors to the rise of COVID-19 in South Asia.

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We assess demographic, social, workplaces, and personal trends.

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Various countries have employed public health restrictions to mitigate the spread of COVID-19.

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We contribute to public health surveillance data essential to make pivotal shifts in social policies and practice.

The impact of geo-environmental factors on global COVID-19 transmission: A review of evidence and methodology

Studies on Coronavirus Disease 2019 (COVID-19) transmission indicate that geo-environmental factors have played a significant role in the global pandemic. However, there has not been a systematic review on the impact of geo-environmental factors on global COVID-19 transmission in the context of geography. As such, we reviewed 49 well-chosen studies to reveal the impact of geo-environmental factors (including the natural environment and human activity) on global COVID-19 transmission, and to inform critical intervention strategies that could mitigate the worldwide effects of the pandemic. Existing studies frequently mention the impact of climate factors (e.g., temperature and humidity); in contrast, a more decisive influence can be achieved by human activity, including human mobility, health factors, and non-pharmaceutical interventions (NPIs). The above results exhibit distinct spatiotemporal heterogeneity. The related analytical methodology consists of sensitivity analysis, mathematical modeling, and risk analysis. For future studies, we recommend highlighting geo-environmental interactions, developing geographically statistical models for multiple waves of the pandemic, and investigating NPIs and care patterns. We also propose four implications for practice to combat global COVID-19 transmission.

The impact of COVID-19 on essential health service provision for endemic infectious diseases in the South-East Asia region: A systematic review

Background

There is increasing evidence that the COVID-19 pandemic has impacted adversely on the provision of essential health services. The South East Asia region (SEAR) has experienced extremely high rates of COVID-19 infection, and continues to bear a significant proportion of communicable disease burden worldwide.

Methods

We conducted a systematic literature review of quantitative evidence to estimate the impact of COVID-19 on the provision of essential prevention, detection, treatment, and management services for five high-burden infectious diseases across the SEAR.

Findings

A total of 2338 studies were reviewed, and 12 studies were included in our analysis, covering six countries across the SEAR (Bhutan, Sri Lanka, Nepal, Myanmar, Thailand, and India) for three conditions of interest (HIV, TB, dengue fever). We identified significant disruption to TB testing (range=25% to 77.9%) and diagnoses (range=50% to 58%) in India, Nepal, and Indonesia; and similar disruptions were observed for screening, new diagnoses and commencing HIV treatment in India and Thailand. There was also drastically reduced case detection for dengue fever (range=75% to 90% disrupted) in Bhutan and Sri Lanka. No studies were identified for malaria nor hepatitis in any country, and nor for any service in the remaining six SEAR countries.

Interpretation

We identified evidence of significant disruption to the prevention, diagnoses, treatment, and management of TB, HIV, and dengue fever due to the COVID-19 pandemic across multiple SEAR country settings. This has the potential to set back hard-fought gains in infectious disease control across the region. The lack of evidence for the impact of the pandemic on malaria and hepatitis services, and in the remaining six SEAR countries, is an important evidence gap that should be addressed in order to inform future policy for service protection and pandemic preparedness.

Funding

This work was supported by the WHO Sri Lanka Country office.

Improving vaccination coverage and offering vaccine to all school-age children allowed uninterrupted in-person schooling in King County, WA: Modeling analysis

The rapid spread of highly transmissible SARS-CoV-2 variants combined with slowing pace of vaccination in Fall 2021 created uncertainty around the future trajectory of the epidemic in King County, Washington, USA. We analyzed the benefits of offering vaccination to children ages 5-11 and expanding the overall vaccination coverage using mathematical modeling. We adapted a mathematical model of SARS-CoV-2 transmission, calibrated to data from King County, Washington, to simulate scenarios of vaccinating children aged 5-11 with different starting dates and different proportions of physical interactions (PPI) in schools being restored. Dynamic social distancing was implemented in response to changes in weekly hospitalizations. Reduction of hospitalizations and estimated time under additional social distancing measures are reported over the 2021-2022 school year. In the scenario with 85% vaccination coverage of 12+ year-olds, offering early vaccination to children aged 5-11 with 75% PPI was predicted to prevent 756 (median, IQR 301-1434) hospitalizations cutting youth hospitalizations in half compared to no vaccination and largely reducing the need for additional social distancing measures over the school year. If, in addition, 90% overall vaccination coverage was reached, 60% of remaining hospitalizations would be averted and the need for increased social distancing would almost certainly be avoided. Our work suggests that uninterrupted in-person schooling in King County was partly possible because reasonable precaution measures were taken at schools to reduce infectious contacts. Rapid vaccination of all school-aged children provides meaningful reduction of the COVID-19 health burden over this school year but only if implemented early. It remains critical to vaccinate as many people as possible to limit the morbidity and mortality associated with future epidemic waves.

COVID-19 social distancing compliance mechanisms: UK evidence

Non-compliance with social distancing (SD) measures clearly has negative effects on both public health and post-pandemic economic recovery. However, little is as yet known about people's views on and factors influencing their behavioral intentions toward SD measures. This study draws on moral disengagement theory and the norm-activation model to investigate mechanisms that promote or hinder compliance with SD measures. A longitudinal research approach was adopted to compare changes in the main factors over three periods of the COVID-19 pandemic in England (UK). The results reveal significant differences between the three periods regarding intentions to comply with SD measures, altruistic value, moral obligation and moral disengagement, with no significant change in ascription of responsibility. Residents showed the strongest intentions to comply with SD measures during the first national lockdown, with the highest moral obligation and lowest moral disengagement levels, compared with the lowest intention to comply during the first re-opening period. Altruistic value is important in promoting moral obligation and compliance with SD measures, whereas the predictive powers of ascription of responsibility and moral disengagement were weaker than expected. These findings offer guidance to policymakers and researchers in developing more effective policies and public communication strategies. The results suggest that communication is key to normalizing SD compliance, which can be achieved most effectively by fostering residents' altruistic value and moral considerations. Particular attention must be paid to re-opening periods between lockdowns, with clear messages to remind residents of prosocial aspects of SD compliance and public health. In addition to appropriate communication and education, technologies such as apps, QR codes and contactless shopping settings may also be used to facilitate compliance with SD measures.

Threshold dynamics of a stochastic SIHR epidemic model of COVID-19 with general population-size dependent contact rate

In this paper, we propose a stochastic SIHR epidemic model of COVID-19. A basic reproduction number $ R_{0}^{s} $ is defined to determine the extinction or persistence of the disease. If $ R_{0}^{s} < 1 $, the disease will be extinct. If $ R_{0}^{s} > 1 $, the disease will be strongly stochastically permanent. Based on realistic parameters of COVID-19, we numerically analyze the effect of key parameters such as transmission rate, confirmation rate and noise intensity on the dynamics of disease transmission and obtain sensitivity indices of some parameters on $ R_{0}^{s} $ by sensitivity analysis. It is found that: 1) The threshold level of deterministic model is overestimated in case of neglecting the effect of environmental noise; 2) The decrease of transmission rate and the increase of confirmed rate are beneficial to control the spread of COVID-19. Moreover, our sensitivity analysis indicates that the parameters $ \beta $, $ \sigma $ and $ \delta $ have significantly effects on $ R_0^s $.

COVID-19 pandemic control using restrictions and vaccination

This work deals with the impact of the vaccination in combination with a restriction parameter that represents non-pharmaceutical interventions measures applied to the compartmental SEIR model in order to control the COVID-19 epidemic. This restriction parameter is used as a control parameter, and the univariate autoregressive integrated moving average (ARIMA) is used to forecast the time series of vaccination of all individuals of a specific country. Having in hand the time series of the population fully vaccinated (real data + forecast), the Levenberg-Marquardt algorithm is used to fit an analytic function that models this evolution over time. Here, it is used two time series of real data that refer to a slow vaccination obtained from India and Brazil, and two faster vaccination as observed in Israel and the United States of America. Together with vaccination, two different control approaches are presented in this paper, which enable reduces the infected people successfully: namely, the feedback and nonfeedback control methods. Numerical results predict that vaccination can reduce the peaks of infections and the duration of the pandemic, however, a better result is achieved when the vaccination is combined with any restrictions or prevention policy.

Positive association between outdoor air pollution and the incidence and severity of COVID-19. A review of the recent scientific evidences

In June 2020, we published a review focused on assessing the influence of various air pollutants on the transmission of SARS-CoV-2, and the severity of COVID-19 in patients infected by the coronavirus. The results of most of those reviewed studies suggested that chronic exposure to certain air pollutants might lead to more severe and lethal forms of COVID-19, as well as delays/complications in the recovery of the patients. Since then, a notable number of studies on this topic have been published, including also various reviews. Given the importance of this issue, we have updated the information published since our previous review. Taking together the previous results and those of most investigations now reviewed, we have concluded that there is a significant association between chronic exposure to various outdoor air pollutants: PM2.5, PM10, O3, NO2, SO2 and CO, and the incidence/risk of COVID-19 cases, as well as the severity/mortality of the disease. Unfortunately, studies on the potential influence of other important air pollutants such as VOCs, dioxins and furans, or metals, are not available in the scientific literature. In relation to the influence of outdoor air pollutants on the transmission of SARS-CoV-2, although the scientific evidence is much more limited, some studies point to PM2.5 and PM10 as potential airborne transmitters of the virus. Anyhow, it is clear that environmental air pollution plays an important negative role in COVID-19, increasing its incidence and mortality.