Association between population density and infection rate suggests the importance of social distancing and travel restriction in reducing the COVID-19 pandemic

Time to establish an international vaccine candidate pool for potential highly infectious respiratory disease: a community's view

In counteracting highly infectious and disruptive respiratory diseases such as COVID-19, vaccination remains the primary and safest way to prevent disease, reduce the severity of illness, and save lives. Unfortunately, vaccination is often not the first intervention deployed for a new pandemic, as it takes time to develop and test vaccines, and confirmation of safety requires a period of observation after vaccination to detect potential late-onset vaccine-associated adverse events. In the meantime, nonpharmacologic public health interventions such as mask-wearing and social distancing can provide some degree of protection. As climate change, with its environmental impacts on pathogen evolution and international mobility continue to rise, highly infectious respiratory diseases will likely emerge more frequently and their impact is expected to be substantial. How quickly a safe and efficacious vaccine can be deployed against rising infectious respiratory diseases may be the most important challenge that humanity will face in the near future. While some organizations are engaged in addressing the World Health Organization's "blueprint for priority diseases", the lack of worldwide preparedness, and the uncertainty around universal vaccine availability, remain major concerns. We therefore propose the establishment of an international candidate vaccine pool repository for potential respiratory diseases, supported by multiple stakeholders and countries that contribute facilities, technologies, and other medical and financial resources. The types and categories of candidate vaccines can be determined based on information from previous pandemics and epidemics. Each participant country or region can focus on developing one or a few vaccine types or categories, together covering most if not all possible potential infectious diseases. The safety of these vaccines can be tested using animal models. Information for effective candidates that can be potentially applied to humans will then be shared across all participants. When a new pandemic arises, these pre-selected and tested vaccines can be quickly tested in RCTs for human populations.

Spatial association between socio-economic health service factors and sepsis mortality in Thailand

Sepsis is a significant global health issue causing organ failure and high mortality. The number of sepsis cases has recently increased in Thailand making it crucial to comprehend the factors behind these infections. This study focuses on exploring the spatial autocorrelation between socio-economic factors and health service factors on the one hand and sepsis mortality on the other. We applied global Moran's I, local indicators of spatial association (LISA) and spatial regression to examine the relationship between these variables. Based on univariate Moran's I scatter plots, sepsis mortality in all 77 provinces in Thailand were shown to exhibit a positive spatial autocorrelation that reached a significant value (0.311). The hotspots/ high-high (HH) clusters of sepsis mortality were mostly located in the central region of the country, while the coldspots/low-low (LL) clusters were observed in the north-eastern region. Bivariate Moran's I indicated a spatial autocorrelation between various factors and sepsis mortality, while the LISA analysis revealed 7 HH clusters and 5 LL clusters associated with population density. Additionally, there were 6 HH and 4 LL clusters in areas with the lowest average temperature, 4 HH and 2 LL clusters in areas with the highest average temperature, 8 HH and 5 LL clusters associated with night-time light and 6 HH and 5 LL clusters associated with pharmacy density. The spatial regression models conducted in this study determined that the spatial error model (SEM) provided the best fit, while the parameter estimation results revealed that several factors, including population density, average lowest and highest temperature, night-time light and pharmacy density, were positively correlated with sepsis mortality. The coefficient of determination (R2) indicated that the SEM model explained 56.4% of the variation in sepsis mortality. Furthermore, based on the Akaike Information Index (AIC), the SEM model slightly outperformed the spatial lag model (SLM) with an AIC value of 518.1 compared to 520.

SARS-CoV-2 case detection using community event-based surveillance system-February-September 2020: lessons learned from Senegal

The COVID-19 pandemic necessitated the rapid development and implementation of effective surveillance systems to detect and respond to the outbreak in Senegal. In this documentation, we describe the design and implementation of the Community Event-Based Surveillance (CEBS) system in Senegal to strengthen the existing Integrated Disease Surveillance and Response system. The CEBS system used a hotline and toll-free number to collect and triage COVID-19-related calls from the community. Data from the CEBS system were integrated with the national system for further investigation and laboratory testing. From February to September 2020, a total of 10 760 calls were received by the CEBS system, with 10 751 calls related to COVID-19. The majority of calls came from the Dakar region, which was the epicentre of the outbreak in Senegal. Of the COVID-19 calls, 50.2% were validated and referred to health districts for further investigation, and 25% of validated calls were laboratory-confirmed cases of SARS-CoV-2. The implementation of the CEBS system allowed for timely detection and response to potential COVID-19 cases, contributing to the overall surveillance efforts in the country. Lessons learned from this experience include the importance of decentralised CEBS, population sensitisation on hotlines and toll-free usage, and the potential role of Community Health Workers in triaging alerts that needs further analysis. This experience highlights the contribution of a CEBS system in Senegal and provides insights into the design and operation of such a system. The findings can inform other countries in strengthening their surveillance systems and response strategies.

The global dynamic transmissibility of COVID-19 and its influencing factors: an analysis of control measures from 176 countries

OBJECTIVE

To summarise the dynamic characteristics of COVID-19 transmissibility; To analyse and quantify the effect of control measures on controlling the transmissibility of COVID-19; To predict and compare the effectiveness of different control measures.

METHODS

We used the basic reproduction number ([Formula: see text]) to measure the transmissibility of COVID-19, the transmissibility of COVID-19 and control measures of 176 countries and regions from January 1, 2020 to May 14, 2022 were included in the study. The dynamic characteristics of COVID-19 transmissibility were summarised through descriptive research and a Dynamic Bayesian Network (DBN) model was constructed to quantify the effect of control measures on controlling the transmissibility of COVID-19.

RESULTS

The results show that the spatial transmissibility of COVID-19 is high in Asia, Europe and Africa, the temporal transmissibility of COVID-19 increases with the epidemic of Beta and Omicron strains. Dynamic Bayesian Network (DBN) model shows that the transmissibility of COVID-19 is negatively correlated with control measures. Restricting population mobility has the strongest effect, nucleic acid testing (NAT) has a strong effect, and vaccination has the weakest effect.

CONCLUSION

Strict control measures are essential for controlling the COVID-19 outbreak; Restricting population mobility and nucleic acid testing (NAT) have significant impacts on controlling the COVID-19 transmissibility, while vaccination has no significant impact. In light of these findings, future control measures may include the widespread use of new NAT technology and the promotion of booster immunization.

Geographic disparities in COVID-19 testing and outcomes in Florida

BACKGROUND

Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during the early stages of the pandemic can guide policies, inform allocation of control and prevention resources, and provide valuable baseline data to evaluate the effectiveness of interventions for mitigating health, economic and social impacts. Therefore, the objective of this study was to identify geographic disparities in COVID-19 testing, incidence, hospitalizations, and deaths during the first five months of the pandemic in Florida.  METHODS: Florida county-level COVID-19 data for the time period March-July 2020 were used to compute various COVID-19 metrics including testing rates, positivity rates, incidence risks, percent of hospitalized cases, hospitalization risks, case-fatality rates, and mortality risks. High or low risk clusters were identified using either Kulldorff's circular spatial scan statistics or Tango's flexible spatial scan statistics and their locations were visually displayed using QGIS.

RESULTS

Visual examination of spatial patterns showed high estimates of all COVID-19 metrics for Southern Florida. Similar to the spatial patterns, high-risk clusters for testing and positivity rates and all COVID-19 outcomes (i.e. hospitalizations and deaths) were concentrated in Southern Florida. The distributions of these metrics in the other parts of Florida were more heterogeneous. For instance, testing rates for parts of Northwest Florida were well below the state median (11,697 tests/100,000 persons) but they were above the state median for North Central Florida. The incidence risks for Northwest Florida were equal to or above the state median incidence risk (878 cases/100,000 persons), but the converse was true for parts of North Central Florida. Consequently, a cluster of high testing rates was identified in North Central Florida, while a cluster of low testing rate and 1-3 clusters of high incidence risks, percent of hospitalized cases, hospitalization risks, and case fatality rates were identified in Northwest Florida. Central Florida had low-rate clusters of testing and positivity rates but it had a high-risk cluster of percent of hospitalized cases.

CONCLUSIONS

Substantial disparities in the spatial distribution of COVID-19 outcomes and testing and positivity rates exist in Florida, with Southern Florida counties generally having higher testing and positivity rates and more severe outcomes (i.e. hospitalizations and deaths) compared to Northern Florida. These findings provide valuable baseline data that is useful for assessing the effectiveness of preventive interventions, such as vaccinations, in various geographic locations in the state. Future studies will need to assess changes in spatial patterns over time at lower geographical scales and determinants of any identified patterns.

Analysis of a German blood donor cohort reveals a high number of undetected SARS-CoV-2 infections and sex-specific differences in humoral immune response

Seroprevalence studies can contribute to a better assessment of the actual incidence of infection. Since long-term data for Germany are lacking, we determined the seroprevalence of immunoglobulin G (IgG) antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in residual plasma samples of 3,759 German regular blood donors between July 2020 and June 2021. Over almost the entire study period, the incidences determined based on our data were higher than those officially reported by the Robert Koch Institute, the public health institute in Germany. Using our serological testing strategy, we retrospectively detected natural infection in 206/3,759 (5.48%; 95% confidence interval (CI): 4.77-6.25) individuals. The IgG seroprevalence ranked from 5.15% (95% CI: 3.73-6.89) in Lower Saxony to 5.62% (95% CI: 4.57-6.84) in North Rhine Westphalia. The analyses of follow-up samples of 88 seropositive blood donors revealed a comparable fast decay of binding and neutralizing anti-SARS-CoV-2 IgG antibodies. The antibody avidity remained at a low level throughout the whole follow-up period of up to 181 days. Interestingly, female donors seem to express a stronger and longer lasting humoral immunity against the new coronavirus when compared to males. Conclusion: Overall, our data emphasizes that seroprevalence measurements can and should be used to understand the true incidence of infection better. Further characterization of follow-up samples from seropositive donors indicated rapid antibody waning with sex-specific differences concerning the strength and persistence of humoral immune response.

Vaccination practices, knowledge and attitudes regarding COVID-19 vaccines among Chinese university students: a cross-sectional study from a comprehensive university in Wuhan

OBJECTIVE

In China, a free national COVID-19 vaccination programme has been launched in spring 2021 and is ongoing across the country. This study aimed to preliminarily understand the current COVID-19 vaccination practices and the associated knowledge/attitude factors among Chinese university students.

DESIGN AND SETTING

A single-centre cross-sectional questionnaire-based study was conducted between 1 and 20 May 2021 among a convenience sample of Chinese university students recruited from a comprehensive university in Wuhan. A total of 359 valid questionnaires were obtained.

RESULTS

Just 1 month after the commencement of the university vaccination process, 75% of responding students had received the COVID-19 vaccination. Students without experience of COVID-19 infection were more likely to get vaccinated than those who had been infected. Most (57%) received information about COVID-19 vaccination through official announcements released by the university, and 67% were vaccinated at vaccination place arranged by the university. An interesting finding is that, although the overall vaccine uptake rate was 75%, nearly one-third of vaccinees did not know or incorrectly stated the type of COVID-19 vaccines that they had received. Mean knowledge test score was 3.05 out of 6, suggesting poor knowledge regarding COVID-19 vaccines. The vaccinated showed more positive attitudes towards the importance of establishing herd immunity through the COVID-19 vaccination programme for pandemic control than the non-vaccinated did. 'Effectiveness in preventing COVID-19' was perceived by 76% respondents as the most important factor contributing to COVID-19 vaccination. A total of 76% were worried about safety-related issues, including 'unknown long-term safety', 'vaccine allergy' and 'vaccine-caused COVID-19 infection'.

CONCLUSIONS

Data suggested a rapid and effective progress of the national COVID-19 vaccination programme in China among university students. Taking effective official actions, as well as enhancing the belief in vaccines' effectiveness, might be necessary for the good implementation of COVID-19 vaccination programmes.

An alternative approach-combination of lockdown and open in fighting COVID-19 pandemics

As the COVID-19 pandemic enters its third year and the omicron variant becomes dominant, we propose an alternative strategy for dealing with COVID-19, called hybrid lockdown, that is, the combination of lockdown (the centralized and organized lockdown of the high-risk population) and free mobility (normal mobility) of the low-risk population. Such an approach will enable a country or region, especially with a high population density, to achieve significant prevention and control the effects of the COVID-19 pandemic at the least cost.

Mortality in Four Waves of COVID-19 Is Differently Associated with Healthcare Capacities Affected by Economic Disparities

Background: The greatest challenges are imposed on the overall capacity of disease management when the cases reach the maximum in each wave of the pandemic. Methods: The cases and deaths for the four waves of COVID-19 in 119 countries and regions (CRs) were collected. We compared the mortality across CRs where populations experience different economic and healthcare disparities. Findings: Among 119 CRs, 117, 112, 111, and 55 have experienced 1, 2, 3, and 4 waves of COVID-19 disease, respectively. The average mortality rates at the disease turning point were 0.036, 0.019. 0.017, and 0.015 for the waves 1, 2, 3, and 4, respectively. Among 49 potential factors, income level, gross national income (GNI) per capita, and school enrollment are positively correlated with the mortality rates in the first wave, but negatively correlated with the rates of the rest of the waves. Their values for the first wave are 0.253, 0.346 and 0.385, respectively. The r value for waves 2, 3, and 4 are −0.310, −0.293, −0.234; −0.263, −0.284, −0.282; and −0.330, −0.394, −0.048, respectively. In high-income CRs, the mortality rates in waves 2 and 3 were 29% and 28% of that in wave 1; while in upper-middle-income CRs, the rates for waves 2 and 3 were 76% and 79% of that in wave 1. The rates in waves 2 and 3 for lower-middle-income countries were 88% and 89% of that in wave 1, and for low-income countries were 135% and 135%. Furthermore, comparison among the largest case numbers through all waves indicated that the mortalities in upper- and lower-middle-income countries is 65% more than that of the high-income countries. Interpretation: Conclusions from the first wave of the COVID-19 pandemic do not apply to the following waves. The clinical outcomes in developing countries become worse along with the expansion of the pandemic.

Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method

Background

The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA.

Objective

The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19.

Methods

We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages.

Results

We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic.

Conclusions

The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.

SARS-CoV-2 pre-exposure prophylaxis: A potential COVID-19 preventive strategy for high-risk populations, including healthcare workers, immunodeficient individuals, and poor vaccine responders

The unprecedented worldwide spread of SARS-CoV-2 has imposed severe challenges on global health care systems. The roll-out and widespread administration of COVID-19 vaccines has been deemed a major milestone in the race to restrict the severity of the infection. Vaccines have as yet not entirely suppressed the relentless progression of the pandemic, due mainly to the emergence of new virus variants, and also secondary to the waning of protective antibody titers over time. Encouragingly, an increasing number of antiviral drugs, such as remdesivir and the newly developed drug combination, Paxlovid® (nirmatrelvir/ritonavir), as well as molnupiravir, have shown significant benefits for COVID-19 patient outcomes. Pre-exposure prophylaxis (PrEP) has been proven to be an effective preventive strategy in high-risk uninfected people exposed to HIV. Building on knowledge from what is already known about the use of PrEP for HIV disease, and from recently gleaned knowledge of antivirals used against COVID-19, we propose that SARS-CoV-2 PrEP, using specific antiviral and adjuvant drugs against SARS-CoV-2, may represent a novel preventive strategy for high-risk populations, including healthcare workers, immunodeficient individuals, and poor vaccine responders. Herein, we critically review the risk factors for severe COVID-19 and discuss PrEP strategies against SARS-CoV-2. In addition, we outline details of candidate anti-SARS-CoV-2 PrEP drugs, thus creating a framework with respect to the development of alternative and/or complementary strategies to prevent COVID-19, and contributing to the global armamentarium that has been developed to limit SARS-CoV-2 infection, severity, and transmission.

Variations of COVID-19 mortality are affected by economic disparities across countries

BACKGROUND

When the COVID-19 case number reaches a maximum in a country, its capacity and management of health system face greatest challenge.

METHODS

We performed a cross-sectional study on data of turning points for cases and deaths for the first three waves of COVID-19 in countries with more than 5000 cumulative cases, as reported by Worldometers and WHO Coronavirus (COVID-19) Dashboard. We compared the case fatality rates (CFRs) and time lags (in unit of day) between the turning points of cases and deaths among countries in different development stages and potential influence factors. As of May 10, 2021, 106 out of 222 countries or regions (56%) reported more than 5000 cases. Approximately half of them have experienced all the three waves of COVID-19 disease. The average mortality rate at the disease turning point was 0.038 for the first wave, 0.020 for the second wave, and 0.023 for wave 3. In high-income countries, the mortality rates during the first wave are higher than that of the other income levels. However, the mortality rates during the second and third waves of COVID-19 were much lower than those of the first wave, with a significant reduction from 5.7% to 1.7% approximately 70%. At the same time, high-income countries exhibited a 2-fold increase in time lags during the second and the third waves compared to the first wave, suggesting that the periods between the cases and deaths turning point extended. High rates in the first wave in developed countries are associated to multiple factors including transportation, population density, and aging populations. In upper middle- and lower middle-income countries, the decreasing of mortality rates in the second and third waves were subtle or even reversed, with increased mortality during the following waves. In the upper and lower middle-income countries, the time lags were about 50% of the durations observed from high-income countries.

INTERPRETATION

Economy and medical resources affect the efficiency of COVID-19 mitigation and the clinical outcomes of the patients. The situation is likely to become even worse in the light of these countries' limited ability to combat COVID-19 and prevent severe outcomes or deaths as the new variant transmission becomes dominant.

Exploring posttraumatic growth after the COVID-19 pandemic

Although it is undeniable that the COVID-19 pandemic presented new threats and traumas for human beings, posttraumatic growth that took place after the struggle with this highly challenging crisis cannot be ignored. Therefore, based on the posttraumatic growth theory, the present research focuses on aspects of tourists' positive changes after the COVID-19 outbreak. A total of 1165 potential tourists from 197 cities in 31 provinces of China were analyzed using symmetrical and asymmetrical approaches. The results of the partial least squares test revealed the net effects of social support, psychological distress, and infection risk perception on the three dimensions of tourists' posttraumatic growth, namely, travel risk aversion, social identity, and altruistic behavior. Fuzzy-set qualitative comparative analysis provided causal recipes for realizing posttraumatic growth, and necessary condition analysis supplemented the necessary antecedents. The implications of the findings and the paths for future research are also presented.

Exploring posttraumatic growth after the COVID-19 pandemic

Although it is undeniable that the COVID-19 pandemic presented new threats and traumas for human beings, posttraumatic growth that took place after the struggle with this highly challenging crisis cannot be ignored. Therefore, based on the posttraumatic growth theory, the present research focuses on aspects of tourists' positive changes after the COVID-19 outbreak. A total of 1165 potential tourists from 197 cities in 31 provinces of China were analyzed using symmetrical and asymmetrical approaches. The results of the partial least squares test revealed the net effects of social support, psychological distress, and infection risk perception on the three dimensions of tourists' posttraumatic growth, namely, travel risk aversion, social identity, and altruistic behavior. Fuzzy-set qualitative comparative analysis provided causal recipes for realizing posttraumatic growth, and necessary condition analysis supplemented the necessary antecedents. The implications of the findings and the paths for future research are also presented.

Air pollution and meteorological variables' effects on COVID-19 first and second waves in Spain

The aim of this research is to study the influence of atmospheric pollutants and meteorological variables on the incidence rate of COVID-19 and the rate of hospital admissions due to COVID-19 during the first and second waves in nine Spanish provinces. Numerous studies analyze the effect of environmental and pollution variables separately, but few that include them in the same analysis together, and even fewer that compare their effects between the first and second waves of the virus. This study was conducted in nine of 52 Spanish provinces, using generalized linear models with Poisson link between levels of PM₁₀, NO₂ and O₃ (independent variables) and maximum temperature and absolute humidity and the rates of incidence and hospital admissions of COVID-19 (dependent variables), establishing a series of significant lags. Using the estimators obtained from the significant multivariate models, the relative risks associated with these variables were calculated for increases of 10 µg/m³ for pollutants, 1 °C for temperature and 1 g/m³ for humidity. The results suggest that NO₂ has a greater association than the other air pollution variables and the meteorological variables. There was a greater association with O₃ in the first wave and with NO₂ in the second. Pollutants showed a homogeneous distribution across the country. We conclude that, compared to other air pollutants and meteorological variables, NO₂ is a protagonist that may modulate the incidence and severity of COVID-19, though preventive public health measures such as masking and hand washing are still very important.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13762-022-04190-z.

Evaluating the effectiveness of Hong Kong's border restriction policy in reducing COVID-19 infections

This study evaluates the effectiveness of Hong Kong’s strict border restrictions with mainland China in curbing the transmission of COVID-19. Combining big data from Baidu Population Migration with traditional meteorological data and census data for over 200 Chinese cities, we utilize an advanced quantitative approach, namely synthetic control modeling, to produce a counterfactual “synthetic Hong Kong” without a strict border restriction policy. We then simulate infection trends under the hypothetical scenarios and compare them to actual infection numbers. Our counterfactual synthetic control model demonstrates a lower number of COVID-19 infections than the actual scenario, where strict border restrictions with mainland China were implemented from February 8 to March 6, 2020. Moreover, the second synthetic control model, which assumes a border reopen on 7 May 2020 demonstrates nonpositive effects of extending the border restriction policy on preventing and controlling infections. We conclude that the border restriction policy and its further extension may not be useful in containing the spread of COVID-19 when the virus is already circulating in the local community. Given the substantial economic and social costs, and as precautionary measures against COVID-19 becomes the new normal, countries can consider reopening borders with neighbors who have COVID-19 under control. Governments also need to closely monitor the changing epidemic situations in other countries in order to make prompt and sensible amendments to their border restriction policies.

The ISO 26000's labor environmental issues during COVID-19: does corporate social responsibility help? Evidence from the Egyptian small and medium enterprises

Based on both the "stakeholder" and "cognitive" theories, this study shed light on the optimistic side of the COVID-19 pandemic, as it also brings the concepts of corporate social responsibility (CSR) and sustainability back into the light, which helps in solving the labor environmental issues. Our research aims to profoundly investigate the correlation between CSR and labor environmental issues based on the International Organization for Standardization's standard 26,000 (ISO 26000) during COVID-19 and to also examine how CSR practices help solve labor environmental issues in the Egyptian small and medium enterprises (SMEs). Partial least squares structural equation modeling (PLS-SEM) was adopted, for data analysis and hypotheses testing, on a sample of 307 manager-level employees in the Egyptian SMEs. Results indicate that CSR positively impacts labor practices dimensions (employment relationships, human development and training, social dialog, and health and safety at work). However, CSR has an insignificant effect on social protection and work conditions. This study is scientifically valuable since it helps better understand the pandemic's effects on environmental labor issues in the Egyptian SMEs and shows how CSR helps solve those issues. Also, it discussed the theoretical contributions and practical implications and showed the limitations and future research for this study area.

Epidemiological trends and sociodemographic factors associated with acute hemorrhagic conjunctivitis in mainland China from 2004 to 2018

Background

Acute hemorrhagic conjunctivitis (AHC) is classified as a class C notifiable infectious disease in China and poses a great threat to public health. This study aimed to investigate the epidemiological trends and hotspots of AHC in mainland China. Sociodemographic factors that could contribute to early warning of AHC were further explored.

Methods

Yearly and monthly incidences of acute hemorrhagic conjunctivitis by date and region from 2004 to 2018 were extracted from the Data Center of China Public Health Science. Joinpoint regression and spatial autocorrelation analysis were performed to explore the epidemiological trends and hotspots of AHC. A generalized linear model was then applied to explore the relationship between sociodemographic factors and AHC incidence.

Results

The average annual AHC incidence was 3.58/100,000 in mainland China. The first-level spatial and temporal aggregation areas were distributed in Guangxi, Hainan, Guangdong, Guizhou, Hunan, Jiangxi, Fujian, Chongqing, Hubei, Anhui, and Zhejiang, with gathering times from 2010/1/1 to 2010/12/31 (RR = 20.13, LLR = 474,522.89, P < 0.01). After 2010, the AHC incidence was stable (APC = − 8.37, 95% CI: − 23.02–9.06). However, it was significantly increased in low- and middle-income provinces (AAPC = 10.65, 95% CI: 0.62–21.68, AAPC = 11.94, 95% CI: 0.62–24.53). The peak of AHC occurred during the August to October period. Children who age 0–3 years are identified as high-risk group with AHC incidence significantly increased (APC = 31.54, 95% CI: 0.27–72.56). Birth rate, population ages 0–14 (% of total population), passenger traffic, and urban population (% of total population) were positively associated with the AHC incidence, while per capita gross domestic product was negatively associated with the AHC incidence.

Conclusion

Overall, the AHC incidence was stable after 2010 in China, but it was significantly increased in low- and middle-income provinces. Regions with a high birth rate, population ages 0–14 (% of the total population), passenger traffic, urban population (% of the total population) and low per capita gross domestic product are at high risk of incidences of AHC. In the future, public health policy and resource priority for AHC in regions with these characteristics are necessary.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-022-01758-6.

Classification Schemes of COVID-19 High Risk Areas and Resulting Policies: A Rapid Review

The COVID-19 pandemic has posed a significant global health threat since January 2020. Policies to reduce human mobility have been recognized to effectively control the spread of COVID-19; although the relationship between mobility, policy implementation, and virus spread remains contentious, with no clear pattern for how countries classify each other, and determine the destinations to- and from which to restrict travel. In this rapid review, we identified country classification schemes for high-risk COVID-19 areas and associated policies which mirrored the dynamic situation in 2020, with the aim of identifying any patterns that could indicate the effectiveness of such policies. We searched academic databases, including PubMed, Scopus, medRxiv, Google Scholar, and EMBASE. We also consulted web pages of the relevant government institutions in all countries. This rapid review's searches were conducted between October 2020 and December 2021. Web scraping of policy documents yielded additional 43 country reports on high-risk area classification schemes. In 43 countries from which relevant reports were identified, six issued domestic classification schemes. International classification schemes were issued by the remaining 38 countries, and these mainly used case incidence per 100,000 inhabitants as key indicator. The case incidence cut-off also varied across the countries, ranging from 20 cases per 100,000 inhabitants in the past 7 days to more than 100 cases per 100,000 inhabitants in the past 28 days. The criteria used for defining high-risk areas varied across countries, including case count, positivity rate, composite risk scores, community transmission and satisfactory laboratory testing. Countries either used case incidence in the past 7, 14 or 28 days. The resulting policies included restrictions on internal movement and international travel. The quarantine policies can be summarized into three categories: (1) 14 days self-isolation, (2) 10 days self-isolation and (3) 14 days compulsory isolation.

Spatial analysis of COVID-19 hospitalised cases in an entire city: The risk of studying only lattice data

We live in a global pandemic caused by the COVID-19 disease where severe social distancing measures are necessary. Some of these measures have been taken into account by the administrative boundaries within cities (neighborhoods, postal districts, etc.). However, considering only administrative boundaries in decision making can prove imprecise, and could have consequences when it comes to taking effective measures. To solve the described problems, we present an epidemiological study that proposes using spatial point patterns to delimit spatial units of analysis based on the highest local incidence of hospitalisations instead of administrative limits during the first COVID-19 wave. For this purpose, the 579 addresses of the cases hospitalised between March 3 and April 6, 2020, in Albacete (Spain), and the addresses of the random sample of 383 controls from the Inhabitants Register of the city of Albacete, were georeferenced. The risk ratio in those hospitalised for COVID-19 was compatible with the constant risk ratio in Albacete (p = 0.49), but areas with a significantly higher risk were found and coincided with those with greater economic inequality (Gini Index). Moreover, two districts had areas with a significantly high incidence that were masked by others with a significantly low incidence. In conclusion, taking measures conditioned exclusively by administrative limits in a pandemic can cause problems caused by managing entire districts with lax measures despite having interior areas with high significant incidences. In a pandemic context, georeferencing disease cases in real time and spatially comparing them to updated random population controls to automatically and accurately detect areas with significant incidences are suggested. This would facilitate decision making, which must be fast and accurate in these situations.

Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

Impact of the COVID-19 pandemic on interest in renal diseases

There is an information gap about the public's interest in nephrological diseases in the COVID-19 era. The objective was to identify public interest in kidney diseases during the pandemic. In this infodemiology study, Google Trends was queried for a total of 50 search queries corresponding to a broad spectrum of nephrological diseases and the term "nephrologist." Two time intervals of 2020 (March 15-July 4 and July 5-October 31) were compared to similar time intervals of 2016-2019 for providing information on interest in different phases of the pandemic. Compared to the prior 4 years, analyses showed significant decreases in relative search volume (RSV) in the majority (76%) of search queries on March 15-July 4, 2020 period. However, RSV of the majority of search queries (≈70%) on July 5-October 31, 2020 period was not significantly different from similar periods of the previous 4 years, with an increase in search terms of amyloidosis, kidney biopsy, hematuria, chronic kidney disease, hypertension, nephrolithiasis, acute kidney injury, and Fabry disease. During the early pandemic, there have been significant decreases in search volumes for many nephrological diseases. However, this trend reversed in the period from July 5 to October 31, 2020, implying the increased need for information on kidney diseases. The results of this study enable us to understand how COVID-19 impacted the interest in kidney diseases and demands/needs for kidney diseases by the general public during the pandemic.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

COVID-19 seroprevalence among hospital staff and preprocedural patients in Thai community hospitals: a cross-sectional study

OBJECTIVES

We aimed to explore the seroprevalence of hospital staff comparing to preprocedural patients in Thai community hospitals to shed light on the situation of COVID-19 infection of frontline healthcare workers in low infection rate countries where mass screening was not readily available.

DESIGN

Cross-sectional study.

SETTING

52 community hospitals in 35 provinces covered all regions of Thailand.

PARTICIPANTS

857 participants consisted of 675 hospital staff and 182 preprocedural patients.

OUTCOME MEASURE

COVID-19 seroprevalence using a locally developed rapid IgM/IgG test kit RESULTS: Overall, 5.5% of the participants (47 of 857) had positive IgM, 0.2% (2 of 857) had positive IgG which both of them also had positive IgM. Hospitals located in the central part of Thailand had the highest IgM seroprevalence (11.9%). Preprocedural patients had a higher rate of positive IgM than the hospital staff (12.1% vs 3.7%). Participants with present upper respiratory tract symptoms had a higher rate of positive IgM than those without (9.6% vs 4.5%). Three quarters (80.5%, 690 of 857) of the participants were asymptomatic, of which, 31 had positive IgM (4.5%) which consisted of 20 of 566 healthcare workers (3.5%) and 11 of 124 preprocedural patients (8.9%).

CONCLUSIONS

COVID-19 antibody test could detect a substantial number of potential silent spreaders in Thai community hospitals where the nasopharyngeal PCR was not readily available, and the antigen test was prohibited. Antibody testing should be encouraged for mass screening in a limited resource setting, especially in asymptomatic individuals.

TRIAL REGISTRATION

TCTR20200426002.

Graphical Trajectory Comparison to Identify Errors in Data of COVID-19: A Cross-Country Analysis

Data from the early stage of a novel infectious disease outbreak provide vital information in risk assessment, prediction, and precise disease management. Since the first reported case of COVID-19, the pattern of the novel coronavirus transmission in Wuhan has become the interest of researchers in epidemiology and public health. To thoroughly map the mechanism of viral spreading, we used the patterns of data at the early onset of COVID-19 from seven countries to estimate the time lag between peak days of cases and deaths. This study compared these data with those of Wuhan and estimated the natural history of disease across the infected population and the time lag. The findings suggest that comparative analyses of data from different regions and countries reveal the differences between peaks of cases and deaths caused by COVID-19 and the incomplete and underestimated cases in Wuhan. Different countries may show different patterns of cases peak days, deaths peak days, and peak periods. Error in the early COVID-19 statistics in Brazil was identified. This study provides sound evidence for policymakers to understand the local circumstances in diagnosing the health of a population and propose precise and timely public health interventions to control and prevent infectious diseases.

Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China

COVID-19 has caused huge impacts on human health and the economic operation of the world. Analyzing and summarizing the early propagation law can help reduce the losses caused by public health emergencies in the future. Early data on the spread of COVID-19 in 30 provinces (autonomous regions and municipalities) of mainland China except for Hubei, Hong Kong, Macao, and Taiwan were selected in this study. Spatio-temporal analysis, inflection point analysis, and correlation analysis are used to explore the spatio-temporal characteristics in the early COVID-19 spread. The results suggested that (1) the total confirmed cases have risen in an "S"-shaped curve over time, and the daily new cases have first increased and finally decreased; (2) the spatial distributions of both total and daily new cases show a trend of more in the east and less in the west, with a "multi-center agglomeration distribution" around Hubei Province and some major cities; (3) the spatial agglomeration of total confirmed cases has been increasing over time, while that of the daily new cases shows much more obvious in the mid-stage; and (4) timely release of the first-level public health emergency response can accelerate the emergence of the epidemic inflection point. The above analysis results have a specific reference value for the government's policy-making and measures to face public health emergencies.

Co-distribution of Light At Night (LAN) and COVID-19 incidence in the United States

Background

Light at night (LAN) as a circadian disruption factor may affect the human immune system and consequently increase an individual’s susceptibility to the severity of infectious diseases, such as COVID-19. COVID-19 infections spread differently in each state in the United States (US). The current analysis aimed to test whether there is an association between LAN and COVID-19 cases in 4 selected US states: Connecticut, New York, California, and Texas.

Methods

We analyzed clustering patterns of COVID-19 cases in ArcMap and performed a multiple linear regression model using data of LAN and COVID-19 incidence with adjustment for confounding variables including population density, percent below poverty, and racial factors.

Results

Hotspots of LAN and COVID-19 cases are located in large cities or metro-centers for all 4 states. LAN intensity is associated with cases/1 k for overall and lockdown durations in New York and Connecticut (P < 0.001), but not in Texas and California. The overall case rates are significantly associated with LAN in New York (P < 0.001) and Connecticut (P < 0.001).

Conclusions

We observed a significant positive correlation between LAN intensity and COVID-19 cases-rate/1 k, suggesting that circadian disruption of ambient light may increase the COVID-19 infection rate possibly by affecting an individual’s immune functions. Furthermore, differences in the demographic structure and lockdown policies in different states play an important role in COVID-19 infections.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11500-6.

Changes in 25-(OH) Vitamin D Levels during the SARS-CoV-2 Outbreak: Lockdown-Related Effects and First-to-Second Wave Difference-An Observational Study from Northern Italy

Simple Summary

Several biological functions, more or less sustained by experimental evidence, have been proposed for vitamin D and in addition to its role in bone health, its optimal status has also been related with a reduced risk of allergy, obesity, and respiratory tract infections. During the SARS-CoV-2 pandemic, vitamin D levels have been put into relation with both the susceptibility to SARS-CoV-2 infection and the severity of COVID-19. The results from different studies are, however, not conclusive, since several variables impact on this relationship. In this study, we investigated the impact of the stringent confinement during the March–April 2020 lockdown on vitamin D levels and the relationship with the recorded sun exposure in the considered area (Milano, Italy). Furthermore, we investigated the eventual relationship between vitamin D levels and SARS-CoV-2 infection in different age groups throughout the pandemic, including the first and second wave, as well as the lockdown and between-lockdown periods. Taken together, our data suggest that 25-(OH)D levels are neither related with sun exposure nor with SARS-CoV-2 infection.

Abstract

Background. We investigated the changes in 25-hydroxy vitamin D [25-(OH)D] concentrations values, during the first and the second pandemic waves and the impact of the lockdown periods, with their different approaches to home confinement, among different age groups. Methods. Daily cloud-modified vitamin D UV dose (UVDVC), for the area of interest (Milano, Italy), was obtained. Single-center 25-(OH)D determinations from 40,996 individuals in a 2019 (pre-pandemic), 32,355 individuals from 1 January to 31 August 2020 (containing the first pandemic wave) and 27,684 individuals from 1 June to 30 November 2020 (containing the second wave) were compared to investigate both the effect of the lockdown on vitamin D status and the association between 25-(OH)D and SARS-CoV-2 positivity. Results. No direct association was found between UVDVC, 25-(OH)D status and SARS-CoV-2 infection. The stringent confinement implemented during the first wave lockdown has not had any effect on 25-(OH)D status, although some peculiar time-restricted profile behaviors can be deduced, possibly due to vitamin D supplementation habits and features of those patients who presented to the hospital during the lockdown period. Conclusions. Although our data do not suggest any direct relationship between 25-(OH)D status, sun exposure, and SARS-CoV-2 infection, an indirect protective role cannot be excluded.