Climate and the spread of COVID-19

Urban density, household overcrowding and the spread of COVID-19 in Australian cities

The UN-Habitat World Cities Report 2020 highlighted that overcrowded housing, not urban density, is the major contributing factor to the spread of COVID-19. The relatively successful ability of densely populated cities such as Seoul, Singapore, Tokyo and New York City to manage virus spread supports this. We hypothesise that, given the complexity of the interaction between people and place, the relative contribution of density and crowding to the spread of infectious diseases may be contingent on local factors. To directly compare the role of urban density and household overcrowding, we examine each in relation to COVID-19 incidence in the three largest cities in Australia, Sydney, Melbourne and Brisbane, as the pandemic unfolded from July 2021 to January 2022. Using ecological models adjusted for spatial autocorrelation and area-level measures of age and socio-economic factors, we assess the association between population density, overcrowding in homes, and COVID-19 infections in local neighbourhoods. Challenging prevailing assumptions, we find evidence for an effect of both density and overcrowding on COVID-19 infections depending on the city and area within cities; that is, depending on the local context. For example, in the southwestern suburbs of Sydney, the case rate decreases by between 0.4 and 6.4 with every one-unit increase in gross density however the case rate increases by between 0.01 and 9.6 with every one-unit increase in total overcrowding. These findings have important implications for developing pandemic response strategies: public health measures that target either density (e.g., lockdowns and restricted range of travel) or overcrowding (e.g., restricting number of people relative to dwelling, mask-wearing indoors, vaccination prioritisation) must be cognisant of the geographically local contexts in which they are implemented.

SARS-CoV-2 prevalence in wildlife 2020-2022: a worldwide systematic review and meta-analysis

The widespread transmission of SARS-CoV-2 in humans poses a serious threat to public health security, and a growing number of studies have discovered that SARS-CoV-2 infection in wildlife and mutate over time. This article mainly reports the first systematic review and meta-analysis of the prevalence of SARS-CoV-2 in wildlife. The pooled prevalence of the 29 included articles was calculated by us using a random effects model (22.9%) with a high heterogeneity (I2 = 98.7%, p = 0.00). Subgroup analysis and univariate regression analysis found potential risk factors contributing to heterogeneity were country, wildlife species, sample type, longitude, and precipitation. In addition, the prevalence of SARS-CoV-2 in wildlife increased gradually over time. Consequently, it is necessary to comprehensively analyze the risk factors of SARS-CoV-2 infection in wildlife and develop effective control policies, as well as to monitor the mutation of SARS-CoV-2 in wildlife at all times to reduce the risk of SARS-CoV-2 transmission among different species.

The complex link between socioeconomic deprivation and COVID-19. Evidence from small areas of Catalonia

This ecological study assesses the association between the incidence rate of COVID-19 confirmed cases and socioeconomic deprivation in the Catalan small areas for the first six waves of the pandemic. The association is estimated using Poisson regressions and, in contrast to previous studies, considering that the relationship is not linear but rather depends on the degree of deprivation. The results show that the association between deprivation and incidence varied between waves, not only in intensity but also in its sign. Although it was insignificant in the first, third and fourth waves, the association was positive and significant in the second, becoming significantly negative in the fifth and sixth waves. Interestingly, the evidence suggests that the link between both magnitudes was not homogeneous throughout the distribution of deprivation, the pattern also varying between waves. The results are discussed in view of the role of non-pharmacological interventions and vaccination, as well as potential biases (for example that associated with differences between population groups in the propensity to be tested in each wave).

Seasonality of endemic COVID-19

IMPORTANCE

The seasonality of COVID-19 is important for effective healthcare and public health decision-making. Previous waves of SARS-CoV-2 infections have indicated that the virus will likely persist as an endemic pathogen with distinct surges. However, the timing and patterns of potentially seasonal surges remain uncertain, rendering effective public health policies uninformed and in danger of poorly anticipating opportunities for intervention, such as well-timed booster vaccination drives. Applying an evolutionary approach to long-term data on closely related circulating coronaviruses, our research provides projections of seasonal surges that should be expected at major temperate population centers. These projections enable local public health efforts that are tailored to expected surges at specific locales or regions. This knowledge is crucial for enhancing medical preparedness and facilitating the implementation of targeted public health interventions.

Climate and COVID-19 transmission: a cross-sectional study in Africa

The role of climate in the Coronavirus disease 2019 (COVID-19) transmission appears to be controversial, as reported in earlier studies. In Africa, the subject is poorly documented. In this study, over the period from January 1st, 2020 to September 31, 2022, the daily variations in cumulative confirmed cases of COVID-19 for each African country (54 countries) are modelled through time-series-based approaches and using meteorological factors as covariates. It is suggested from the findings that climate plays a role in COVID-19 transmission since at least one meteorological factor is found to be significant in 32 countries. In decreasing order, the most often occurring meteorological factors are dewpoint temperature, relative and absolute humidity, average temperature and solar radiation. Most of these factors show a lagged effect with confirmed cases (between 0 and 28 days). Also, some meteorological factors exhibit contrasting effects on COVID-19 transmission, resulting in both positive and negative association with cumulative cases, therefore highlighting the complex nature of the interplay between climate and COVID-19 transmission.

Investigating the Possible Reasons for the Low Reported Morbidity and Mortality of COVID-19 in African Countries: An Integrative Review

BACKGROUND

COVID-19 has impacted the world differentially with the highest mortality and morbidity rate burden in Europe and the USA and the lowest mortality and morbidity burden in Africa. This study aims to investigate the possible reasons why Africa recorded the lowest COVID-19 mortality and morbidity.

METHODS

The following search terms were used PubMed database: ["mortalit*" (tw) OR "morbidit*" (tw) AND "COVID-19" (tw) AND "Africa" (tw)]. Studies that discuss a factor for the low COVID-19 burden in Africa have a defined methodology, discuss its research question and mention its limitations are selected for review. Data from the final articles were extracted using a data collection tool.

RESULTS

Twenty-one studies were used in this integrative review. Results were grouped into 10 themes, which are younger African population, lower health capacity, weather, vaccines and drugs, effective pandemic response, lower population density and mobility, African socioeconomic status, lower prevalence of comorbidities, genetic difference and previous infection exposure. The low COVID-19 mortality and morbidity in Africa is largely a result of a combined effect of the younger African population and underreporting of COVID-19 cases.

CONCLUSIONS

There is a need to strengthen the health capacities of African countries. Moreover, African countries that have other health problem priorities may use a tailored approach to vaccinating the elderly. More definitive studies are needed to know the role of BCG vaccination, weather, genetic makeup and prior infection exposure in the differential impact of the COVID-19 pandemic.

Effect of elevated temperature on SARS-CoV-2 viability

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a worldwide disruption of global health putting healthcare workers at high risk. To reduce the transmission of SARS-CoV-2, in particular during treating the patients, our team aims to develop an optimized isolation chamber. The present study was conducted to evaluate the role of temperature elevation against SARS-CoV-2 viability, where the information would be used to build the isolation chamber. 0.6 mL of the Indonesian isolate of SARS-CoV-2 strain 20201012747 (approximately 10 13 PFU/mL) was incubated for one hour with a variation of temperatures: 25, 30, 35, 40, 45, 50, 55, 60, and 65°C in digital block heater as well as at room temperature (21-23°C) before used to infect Vero E6 cells. The viability was determined using a plaque assay. Our data found a significant reduction of the viral viability from 10 13 PFU/mL to 10 9 PFU/mL after the room temperature was increase to 40°C. Further elevation revealed that 55°C and above resulted in the total elimination of the viral viability. Increasing the temperature 40°C to reduce the SARS-CoV-2 survival could create mild hyperthermia conditions in a patient which could act as a thermotherapy. In addition, according to our findings, thermal sterilization of the vacant isolation chamber could be conducted by increasing the temperature to 55°C. In conclusion, elevating the temperature of the isolation chamber could be one of the main variables for developing an optimized isolation chamber for COVID-19 patients.

Ecosystem degradation and the spread of Covid-19

The linkages between the emergence of zoonotic diseases and ecosystem degradation have been widely acknowledged by the scientific community and policy makers. In this paper we investigate the relationship between human overexploitation of natural resources, represented by the Human Appropriation of Net Primary Production Index (HANPP) and the spread of Covid-19 cases during the first pandemic wave in 730 regions of 63 countries worldwide. Using a Bayesian estimation technique, we highlight the significant role of HANPP as a driver of Covid-19 diffusion, besides confirming the well-known impact of population size and the effects of other socio-economic variables. We believe that these findings could be relevant for policy makers in their effort towards a more sustainable intensive agriculture and responsible urbanisation.

Analysis on the factors associated with COVID-19 infection among Chinese residents after the implementation of the 10 new rules to optimize COVID-19 response: a cross-sectional study

Introduction

This study aimed to investigate the status of COVID-19 infection and the associated factors among Chinese residents after the implementation of the 10 New Rules to optimize COVID response.

Methods

Participants were recruited using convenience sampling. The study used self-filled questionnaires to examine COVID-19 infection and associated factors among Chinese residents, from December 29, 2022, to January 2, 2023. For the statistical analysis, descriptive and quantitative analyses were used. The potential risk factors for COVID-19 infection were identified by multivariable logistic regression analysis.

Results

After the adjustments in control strategies against COVID-19, the infection rate of COVID-19 was high among respondents, and 98.4% of individuals who tested positive showed symptoms including cough, fever, fatigue, headache, sore throat, nasal congestion, sputum production, muscle and joint pain, and runny nose. The main problems respondents reported were the shortage of drugs and medical supplies, the increased burden on families, and the unreliable information source of COVID-19 infection. Logistic regression showed that isolating patients with COVID-19 at home was associated with a lower risk of COVID-19 infection (OR = 0.58, 95%CI: 0.42-0.81).

Conclusion

COVID-19 infection among residents is closely related to age, gender, and epidemic prevention measures. The government needs to strengthen education for individuals and centrally manage and properly address difficulties that may arise during COVID-19.

COVID-19 positive cases among asymptomatic individuals during the second wave in Ndola, Zambia

Background

Coronavirus disease 2019 (COVID-19) is a worldwide public health concern for healthcare workers. About 80% of cases appear to be asymptomatic, and about 3% may experience hospitalisation and later die. Less than 20% of studies have looked at the positivity rate of asymptomatic individuals.

Objective

This study investigated the COVID-19 positivity rates among asymptomatic individuals during the second COVID-19 wave at one of Zambia's largest testing centre.

Methods

This was a retrospective cross-sectional study conducted on routine surveillance and laboratory data at the Tropical Diseases Research Centre COVID-19 laboratory in Ndola, Zambia, from 01 December 2020 to 31 March 2021. The study population was made up of persons that had tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection as a requirement for travel. Microsoft Excel was used to come up with an epidemiological curve of daily COVID-19 positive cases; proportions for gender were described using frequencies and percentages.

Results

A total of 11 144 asymptomatic individuals tested for SARS-CoV-2 were sampled for the study and 1781 (16.0%) returned positive results. The median age among those tested was 36 years (interquartile range: 29-46). Testing for COVID-19 peaked in the month of January 2021 (37.4%) and declined in March 2021 (21.0%). The epidemiological curve showed a combination of continuous and propagated point-source transmission.

Conclusion

The positivity rate of 16.0% among asymptomatic individuals was high and could imply continued community transmission, especially during January 2021 and February 2021. We recommend heightened testing for SARS-CoV-2 among asymptomatic individuals.

What this study adds

This study adds critical knowledge to the transmission of COVID-19 among asymptomatic travellers who are usually a key population in driving community infection. This knowledge is critical in instituting evidence-based interventions in the screening and management of travellers, and its control.

Spatio-temporal multidisciplinary analysis of socio-environmental conditions to explore the COVID-19 early evolution in urban sites in South America

This study aimed to analyse how socio-environmental conditions affected the early evolution of COVID-19 in 14 urban sites in South America based on a spatio-temporal multidisciplinary approach. The daily incidence rate of new COVID-19 cases with symptoms as the dependent variable and meteorological-climatic data (mean, maximum, and minimum temperature, precipitation, and relative humidity) as the independent variables were analysed. The study period was from March to November of 2020. We inquired associations of these variables with COVID-19 data using Spearman's non-parametric correlation test, and a principal component analysis considering socio economic and demographic variables, new cases, and rates of COVID-19 new cases. Finally, an analysis using non-metric multidimensional scale ordering by the Bray-Curtis similarity matrix of meteorological data, socio economic and demographic variables, and COVID-19 was performed. Our findings revealed that the average, maximum, and minimum temperatures and relative humidity were significantly associated with rates of COVID-19 new cases in most of the sites, while precipitation was significantly associated only in four sites. Additionally, demographic variables such as the number of inhabitants, the percentage of the population aged 60 years and above, the masculinity index, and the GINI index showed a significant correlation with COVID-19 cases. Due to the rapid evolution of the COVID-19 pandemic, these findings provide strong evidence that biomedical, social, and physical sciences should join forces in truly multidisciplinary research that is critically needed in the current state of our region.

The effect of vaccine on COVID-19 spread by function-on-scalar regression model: a case study of Africa

Aim

This paper aimed to study the effect of the vaccine on the reproduction rate of coronavirus in Africa from January 2021 to November 2021.

Subject and methods

Functional data analysis (FDA), a relatively new area in statistics, can describe, analyze, and predict data collected over time, space, or other continuum measures in many countries every day and is increasingly common across scientific domains. For our data, the first step of functional data is smoothing. We used the B-spline method to smooth our data. Then, we apply the function-on-scalar and Bayes function-on-scalar models to fit our data.

Results

Our results indicate a statistically significant relationship between the vaccine and the rate of virus reproduction and spread. When the vaccination rate falls, the reproduction rate also decreases. Furthermore, we found that the effect of latitude and the region on the reproduction rate depends on the region. We discovered that in Middle Africa, from the beginning of the year until the end of the summer, the impact is negative, implying that the virus spread due to a decrease in the vaccination rates.

Conclusion

The study found that vaccination rates significantly impact the virus's reproduction rate.

Human disturbance increases coronavirus prevalence in bats

Human land modification is a known driver of animal-to-human transmission of infectious agents (zoonotic spillover). Infection prevalence in the reservoir is a key predictor of spillover, but landscape-level associations between the intensity of land modification and infection rates in wildlife remain largely untested. Bat-borne coronaviruses have caused three major disease outbreaks in humans: severe acute respiratory syndrome (SARS), Middle East respiratory syndrome, and coronavirus disease 2019 (COVID-19). We statistically link high-resolution land modification data with bat coronavirus surveillance records and show that coronavirus prevalence significantly increases with the intensity of human impact across all climates and levels of background biodiversity. The most significant contributors to the overall human impact are agriculture, deforestation, and mining. Regions of high predicted bat coronavirus prevalence coincide with global disease hotspots, suggesting that infection prevalence in wildlife may be an important factor underlying links between human land modification and zoonotic disease emergence.

Similarities in COVID-19 Mortality Between Canadian Provinces and American States Before Vaccines Were Available

Canada and the USA are often compared for their markedly different approaches to health care despite cultural similarities and sharing the world's longest international boundary. The period between the onset of the COVID-19 pandemic in January 2020 and the availability of a vaccine in December 2020 offers an ideal opportunity to compare subnational Canadian and American pandemic mortality. Preventing the spread of COVID-19 was through compliance with health orders and best practices; treatment was only available to those admitted to hospitals and whose lives were at risk. Using publicly available data from the Johns Hopkins University 2019 Novel Coronavirus Visual Dashboard, we seek to uncover if there were any similarities in Canadian provinces' and American states' monthly COVID-19 mortality per 100,000 people, building on a broader scientific push towards understanding the successes and failures of different health systems in the pandemic. The similar province and state cumulative COVID-19 mortality rate trajectories identified in our analyses do not amount to intuitive comparative jurisdictions which suggests the importance of identifying localized pandemic responses.

The shift of percent excess mortality from zero-COVID policy to living-with-COVID policy in Singapore, South Korea, Australia, New Zealand and Hong Kong SAR

Introduction

With the economic recession and pandemic fatigue, milder viral variants and higher vaccine coverage along the time lay the basis for lifting anti-COVID policies to restore COVID-19 normalcy. However, when and how to adjust the anti-COVID policies remain under debate in many countries.

Methods

In this study, four countries (Singapore, South Korea, Australia, and New Zealand) and one region (Hong Kong SAR), that have shifted from the zero-COVID (ZC) policy to or close to the living-with-COVID (LWC) during or after the Omicron outbreak, were selected as research objects. All-cause mortality data were collected for these objects from 2009 to 2019. The expected mortality was estimated by a simple linear regression method. Excess mortality over time was calculated as the difference between the expected mortality and the observed mortality. Finally, percent excess mortality (PEM) was calculated as the excess mortality divided by the expected mortality.

Results

In the examined four countries, PEM fluctuated around 0% and was lower than 10% most of the time under the ZC policy before 2022. After shifting to the LWC policy, all the examined countries increased the PEM. Briefly, countries with high population density (Singapore and South Korea) experienced an average PEM of 20-40% during the first half of 2022, and followed by a lower average PEM of 15-18% during the second half of 2022. For countries with low population density under the LWC policy, Australia experienced an average PEM of 39.85% during the first half of 2022, while New Zealand was the only country in our analysis that achieved no more than 10% in average PEM all the time. On the contrary, Hong Kong SAR under their ZC policy attained an average PEM of 71.14% during the first half of 2022, while its average PEM decreased to 9.19% in the second half of 2022 with LWC-like policy.

Conclusion

PEM under different policies within each country/region overtime demonstrated that the mortality burden caused by COVID-19 had been reduced overtime. Moreover, anti-COVID policies are suggested to control the excess mortality to achieve as low as 10% in PEM.

The impact of mean body mass index on reported mortality from COVID-19 across 181 countries

Accountability for global health issues such as a pandemic and its devastating consequences are usually ascribed to a virus, but a comprehensive view should also take into account the state of the host. Data suggests that excessive nutrition is to blame for a yet unknown but not negligible portion of deaths attributed to severe acute respiratory syndrome coronavirus 2. We analyzed the correlation between mean body mass index (BMI) and 2-year coronavirus disease 2019 (COVID-19) mortality rates reported by 181 countries worldwide. Almost two thirds of the countries included had a mean BMI greater or equal to 25, with death rates ranging from 3 to 6,280 per million. Death rates in countries with a mean BMI below 25 ranged from 3 to 1,533. When the analysis was restricted to countries where the extent of testing was deemed more representative of actual mortality, only 20.1% had a mean BMI <25 but the mortality difference persisted. A second analysis looking at pre-vaccination mortality obtained from a different source led to similar conclusions. Due to the nature of the variables, reverse causation can be excluded while common causation can not. A mean BMI <25 for a country seems to spare its citizens from the highest COVID-19 mortality rates. The impact of excess weight on global COVID-19 mortality is suspected to have been much higher than what currently perceived, here estimated at no less than a fourfold increase in mortality. Countries with normal mean BMI constitute precious test beds for the quantification of the effects of overeating on COVID-19 mortality.

Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of tropical Andean South America: a spatiotemporally disaggregated time series analysis

Background

The COVID-19 pandemic has caused societal disruption globally, and South America has been hit harder than other lower-income regions. This study modeled the effects of six weather variables on district-level SARS-CoV-2 reproduction numbers (Rt ) in three contiguous countries of tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors.

Methods

Daily time-series data on SARS-CoV-2 infections were sourced from the health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a unified COVID-19 dataset and other publicly available sources for May-December, 2020. Generalized additive models were fitted.

Findings

Relative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt . Days with radiation above 1000 kJ/m2 saw a 1.3% reduction in Rt , and those with humidity above 50% recorded a 0.9% reduction in Rt . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with lowest population mobility. Wind speed, temperature, region, aggregate government policy response, and population age structure had little impact. The fully adjusted model explained 4.3% of Rt variance.

Interpretation

Dry atmospheric conditions of low humidity increase district-level SARS-CoV-2 reproduction numbers, while higher levels of solar radiation decrease district-level SARS-CoV-2 reproduction numbers - effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures.

Funding

NASA's Group on Earth Observations Work Programme (16-GEO16-0047).

The role of the HLA allelic repertoire on the clinical severity of COVID-19 in Canadians, living in the Saskatchewan province

AIMS

The HLA system has been implicated as an underlying determinant for modulating the immune response to SARS-CoV-2. In this study, we aimed to determine the association of patients' HLA genetic profiles with the disease severity of COVID-19 infection.

METHODS

Prospective study was conducted on COVID-19 patients (n = 40) admitted to hospitals in Saskatoon, Canada, between March and December 2020. Next-generation sequencing was performed on the patient samples to obtain high-resolution HLA typing profiles. The statistical association between HLA allelic frequency and disease severity was examined. The disease severity was categorized based on the length of hospital stay and intensive care needs or demise during the hospital stay.

RESULTS

HLA allelic frequencies of the high and low-severity cohorts were normalized against corresponding background allelic frequencies. In the high-severity cohort, A*02:06 (11.8-fold), B*51:01 (2.4-fold), B*15:01(3.1-fold), C*01:02 (3.3-fold), DRB1*08:02 (31.2-fold), DQ*06:09 (11-fold), and DPB1*04:02(4-fold) were significantly overrepresented (p < 0.05) making these deleterious alleles. In the low-severity cohort, A*24:02 (2.8-fold), B*35:01 (2.8-fold), DRB1*04:07 (5.3-fold), and DRB1*08:11 (22-fold) were found to be significantly overrepresented (p < 0.05) making these protective alleles. These above alleles interact with NK cell antiviral activity via the killer immunoglobulin-like receptors (KIR). The high-severity cohort had a higher predilection for HLA alleles associated with KIR subgroups; Bw4-80I (1.1-fold), and C1 (1.6-fold) which promotes NK cell inhibition, while the low-severity cohort had a higher predilection for Bw4-80T (1.6-fold), and C2 (1.6-fold) which promote NK cell activation.

CONCLUSION

In this study, the HLA allelic repository with the distribution of deleterious and protective alleles was found to correlate with the severity of the clinical course in COVID-19. Moreover, the interaction of specific HLA alleles with the KIR-associated subfamily modulates the NK cell-mediated surveillance of SARS-CoV-2. Both deleterious HLA alleles and inhibitory KIR appear prominently in the severe COVID-19 group focusing on the importance of NK cells in the convalescence of COVID-19.

Warmer ambient air temperatures reduce nasal turbinate and brain infection, but increase lung inflammation in the K18-hACE2 mouse model of COVID-19

Warmer climatic conditions have been associated with fewer COVID-19 cases. Herein we infected K18-hACE2 mice housed at the standard animal house temperature of ∼22 °C, or at ∼31 °C, which is considered to be thermoneutral for mice. On day 2 post infection, RNA-Seq analyses showed no significant differential gene expression lung in lungs of mice housed at the two temperatures, with almost identical viral loads and type I interferon responses. There was also no significant difference in viral loads in lungs on day 5, but RNA-Seq and histology analyses showed clearly elevated inflammatory signatures and infiltrates. Thermoneutrality thus promoted lung inflammation. On day 2 post infection mice housed at 31 °C showed reduced viral loads in nasal turbinates, consistent with increased mucociliary clearance at the warmer ambient temperature. These mice also had reduced virus levels in the brain, and an ensuing amelioration of weight loss and a delay in mortality. Warmer air temperatures may thus reduce infection of the upper respiratory track and the olfactory epithelium, resulting in reduced brain infection. Potential relevance for anosmia and neurological sequelae in COVID-19 patients is discussed.

Climatic and Environmental Factors Influencing COVID-19 Transmission-An African Perspective

Since the outbreak of COVID-19 was decreed by the World Health Organization as a public health emergency of worldwide concern, the epidemic has drawn attention from all around the world. The disease has since spread globally in developed and developing countries. The African continent has not been spared from the pandemic; however, the low number of cases in Africa compared to developed countries has brought about more questions than answers. Africa is known to have a poor healthcare system that cannot sustain the emerging infectious disease pandemic. This study explored climatic and environmental elements influencing COVID-19 transmission in Africa. This study involved manuscripts and data that evaluated and investigated the climatic and environmental elements of COVID-19 in African countries. Only articles written in English were considered in the systematic review. Seventeen articles and one database were selected for manuscript write-ups after the review process. The findings indicated that there is evidence that suggests the influence of climatic and environmental elements on the spread of COVID-19 in the continent of Africa; however, the evidence needs more investigation in all six regions of Africa and at the country level to understand the role of weather patterns and environmental aspects in the transmission of COVID-19.

Understanding the exceptional pre-vaccination Era East Asian COVID-19 outcomes

During the first year of the pandemic, East Asian countries have reported fewer infections, hospitalizations, and deaths from COVID-19 disease than most countries in Europe and the Americas. Our goal in this paper is to generate and evaluate hypothesis that may explain this striking fact. We consider five possible explanations: (1) population age structure (younger people tend to have less severe COVID-19 disease upon infection than older people); (2) the early adoption of lockdown strategies to control disease spread; (3) genetic differences between East Asian population and European and American populations that confer protection against COVID-19 disease; (4) seasonal and climactic contributors to COVID-19 spread; and (5) immunological differences between East Asian countries and the rest of the world. The evidence suggests that the first four hypotheses are unlikely to be important in explaining East Asian COVID-19 exceptionalism. Lockdowns, in particular, fail as an explanation because East Asian countries experienced similarly good infection outcomes despite vast differences in lockdown policies adopted by different countries to control the COVID-19 epidemic. The evidence to date is consistent with our fifth hypothesis - pre-existing immunity unique to East Asia - but there are still essential parts of this story left for scientists to check.

Post-COVID-19 complications in home and hospital-based care: A study from Dhaka city, Bangladesh

A cross-sectional survey was undertaken to understand the management patterns and post-COVID-19 complications among hospital and home-treated participants. Retrospective information was collected from four COVID-19 dedicated hospitals and four selected community settings. Using probability proportional sampling, 925 participants were selected. Data were collected using a semi-structured questionnaire. Bivariate and multivariate logistic regression analysis and the exact chi-square tests were utilized to analyze the association between the studied variables. A total of 659 participants responded (response rate 70.93%); 375 from hospitals and 284 from communities. About 80% of participants were mild cases, 75% were treated at home, and 65% of hospital-treated participants were referred after home treatment. Participants treated at home-to hospital and directly in the hospital had 1.64 and 3.38 times longer recovery time respectively than what home-based participants had. A significant increasing trend (p < 0.001) of co-morbidities was found among referred and hospital treated participants. Age, level of education, physical exercise, practicing preventive measures, exposure to sunlight, and intake of carbohydrate, additional liquid, food supplements, and avoidance of junk foods were significantly associated with place of treatment. Post-COVID-19 difficulties of all factors were statistically significant for home treatment participants, whilst only depression (p = 0.026), chest pain (p = 0.017), and digestive disorders (p = 0.047) were significant (p < 0.05) for hospital treated participants. The outcomes from this study provide insight into a range of post-COVID-19 difficulties relating to at home and in hospital treatment participants. There are clear differences in the complications experienced, many of which are statistically significant. The health care professionals, the community people and COVID-19 survivors will be benefitted from the study findings, and the policy level people may use the information for designing health education program on post COVID-19 complications.

Systemic corticosteroids for the treatment of COVID-19: Equity-related analyses and update on evidence

BACKGROUND

Systemic corticosteroids are used to treat people with COVID-19 because they counter hyper-inflammation. Existing evidence syntheses suggest a slight benefit on mortality. Nonetheless, size of effect, optimal therapy regimen, and selection of patients who are likely to benefit most are factors that remain to be evaluated.

OBJECTIVES

To assess whether and at which doses systemic corticosteroids are effective and safe in the treatment of people with COVID-19, to explore equity-related aspects in subgroup analyses, and to keep up to date with the evolving evidence base using a living systematic review approach.

SEARCH METHODS

We searched the Cochrane COVID-19 Study Register (which includes PubMed, Embase, CENTRAL, ClinicalTrials.gov, WHO ICTRP, and medRxiv), Web of Science (Science Citation Index, Emerging Citation Index), and the WHO COVID-19 Global literature on coronavirus disease to identify completed and ongoing studies to 6 January 2022.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) that evaluated systemic corticosteroids for people with COVID-19. We included any type or dose of systemic corticosteroids and the following comparisons: systemic corticosteroids plus standard care versus standard care, different types, doses and timings (early versus late) of corticosteroids. We excluded corticosteroids in combination with other active substances versus standard care, topical or inhaled corticosteroids, and corticosteroids for long-COVID treatment.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methodology. To assess the risk of bias in included studies, we used the Cochrane 'Risk of bias' 2 tool for RCTs. We rated the certainty of the evidence using the GRADE approach for the following outcomes: all-cause mortality up to 30 and 120 days, discharged alive (clinical improvement), new need for invasive mechanical ventilation or death (clinical worsening), serious adverse events, adverse events, hospital-acquired infections, and invasive fungal infections.

MAIN RESULTS

We included 16 RCTs in 9549 participants, of whom 8271 (87%) originated from high-income countries. A total of 4532 participants were randomised to corticosteroid arms and the majority received dexamethasone (n = 3766). These studies included participants mostly older than 50 years and male. We also identified 42 ongoing and 23 completed studies lacking published results or relevant information on the study design. Hospitalised individuals with a confirmed or suspected diagnosis of symptomatic COVID-19 Systemic corticosteroids plus standard care versus standard care plus/minus placebo We included 11 RCTs (8019 participants), one of which did not report any of our pre-specified outcomes and thus our analyses included outcome data from 10 studies. Systemic corticosteroids plus standard care compared to standard care probably reduce all-cause mortality (up to 30 days) slightly (risk ratio (RR) 0.90, 95% confidence interval (CI) 0.84 to 0.97; 7898 participants; estimated absolute effect: 274 deaths per 1000 people not receiving systemic corticosteroids compared to 246 deaths per 1000 people receiving the intervention (95% CI 230 to 265 per 1000 people); moderate-certainty evidence). The evidence is very uncertain about the effect on all-cause mortality (up to 120 days) (RR 0.74, 95% CI 0.23 to 2.34; 485 participants). The chance of clinical improvement (discharged alive at day 28) may slightly increase (RR 1.07, 95% CI 1.03 to 1.11; 6786 participants; low-certainty evidence) while the risk of clinical worsening (new need for invasive mechanical ventilation or death) may slightly decrease (RR 0.92, 95% CI 0.84 to 1.01; 5586 participants; low-certainty evidence). For serious adverse events (two RCTs, 678 participants), adverse events (three RCTs, 447 participants), hospital-acquired infections (four RCTs, 598 participants), and invasive fungal infections (one study, 64 participants), we did not perform any analyses beyond the presentation of descriptive statistics due to very low-certainty evidence (high risk of bias, heterogeneous definitions, and underreporting). Different types, dosages or timing of systemic corticosteroids We identified one RCT (86 participants) comparing methylprednisolone to dexamethasone, thus the evidence is very uncertain about the effect of methylprednisolone on all-cause mortality (up to 30 days) (RR 0.51, 95% CI 0.24 to 1.07; 86 participants). None of the other outcomes of interest were reported in this study. We included four RCTs (1383 participants) comparing high-dose dexamethasone (12 mg or higher) to low-dose dexamethasone (6 mg to 8 mg). High-dose dexamethasone compared to low-dose dexamethasone may reduce all-cause mortality (up to 30 days) (RR 0.87, 95% CI 0.73 to 1.04; 1269 participants; low-certainty evidence), but the evidence is very uncertain about the effect of high-dose dexamethasone on all-cause mortality (up to 120 days) (RR 0.93, 95% CI 0.79 to 1.08; 1383 participants) and it may have little or no impact on clinical improvement (discharged alive at 28 days) (RR 0.98, 95% CI 0.89 to 1.09; 200 participants; low-certainty evidence). Studies did not report data on clinical worsening (new need for invasive mechanical ventilation or death). For serious adverse events, adverse events, hospital-acquired infections, and invasive fungal infections, we did not perform analyses beyond the presentation of descriptive statistics due to very low-certainty evidence. We could not identify studies for comparisons of different timing and systemic corticosteroids versus other active substances. Equity-related subgroup analyses We conducted the following subgroup analyses to explore equity-related factors: sex, age (< 70 years; ≥ 70 years), ethnicity (Black, Asian or other versus White versus unknown) and place of residence (high-income versus low- and middle-income countries). Except for age and ethnicity, no evidence for differences could be identified. For all-cause mortality up to 30 days, participants younger than 70 years seemed to benefit from systemic corticosteroids in comparison to those aged 70 years and older. The few participants from a Black, Asian, or other minority ethnic group showed a larger estimated effect than the many White participants. Outpatients with asymptomatic or mild disease There are no studies published in populations with asymptomatic infection or mild disease.

AUTHORS' CONCLUSIONS

Systemic corticosteroids probably slightly reduce all-cause mortality up to 30 days in people hospitalised because of symptomatic COVID-19, while the evidence is very uncertain about the effect on all-cause mortality up to 120 days. For younger people (under 70 years of age) there was a potential advantage, as well as for Black, Asian, or people of a minority ethnic group; further subgroup analyses showed no relevant effects. Evidence related to the most effective type, dose, or timing of systemic corticosteroids remains immature. Currently, there is no evidence on asymptomatic or mild disease (non-hospitalised participants). Due to the low to very low certainty of the current evidence, we cannot assess safety adequately to rule out harmful effects of the treatment, therefore there is an urgent need for good-quality safety data. Findings of equity-related subgroup analyses should be interpreted with caution because of their explorative nature, low precision, and missing data. We identified 42 ongoing and 23 completed studies lacking published results or relevant information on the study design, suggesting there may be possible changes of the effect estimates and certainty of the evidence in the future.

SARS-CoV-2 phase I transmission and mutability linked to the interplay of climatic variables: a global observation on the pandemic spread

The study aims to determine the impact of global meteorological parameters on SARS-COV-2, including population density and initiation of lockdown in twelve different countries. The daily trend of these parameters and COVID-19 variables from February 15th to April 25th, 2020, were considered. Asian countries show an increasing trend between infection rate and population density. A direct relationship between the time-lapse of the first infected case and the period of suspension of movement controls the transmissivity of COVID-19 in Asian countries. The increase in temperature has led to an increase in COVID-19 spread, while the decrease in humidity is consistent with the trend in daily deaths during the peak of the pandemic in European countries. Countries with 65°F temperature and 5 mm rainfall have a negative impact on COVID-19 spread. Lower oxygen availability in the atmosphere, fine droplets of submicron size together with infectious aerosols, and low wind speed have contributed to the increase in total cases and mortality in Germany and France. The onset of the D614G mutation and subsequent changes to D614 before March, later G614 in mid-March, and S943P, A831V, D839/Y/N/E in April were observed in Asian and European countries. The results of the correlation and factor analysis show that the COVID-19 cases and the climatic factors are significantly correlated with each other. The optimum meteorological conditions for the prevalence of G614 were identified. It was observed that the complex interaction of global meteorological factors and changes in the mutational form of CoV-2 phase I influenced the daily mortality rate along with other comorbid factors. The results of this study could help the public and policymakers to create awareness of the COVID-19 pandemic.

Cumulative effects of air pollution and climate drivers on COVID-19 multiwaves in Bucharest, Romania

Over more than two years of global health crisis due to ongoing COVID-19 pandemic, Romania experienced a five-wave pattern. This study aims to assess the potential impact of environmental drivers on COVID-19 transmission in Bucharest, capital of Romania during the analyzed epidemic period. Through descriptive statistics and cross-correlation tests applied to time series of daily observational and geospatial data of major outdoor inhalable particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) or ≤ 10 µm (PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), Aerosol Optical Depth at 550 nm (AOD) and radon (222Rn), we investigated the COVID-19 waves patterns under different meteorological conditions. This study examined the contribution of individual climate variables on the ground level air pollutants concentrations and COVID-19 disease severity. As compared to the long-term average AOD over Bucharest from 2015 to 2019, for the same year periods, this study revealed major AOD level reduction by ~28 % during the spring lockdown of the first COVID-19 wave (15 March 2020-15 May 2020), and ~16 % during the third COVID-19 wave (1 February 2021-1 June 2021). This study found positive correlations between exposure to air pollutants PM2.5, PM10, NO2, SO2, CO and 222Rn, and significant negative correlations, especially for spring-summer periods between ground O3 levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance with COVID-19 incidence and deaths. For the analyzed time period 1 January 2020-1 April 2022, before and during each COVID-19 wave were recorded stagnant synoptic anticyclonic conditions favorable for SARS-CoV-2 virus spreading, with positive Omega surface charts composite average (Pa/s) at 850 mb during fall- winter seasons, clearly evidenced for the second, the fourth and the fifth waves. These findings are relevant for viral infections controls and health safety strategies design in highly polluted urban environments.

Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study

During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus' effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO₂, sulfur dioxide-SO₂, carbon monoxide-CO) and radon - ²²²Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O₃ levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.

Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-022-02646-3.

Assessing the impact of long-term exposure to nine outdoor air pollutants on COVID-19 spatial spread and related mortality in 107 Italian provinces

This paper investigates the air quality in 107 Italian provinces in the period 2014-2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants-nitrogen dioxide (NO₂), ozone (O₃), and particulate matter (PM_2.5 and PM₁₀)-were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO₂, PM_2.5, PM₁₀, benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO_2, O₃, PM_2.5, PM₁₀, and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COVID-19 prevalence; while particulate matter and As showed the largest dangerous impact on excess mortality rate. The results were confirmed even after controlling for eighteen covariates and spatial effects. This outcome seems of interest because benzene, BaP, and heavy metals (As and Cd) have not been considered at all in recent literature. It also suggests the need for a national strategy to drive down air pollutant concentrations to cope better with potential future pandemics.

Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions

Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission and infer a single Bayesian estimate of the overall seasonal effect. By extending two state-of-the-art models of non-pharmaceutical intervention (NPI) effects and their datasets covering 143 regions in temperate Europe, we are able to adjust our estimates for the role of both NPIs and mobility patterns in reducing transmission. We find strong seasonal patterns, consistent with a reduction in the time-varying reproduction number R(t) (the expected number of new infections generated by an infectious individual at time t) of 42.1% (95% CI: 24.7%-53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions.

Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions

Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission and infer a single Bayesian estimate of the overall seasonal effect. By extending two state-of-the-art models of non-pharmaceutical intervention (NPI) effects and their datasets covering 143 regions in temperate Europe, we are able to adjust our estimates for the role of both NPIs and mobility patterns in reducing transmission. We find strong seasonal patterns, consistent with a reduction in the time-varying reproduction number R(t) (the expected number of new infections generated by an infectious individual at time t) of 42.1% (95% CI: 24.7%-53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions.

COVID-19 Risk Management and Stakeholder Action Strategies: Conceptual Frameworks for Community Resilience in the Context of Indonesia

The coronavirus disease (COVID-19) pandemic has affected people’s lives globally. Indonesia has been significantly affected by this disease. COVID-19 has also affected certain social and economic aspects of Indonesia, including community resilience. Through a variety of contexts and geographic locales, we explore the previously mentioned concept of resilience. From existing literature reviews, we develop a holistic framework for community resilience during the COVID-19 pandemic. Then, we formulate crucial factors for community resilience during the COVID-19 pandemic: natural capital, social capital, human capital, stakeholder engagement, community participation, technology, and communication. Strategic stakeholder action in the community resilience domain has facilitated increases in economic as well financial capital for adapting to and surviving deficits in productivity in the face of the COVID-19 pandemic. This study is a reflection on and a comparative review of the existing literature from different countries.

[Health impact of COVID pandemic. SESPAS Report 2022]

Objetivo

Presentar, a los 22 meses de que la Organización Mundial de la Salud declarara como pandemia la infección global por SARS-CoV-2 el 11 de marzo de 2020, el impacto hasta el momento de la pandemia en términos de salud física y mental.

Método

Se han revisado resultados procedentes de metaanálisis y revisiones sistemáticas, citándose para algún aspecto específico de interés artículos individuales. La información nacional procede de la Red Nacional de Vigilancia Epidemiológica (RENAVE).

Resultados

Hasta finales de octubre se habían producido más de 250 millones de infecciones y 5 millones de muertes. En España se han notificado 4,7 millones de infecciones, aunque la cifra real es superior a los 7 millones. Se ha reducido la esperanza de vida, con efectos especialmente dramáticos en grupos de población con comorbilidad y de edad avanzada. Se constata un empeoramiento de la salud mental general. Es previsible que algunos colectivos, como los/las profesionales sanitarios/as, en su mayoría mujeres, y trabajadores/as de primera línea, tengan un mayor riesgo de desarrollar patologías de salud mental en el futuro. La pandemia y las medidas de control han tenido otras consecuencias indeseables, como la disminución de la actividad asistencial, el aumento del sedentarismo y el aumento de la violencia de género.

Conclusiones

Son necesarios estudios longitudinales para determinar la magnitud de las consecuencias de la propia infección y de las medidas de control y las actuaciones necesarias.

From 15 Minutes to 15 Seconds: How the Delta Variant Changed the Risk of Exposure to COVID-19. A Comparative Epidemiological Investigation Using Community Mobility Data From the Metropolitan Area of Genoa, Italy

The Delta variant became dominant during the second wave of the Covid-19 pandemic due to its competitive advantage, the ability to reduce close contact duration from minutes to seconds, and, consequently, increase the risk of exposure to COVID-19. We used game theory to model the most effective public health response to this new threat. We compared the absolute and relative risk of exposure to COVID-19 before and after the emergence of the Delta variant. The absolute risk of exposure was defined as the product of crowding (people within a six feet distance) and visit duration. Our epidemiological investigation used aggregated and anonymized mobility data from Google Maps to estimate the visit duration for 808 premises in the metropolitan area of Genoa, Italy, in June 2021. The relative risk of exposure was obtained by dividing the risk of exposure of each activity by the lowest value (gas stations = 1). The median absolute risk of exposure to COVID-19 increased by sixty-fold in the first semester of 2021, while the relative risk did not significantly differ from the risk of exposure to the ancestral form of Covid-19 (5.9 in 2021 vs. 2.5 in 2021). The Delta variant represents an evolution of the game against COVID-19, but it is not a game-changer. The best response is to commit to our original strategy based on population-wide vaccination and social distancing. Unilateral deviations from the dominant strategy could offer COVID-19 a fighting chance against humanity.

COVID-19-Related Mortality amongst Physicians in Italy: Trend Pre- and Post-SARS-CoV-2 Vaccination Campaign

Globally, there has been a high burden of COVID-19-related mortality amongst physicians and other healthcare workers during the ongoing SARS-CoV-2 pandemic. Fortunately, anti-COVID-19 vaccination campaigns have helped to protect frontline workers and reduce COVID-19-related mortality amongst this occupational group. We analyzed COVID-19-related mortality data for doctors in Italy and compared the crude mortality rate between March-May 2020 (i.e., the beginning of the pandemic in Italy, with the highest rates of COVID-19-related deaths) and the same time period in March-May 2021 (high vaccination coverage amongst Italian physicians). The mortality rate was 12 times higher in March-May 2020 compared to the same time period after the start of the Italian vaccination campaign. Moreover, there was a strong inverse correlation between the number of deaths and the cumulative number of vaccine doses administered in the Italian population. Although non-pharmaceutical interventions, virus evolution and environmental factors probably had an effect, our analysis clearly supports the hypothesis that the vaccination campaign helped to protect Italian physicians and reduce COVID-19-related mortality. The latest available death trends from September to October 2021 for both physicians and the general population are also in favor of the need for the third vaccine dose, currently underway for the majority of the population at risk.

A Tale of Three Recent Pandemics: Influenza, HIV and SARS-CoV-2

Emerging infectious diseases are one of the main threats to public health, with the potential to cause a pandemic when the infectious agent manages to spread globally. The first major pandemic to appear in the 20th century was the influenza pandemic of 1918, caused by the influenza A H1N1 strain that is characterized by a high fatality rate. Another major pandemic was caused by the human immunodeficiency virus (HIV), that started early in the 20th century and remained undetected until 1981. The ongoing HIV pandemic demonstrated a high mortality and morbidity rate, with discrepant impacts in different regions around the globe. The most recent major pandemic event, is the ongoing pandemic of COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has caused over 5.7 million deaths since its emergence, 2 years ago. The aim of this work is to highlight the main determinants of the emergence, epidemic response and available countermeasures of these three pandemics, as we argue that such knowledge is paramount to prepare for the next pandemic. We analyse these pandemics’ historical and epidemiological contexts and the determinants of their emergence. Furthermore, we compare pharmaceutical and non-pharmaceutical interventions that have been used to slow down these three pandemics and zoom in on the technological advances that were made in the progress. Finally, we discuss the evolution of epidemiological modelling, that has become an essential tool to support public health policy making and discuss it in the context of these three pandemics. While these pandemics are caused by distinct viruses, that ignited in different time periods and in different regions of the globe, our work shows that many of the determinants of their emergence and countermeasures used to halt transmission were common. Therefore, it is important to further improve and optimize such approaches and adapt it to future threatening emerging infectious diseases.

An analysis of the dynamic spatial spread of COVID-19 across South Korea

The first case of coronavirus disease 2019 (COVID-19) in South Korea was confirmed on January 20, 2020, approximately three weeks after the report of the first COVID-19 case in Wuhan, China. By September 15, 2021, the number of cases in South Korea had increased to 277,989. Thus, it is important to better understand geographical transmission and design effective local-level pandemic plans across the country over the long term. We conducted a spatiotemporal analysis of weekly COVID-19 cases in South Korea from February 1, 2020, to May 30, 2021, in each administrative region. For the spatial domain, we first covered the entire country and then focused on metropolitan areas, including Seoul, Gyeonggi-do, and Incheon. Moran's I and spatial scan statistics were used for spatial analysis. The temporal variation and dynamics of COVID-19 cases were investigated with various statistical visualization methods. We found time-varying clusters of COVID-19 in South Korea using a range of statistical methods. In the early stage, the spatial hotspots were focused in Daegu and Gyeongsangbuk-do. Then, metropolitan areas were detected as hotspots in December 2020. In our study, we conducted a time-varying spatial analysis of COVID-19 across the entirety of South Korea over a long-term period and found a powerful approach to demonstrating the current dynamics of spatial clustering and understanding the dynamic effects of policies on COVID-19 across South Korea. Additionally, the proposed spatiotemporal methods are very useful for understanding the spatial dynamics of COVID-19 in South Korea.

Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices

Most coronavirus disease 2019 (COVID-19) models use a combination of agent-based and equation-based models with only a few incorporating environmental factors in their prediction models. Many studies have shown that human and environmental factors play huge roles in disease transmission and spread, but few have combined the use of both factors, especially for SARS-CoV-2. In this study, both man-made policies (Stringency Index) and environment variables (Niño SST Index) were combined to predict the number of COVID-19 cases in South Korea. The performance indicators showed satisfactory results in modeling COVID-19 cases using the Non-linear Autoregressive Exogenous Model (NARX) as the modeling method, and Stringency Index (SI) and Niño Sea Surface Temperature (SST) as model variables. In this study, we showed that the accuracy of SARS-CoV-2 transmission forecasts may be further improved by incorporating both the Niño SST and SI variables and combining these variables with NARX may outperform other models. Future forecasting work by modelers should consider including climate or environmental variables (i.e., Niño SST) to enhance the prediction of transmission and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Overlapping Delta and Omicron Outbreaks During the COVID-19 Pandemic: Dynamic Panel Data Estimates

BACKGROUND

The Omicron variant of SARS-CoV-2 is more transmissible than prior variants of concern (VOCs). It has caused the largest outbreaks in the pandemic, with increases in mortality and hospitalizations. Early data on the spread of Omicron were captured in countries with relatively low case counts, so it was unclear how the arrival of Omicron would impact the trajectory of the pandemic in countries already experiencing high levels of community transmission of Delta.

OBJECTIVE

The objective of this study is to quantify and explain the impact of Omicron on pandemic trajectories and how they differ between countries that were or were not in a Delta outbreak at the time Omicron occurred.

METHODS

We used SARS-CoV-2 surveillance and genetic sequence data to classify countries into 2 groups: those that were in a Delta outbreak (defined by at least 10 novel daily transmissions per 100,000 population) when Omicron was first sequenced in the country and those that were not. We used trend analysis, survival curves, and dynamic panel regression models to compare outbreaks in the 2 groups over the period from November 1, 2021, to February 11, 2022. We summarized the outbreaks in terms of their peak rate of SARS-CoV-2 infections and the duration of time the outbreaks took to reach the peak rate.

RESULTS

Countries that were already in an outbreak with predominantly Delta lineages when Omicron arrived took longer to reach their peak rate and saw greater than a twofold increase (2.04) in the average apex of the Omicron outbreak compared to countries that were not yet in an outbreak.

CONCLUSIONS

These results suggest that high community transmission of Delta at the time of the first detection of Omicron was not protective, but rather preluded larger outbreaks in those countries. Outbreak status may reflect a generally susceptible population, due to overlapping factors, including climate, policy, and individual behavior. In the absence of strong mitigation measures, arrival of a new, more transmissible variant in these countries is therefore more likely to lead to larger outbreaks. Alternately, countries with enhanced surveillance programs and incentives may be more likely to both exist in an outbreak status and detect more cases during an outbreak, resulting in a spurious relationship. Either way, these data argue against herd immunity mitigating future outbreaks with variants that have undergone significant antigenic shifts.

The impact of three progressively introduced interventions on second wave daily COVID-19 case numbers in Melbourne, Australia

BACKGROUND

The city of Melbourne, Australia experienced two waves of the COVID-19 epidemic peaking, the first in March and a more substantial wave in July 2020. During the second wave, a series of control measure were progressively introduced that initially slowed the growth of the epidemic then resulted in decreasing cases until there was no detectable local transmission.

METHODS

To determine the relative efficacy of the progressively introduced intervention measures, we modelled the second wave as a series of exponential growth and decay curves. We used a linear regression of the log of daily cases vs time, using a four-segment linear spline model corresponding to implementation of the three successive major public health measures. The primary model used all reported cases between 14 June and 15 September 2020 then compared the projection of the model with observed cases predicting future case trajectory up until the 31 October 2020 to assess the use of exponential models in projecting the future course and planning future interventions. The main outcome measures were the exponential daily growth constants, analysis of residuals and estimates of the 95% confidence intervals for the expected case distributions, comparison of predicted daily cases.

RESULTS

The exponential growth/decay constants in the primary analysis were: 0.122 (s.e. 0.004), 0.035 (s.e. 0.005), - 0.037 (s.e. 0.011), and - 0.069 (s.e. 0.003) for the initial growth rate, Stage 3, Stage 3 + compulsory masks and Stage 4, respectively. Extrapolation of the regression model from the 14 September to the 31 October matched the decline in observed cases over this period.

CONCLUSIONS

The four-segment exponential model provided an excellent fit of the observed reported case data and predicted the day-to-day range of expected cases. The extrapolated regression accurately predicted the decline leading to epidemic control in Melbourne.

The COVID-19 pandemic, an environmental neurology perspective

Neurologists have a particular interest in SARS-CoV-2 because the nervous system is a major participant in COVID-19, both in its acute phase and in its persistent post-COVID phase. The global spread of SARS-CoV-2 infection has revealed most of the challenges and risk factors that humanity will face in the future. We review from an environmental neurology perspective some characteristics that have underpinned the pandemic. We consider the agent, SARS-CoV-2, the spread of SARS-CoV-2 as influenced by environmental factors, its impact on the brain and some containment measures on brain health. Several questions remain, including the differential clinical impact of variants, the impact of SARS-CoV-2 on sleep and wakefulness, and the neurological components of Long-COVID syndrome. We touch on the role of national leaders and public health policies that have underpinned management of the COVID-19 pandemic. Increased awareness, anticipation and preparedness are needed to address comparable future challenges.

Is There a Relationship Between ADHD and COVID-19 Prevalence and Mortality Indices? An Analysis of Data From 156 Countries

OBJECTIVE

To examine the relationship between nation-level prevalence of ADHD and COVID-19 prevalence and mortality indices.

METHOD

Associations between nation-wise estimated prevalence, crude mortality rates and case-fatality ratios for COVID-19 and estimated prevalence rates for ADHD were examined, controlling for medical conditions known to be associated with COVID-19 outcome, as well as demographic, climate-related, and economic variables.

RESULTS

Prevalence of ADHD was positively correlated with COVID-19 prevalence and crude mortality rates on bivariate analyses, though the strength of this association was low. On multivariate regression, prevalence of ADHD was negatively associated with COVID-19 prevalence and crude mortality rates, though only the former finding was statistically significant.

CONCLUSION

The association between ADHD and COVID-19 prevalence and mortality at a national level is inconsistent, modest, and may be largely due to confounding factors such as age, lifestyle factors, and medical comorbidities.

Lower COVID-19 Incidence in Low-Continentality West-Coast Areas of Europe

In March 2020, the first known cases of COVID-19 occurred in Europe. Subsequently, the pandemic developed a seasonal pattern. The incidence of COVID-19 comprises spatial heterogeneity and seasonal variations, with lower and/or shorter peaks resulting in lower total incidence and higher and/or longer peaks resulting higher total incidence. The reason behind this phenomena is still unclear. Unraveling factors that explain why certain places have higher versus lower total COVID-19 incidence can help health decision makers understand and plan for future waves of the pandemic. We test whether differences in the total incidence of COVID-19 within five European countries (Norway, Sweden, Germany, Italy, and Spain), correlate with two environmental factors: the Köppen-Geiger climate zones and the Continentality Index, while statistically controlling for crowding. Our results show that during the first 16 months of the pandemic (March 2020 to July 2021), climate zones with larger annual differences in temperature and annually distributed precipitation show a higher total incidence than climate zones with smaller differences in temperature and dry seasons. This coincides with lower continentality values. Total incidence increases with continentality, up to a Continentality Index value of 19, where a peak is reached in the semicontinental zone. Low continentality (high oceanic influence) appears to be a strong suppressing factor for COVID-19 spread. The incidence in our study area is lowest at open low continentality west coast areas.

The provision of COVID-19 vaccines developed in China to other countries: A cross-sectional online survey on the views of the Chinese public

BACKGROUND

COVID-19 vaccines are in short supply globally. China was among the first countries to pledge supplies of the COVID-19 vaccine as a global public product, and to date the country has provided more than 600 million vaccines to more than 200 countries and regions with low COVID-19 vaccination rates. Understanding the public's attitude in China towards the global distribution of COVID-19 vaccines could inform global and national decisions, policies and debates.

OBJECTIVE

The aim of this study was to determine the attitudes of adults living in China regarding the global allocation of COVID-19 vaccines developed in China, and how these attitudes vary across provinces and by sociodemographic characteristics.

METHODS

We conducted a cross-sectional online survey among adults registered with the survey company KuRunData. The survey asked participants 31 questions on their attitudes regarding the global allocation of COVID-19 vaccines developed in China. We disaggregated responses by province and sociodemographic characteristics. All analyses used survey sampling weights.

RESULTS

A total of 10,000 participants completed the questionnaire. Participants generally favored providing COVID-19 vaccines to foreign countries before fully fulfilling domestic needs (75.6%, 95% CI: 74.6% - 76.5%). Women (76.8%, OR = 1.18, 95% CI: 1.07 - 1.32, P = .002) and those living in rural areas (76.8%, OR = 1.13, 95% CI: 1.01 - 1.27, P = .03) were especially likely to hold this opinion. Most respondents preferred providing financial support through international platforms rather than directly offering support to individual countries (72.1%, 95% CI: 71.0% - 73.1%), while for vaccine products they preferred direct provision to relevant countries instead of via a delivery platform such as COVAX (77.3%, 95% CI: 76.3% - 78.2%).

CONCLUSIONS

Among our survey sample, we find that adults are generally supportive of the international distribution of COVID-19 vaccines, which may encourage policy makers to support and implement the distribution of COVID-19 vaccines developed in China globally. Conducting similar surveys in other countries could help align policymakers' actions on COVID-19 vaccine distribution with the preferences of their constituencies.

CLINICALTRIAL

The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends

BACKGROUND

Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy.

OBJECTIVE

This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series.

METHODS

We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis.

RESULTS

Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals' using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006).

CONCLUSIONS

Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19-related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.

The Role of Vitamin D in Supporting Health in the COVID-19 Era

The genomic activity of vitamin D is associated with metabolic effects, and the hormone has a strong impact on several physiological functions and, therefore, on health. Among its renowned functions, vitamin D is an immunomodulator and a molecule with an anti-inflammatory effect, and, recently, it has been much studied in relation to its response against viral infections, especially against COVID-19. This review aims to take stock of the correlation studies between vitamin D deficiency and increased risks of severe COVID-19 disease and, similarly, between vitamin D deficiency and acute respiratory distress syndrome. Based on this evidence, supplementation with vitamin D has been tested in clinical trials, and the results are discussed. Finally, this study includes a biochemical analysis on the effects of vitamin D in the body's defense mechanisms against viral infection. In particular, the antioxidant and anti-inflammatory functions are considered in relation to energy metabolism, and the potential, beneficial effect of vitamin D in COVID-19 is described, with discussion of its influence on different biochemical pathways. The proposed, broader view of vitamin D activity could support a better-integrated approach in supplementation strategies against severe COVID-19, which could be valuable in a near future of living with an infection becoming endemic.

Nonlinear frequency analysis of COVID-19 spread in Tokyo using empirical mode decomposition

Empirical mode decomposition (EMD) was adopted to decompose daily COVID-19 infections in Tokyo from February 28, 2020, to July 12, 2021. Daily COVID-19 infections were nonlinearly decomposed into several monochromatic waves, intrinsic mode functions (IMFs), corresponding to their periodic meanings from high frequency to low frequency. High-frequency IMFs represent variabilities of random factors and variations in the number of daily PCR and antigen inspections, which can be nonlinearly denoised using EMD. Compared with a moving average and Fourier transform, EMD provides better performance in denoising and analyzing COVID-19 spread. After variabilities of daily inspections were weekly denoised by EMD, one low-frequency IMF reveals that the average period of external influences (public health and social measures) to stop COVID-19 spread was 19 days, corresponding to the measures response duration based on the incubation period. By monitoring this nonlinear wave, public health and social measures for stopping COVID-19 spread can be evaluated and visualized quantitatively in the instantaneous frequency domain. Moreover, another low-frequency IMF revealed that the period of the COVID-19 outbreak and retreat was 57 days on average. This nonlinear wave can be used as a reference for setting the timeframe for state of emergency declarations. Thus, decomposing daily infections in the instantaneous frequency domain using EMD represents a useful tool to improve public health and social measures for stopping COVID-19 spread.

The influence of meteorological variables and lockdowns on COVID-19 cases in urban agglomerations of Indian cities

Coronavirus has been identified as one of the deadliest diseases and the WHO has declared it a pandemic and a global health crisis. It has become a massive challenge for humanity. India is also facing its fierceness as it is highly infectious and mutating at a rapid rate. To control its spread, many interventions have been applied in India since the first reported case on January 30, 2020. Several studies have been conducted to assess the impact of climatic and weather conditions on its spread in the last one and half years span. As it is a well-established fact that temperature and humidity could trigger the onset of diseases such as influenza and respiratory disorders, the relationship of meteorological variables with the number of COVID-19 confirmed cases has been anticipated. The association of several meteorological variables has therefore been studied in the past with the number of COVID-19 confirmed cases. The conclusions in those studies are based on the data obtained at an early stage, and the inferences drawn based on those short time series studies may not be valid over a longer period. This study attempted to assess the influence of temperature, humidity, wind speed, dew point, previous day's number of deaths, and government interventions on the number of COVID-19 confirmed cases in 18 districts of India. It is also attempted to identify the important predictors of the number of confirmed COVID-19 cases in those districts. The random forest model and the hybrid model obtained by modelling the random forest model's residuals are used to predict the response variable. It is observed that meteorological variables are useful only to some extent when used with the data on the number of the previous day's deaths and lockdown information in predicting the number of COVID-19 cases. Partial lockdown is more important than complete or no lockdown in predicting the number of confirmed COVID-19 cases. Since the time span of the data in the study is reasonably large, the information is useful to policymakers in balancing the restriction activities and economic losses to individuals and the government.

SARS-CoV-2 outbreaks in hospitals and long-term care facilities in Germany: a national observational study

Background

Outbreaks of coronavirus disease (COVID-19) in hospitals and long-term care facilities (LTCFs) pose serious public health threats. We analysed how frequency and size of SARS-CoV-2 outbreaks in hospitals and LTCFs have altered since the beginning of the pandemic, in particular since the start of the vaccination campaign.

Methods

We used mandatory notification data on SARS-CoV-2 cases in Germany and stratified by outbreak cases in hospitals and LTCFs. German vaccination coverage data were analysed. We studied the association of the occurrence of SARS-CoV-2 outbreaks and outbreak cases with SARS-CoV-2 cases in Germany throughout the four pandemic waves. We built also counterfactual scenarios with the first pandemic wave as the baseline.

Findings

By 21 September 2021, there were 4,147,387 SARS-CoV-2 notified cases since March 2020. About 20% of these cases were reported as being related to an outbreak, with 1% of the cases in hospitals and 4% in LTCFs. The median number of outbreak cases in the different phases was smaller (≤5) in hospitals than in LTCFs (>10). In the first and second pandemic waves, we observed strong associations in both facility types between SARS-CoV-2 outbreak cases and total number of notified SARS-CoV-2 cases. However, during the third pandemic wave we observed a decline in outbreak cases in both facility types and only a weak association between outbreak cases and all cases.

Interpretation

The vaccination campaign and non-pharmaceutical interventions have been able to protect vulnerable risk groups in hospitals and LTCFs.

Funding

No specific funding.

COVID-19 Impact on Public Health, Environment, Human Psychology, Global Socioeconomy, and Education

The end of the year 2019 was marked by the introduction of a third highly pathogenic coronavirus, after SARS-CoV (2003) and MERS-CoV (2012), in the human population which was officially declared a global pandemic by the World Health Organization (WHO) on March 11, 2020. Indeed, the pandemic of COVID-19 (Coronavirus Disease 19) has evolved at an unprecedented rate: after its emergence in Wuhan, the capital of the province of Hubei of the People's Republic of China, in December 2019, the total number of confirmed cases did not cease growing very quickly in the world. In this manuscript, we have provided an overview of the impact of COVID-19 on health, and we have proposed different nutrients suitable for infected patients to boost their immune systems. On the other hand, we have described the advantages and disadvantages of COVID-19 on the environment including the quality of water, air, waste management, and energy consumption, as well as the impact of this pandemic on human psychology, the educational system, and the global economy. In addition, we have tried to come up with some solutions to counter the negative repercussions of the pandemic.