Seasonal variation and COVID-19 infection pattern: A gap from evidence to reality

Seasonal and periodic patterns in US COVID-19 mortality using the Variable Bandpass Periodic Block Bootstrap

Since the emergence of the SARS-CoV-2 virus, research into the existence, extent, and pattern of seasonality has been of the highest importance for public health preparation. This study uses a novel bandpass bootstrap approach called the Variable Bandpass Periodic Block Bootstrap to investigate the periodically correlated components including seasonality within US COVID-19 mortality. Bootstrapping to produce confidence intervals for periodic characteristics such as the seasonal mean requires preservation of the periodically correlated component's correlation structure during resampling. While other existing bootstrap methods can preserve the periodically correlated component correlation structure, filtration of that periodically correlated component's frequency from interference is critical to bootstrap the periodically correlated component's characteristics accurately and efficiently. The Variable Bandpass Periodic Block Bootstrap filters the periodically correlated time series to reduce interference from other components such as noise. This greatly reduces bootstrapped confidence interval size and outperforms the statistical power and accuracy of other methods when estimating the periodic mean sampling distribution. Variable Bandpass Periodic Block Bootstrap analysis of US COVID-19 mortality periodically correlated components is provided and compared against alternative bootstrapping methods. Results show that both methods find a significant seasonal component, but the Variable Bandpass Periodic Block Bootstrap produces smaller confidence intervals and only the Variable Bandpass Periodic Block Bootstrap found significant components at the second through the fifth harmonics of the seasonal component, as well as weekly component. This crucial evidence supporting the presence of a seasonal pattern and existence of additional periodically correlated components, their timing, and confidence intervals for their effect which will aid prediction and preparation for future COVID-19 responses.

A Cross-Sectional Study Revealed a Low Prevalence of SARS-CoV-2 Infection among Asymptomatic University Students in Tripoli, North Lebanon

This study aimed to investigate the SARS-CoV-2 infection prevalence among >18-year-old students in the Faculty of Public Health and Faculty of Sciences at the Lebanese University in Tripoli, Northern Lebanon, in June 2023 and to characterize the circulating Omicron subvariants. Out of 357 participants, only 2 (0.56%) tested positive by qPCR, corresponding to 0.61% (2/326) of asymptomatic students. One case tested positive with a qPCR targeting the Omicron BA.2 variant. These findings indicate a low incidence at that time and emphasize the interest of SARS-CoV-2 surveillance among students.

Seasonality of COVID-19 incidence in the United States

Background

The surges of Coronavirus Disease 2019 (COVID-19) appeared to follow a repeating pattern of COVID-19 outbreaks regardless of social distancing, mask mandates, and vaccination campaigns.

Objectives

This study aimed to investigate the seasonality of COVID-19 incidence in the United States of America (USA), and to delineate the dominant frequencies of the periodic patterns of the disease.

Methods

We characterized periodicity in COVID-19 incidences over the first three full seasonal years (March 2020 to March 2023) of the COVID-19 pandemic in the USA. We utilized a spectral analysis approach to find the naturally occurring dominant frequencies of oscillation in the incidence data using a Fast Fourier Transform (FFT) algorithm.

Results

Our study revealed four dominant peaks in the periodogram: the two most dominant peaks show a period of oscillation of 366 days and 146.4 days, while two smaller peaks indicate periods of 183 days and 122 days. The period of 366 days indicates that there is a single COVID-19 outbreak that occurs approximately once every year, which correlates with the dominant outbreak in the early/mid-winter months. The period of 146.4 days indicates approximately 3 peaks per year and matches well with each of the 3 annual outbreaks per year.

Conclusion

Our study revealed the predictable seasonality of COVID-19 outbreaks, which will guide public health preventative efforts to control future outbreaks. However, the methods used in this study cannot predict the amplitudes of the incidences in each outbreak: a multifactorial problem that involves complex environmental, social, and viral strain variables.

Measurement of SARS-CoV-2 RNA in wastewater: A case study of the Northern Cape, South Africa

The SARS-CoV-2 pandemic has resulted in the infection and death of many South Africans. This is in part due to a lack of testing facilities, equipment, and staff in many areas, particularly those with low population densities. The study focused on the infection dynamics of the virus in the Northern Cape province in all five municipalities investigating wastewater-based surveillance for the province. Reverse transcription was used to identify the virus, and SARS-CoV-2 RNA was detected in a batch of wastewater from four of the five areas sampled and was collected in the months that fall within the third wave of COVID as well as the winter season (May-July). The detection of the SARS-CoV-2 RNA correlated with infection statistics as well as the seasonality of the virus. This research showed a positive result in using wastewater epidemiology to track the spread of the virus but also highlighted the need for improved methodology when it comes to this surveillance. This includes sampling smaller areas and frequent sampling in multiple areas to show clear patterns within smaller, sparsely populated communities.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Environmental factors and their impact on the COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic has resulted in numerous cases of illness and death worldwide. Research has shown that there are associations between transmission, as well as the severity of SARS-CoV‑2 (severe acute respiratory syndrome coronavirus 2) infections, and various environmental factors. For example, air pollution with particulate matter is thought to play a crucial role, and both climatic and geographical aspects must be considered. Furthermore, environmental conditions such as industry and urban lifestyle have a significant impact on air quality and thus on health aspects of the population. In this regard, other factors such as chemicals, microplastics, and diet also critically impact health, including respiratory and cardiovascular diseases. Overall, the COVID-19 pandemic has highlighted how closely health and the environment are linked. This review discusses the impact of environmental factors on the COVID-19 pandemic.

What are the Effects of Climate Variables on COVID-19 Pandemic? A Systematic Review and Current Update

The climatological parameters can be different in various geographical locations. Moreover, they have possible impacts on COVID-19 incidence. Therefore, the purpose of this systematic review article was to describe the effects of climatic variables on COVID-19 pandemic in different countries. Systematic literature search was performed in Scopus, ISI Web of Science, and PubMed databases using ("Climate" OR "Climate Change" OR "Global Warming" OR "Global Climate Change" OR "Meteorological Parameters" OR "Temperature" OR "Precipitation" OR "Relative Humidity" OR "Wind Speed" OR "Sunshine" OR "Climate Extremes" OR "Weather Extremes") AND ("COVID" OR "Coronavirus disease 2019" OR "COVID-19" OR "SARS-CoV-2" OR "Novel Coronavirus") keywords. From 5229 articles, 424 were screened and 149 were selected for further analysis. The relationship between meteorological parameters is variable in different geographical locations. The results indicate that among the climatic indicators, the temperature is the most significant factor that influences on COVID-19 pandemic in most countries. Some studies were proved that warm and wet climates can decrease COVID-19 incidence; however, the other studies represented that warm location can be a high risk of COVID-19 incidence. It could be suggested that all climate variables such as temperature, humidity, rainfall, precipitation, solar radiation, ultraviolet index, and wind speed could cause spread of COVID-19. Thus, it is recommended that future studies will survey the role of all meteorological variables and interaction between them on COVID-19 spread in specific small areas such as cities of each country and comparison between them.

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.

Assessing the impact of air pollution and climate seasonality on COVID-19 multiwaves in Madrid, Spain

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020-1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future.

Study of Post-COVID-19 Syndrome in Saudi Arabia

Background Post-COVID-19 syndrome may be predisposed by organ damage as a complication of COVID-19. Patients may experience persistent symptoms after recovering from their initial illness. Objectives To identify manifestations and predisposing factors for post-COVID-19 syndrome in Saudi Arabia. Methods A cross-sectional study was conducted from May 2021 through June 2021 using an online structured pre-coded closed-ended, pilot-tested questionnaire in Arabic. It included male and female inhabitants of Saudi Arabia aged 18 years and above with a past history of COVID-19 infection. Descriptive statistics were carried out for all variables. A univariate analysis Chi-square test and independent t-test were used. A p-value of less than 0.05 was considered significant. Results A total of 85.3% of post-COVID-19 cases had general manifestations: 77.3% had musculoskeletal and joint complaints, 61.3% had mental and psychological problems, 58.7% had gastrointestinal manifestations, 44% had renal complaints, 41.3% had respiratory complaints, and 36.0% had cardiovascular symptoms. Gender, age, smoking, BMI, associated morbid condition, number of previous COVID-19 attacks, the severity of illness, place of treatment, and complications of COVID-19 due to treatment or hospitalization were significantly correlated with the occurrence of post-COVID-19 syndrome. Conclusion Post-COVID-19 syndrome could be manifested by fatigue, malaise, myalgia, joint pain, depression, anxiety, sleep, memory and concentration disturbances, cough, dyspnea, palpations, arrhythmias, and chest pain. It may be influenced by male gender, smoking, old age, high BMI, comorbidities, and past COVID-19 attacks with regard to the number, place of treatment, and occurrence of complications.

The Immunological Factors Predisposing to Severe Covid-19 Are Already Present in Healthy Elderly and Men

Male sex and old age are risk factors for COVID-19 severity, but the underlying causes are unknown. A possible explanation for this might be the differences in immunological profiles in males and the elderly before the infection. With this in mind, we analyzed the abundance of circulating proteins and immune populations associated with severe COVID-19 in 2 healthy cohorts. Besides, given the seasonal profile of COVID-19, the seasonal response against SARS-CoV-2 could also be different in the elderly and males. Therefore, PBMCs of female, male, young, and old subjects in different seasons of the year were stimulated with heat-inactivated SARS-CoV-2 to investigate the season-dependent anti-SARS-CoV-2 immune response. We found that several T cell subsets, which are known to be depleted in severe COVID-19 patients, were intrinsically less abundant in men and older individuals. Plasma proteins increasing with disease severity, including HGF, IL-8, and MCP-1, were more abundant in the elderly and males. Upon in vitro SARS-CoV-2 stimulation, the elderly produced significantly more IL-1RA and had a dysregulated IFNγ response with lower production in the fall compared with young individuals. Our results suggest that the immune characteristics of severe COVID-19, described by a differential abundance of immune cells and circulating inflammatory proteins, are intrinsically present in healthy men and the elderly. This might explain the susceptibility of men and the elderly to SARS-CoV-2 infection.

Exploring the linkage between seasonality of environmental factors and COVID-19 waves in Madrid, Spain

Like several countries, Spain experienced a multi wave pattern of COVID-19 pandemic over more than one year period, between spring 2020 and spring 2021. The transmission of SARS-CoV-2 pandemics is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation.This study aims to quantify the impact of climate and air pollution factors seasonality on incidence and severity of COVID-19 disease waves in Madrid metropolitan region in Spain. We employed descriptive statistics and Spearman rank correlation tests for analysis of daily in-situ and geospatial time-series of air quality and climate data to investigate the associations with COVID-19 incidence and lethality in Madrid under different synoptic meteorological patterns. During the analyzed period (1 January 2020-28 February 2021), with one month before each of three COVID-19 waves were recorded anomalous anticyclonic circulations in the mid-troposphere, with positive anomalies of geopotential heights at 500 mb and favorable stability conditions for SARS-CoV-2 fast diffusion. In addition, the results reveal that air temperature, Planetary Boundary Layer height, ground level ozone have a significant negative relationship with daily new COVID-19 confirmed cases and deaths. The findings of this study provide useful information to the public health authorities and policymakers for optimizing interventions during pandemics.

Which National Factors Are Most Influential in the Spread of COVID-19?

The outbreak of the novel COVID-19, declared a global pandemic by WHO, is the most serious public health threat seen in terms of respiratory viruses since the 1918 H1N1 influenza pandemic. It is surprising that the total number of COVID-19 confirmed cases and the number of deaths has varied greatly across countries. Such great variations are caused by age population, health conditions, travel, economy, and environmental factors. Here, we investigated which national factors (life expectancy, aging index, human development index, percentage of malnourished people in the population, extreme poverty, economic ability, health policy, population, age distributions, etc.) influenced the spread of COVID-19 through systematic statistical analysis. First, we employed segmented growth curve models (GCMs) to model the cumulative confirmed cases for 134 countries from 1 January to 31 August 2020 (logistic and Gompertz). Thus, each country's COVID-19 spread pattern was summarized into three growth-curve model parameters. Secondly, we investigated the relationship of selected 31 national factors (from KOSIS and Our World in Data) to these GCM parameters. Our analysis showed that with time, the parameters were influenced by different factors; for example, the parameter related to the maximum number of predicted cumulative confirmed cases was greatly influenced by the total population size, as expected. The other parameter related to the rate of spread of COVID-19 was influenced by aging index, cardiovascular death rate, extreme poverty, median age, percentage of population aged 65 or 70 and older, and so forth. We hope that with their consideration of a country's resources and population dynamics that our results will help in making informed decisions with the most impact against similar infectious diseases.