Questioning the seasonality of SARS-COV-2: a Fourier spectral analysis

COVID-19 Vaccination Strategies in the Endemic Period: Lessons from Influenza

Coronavirus disease 2019 (COVID-19) is a highly contagious zoonotic respiratory disease with many similarities to influenza. Effective vaccines are available for both; however, rapid viral evolution and waning immunity make them virtually impossible to eradicate with vaccines. Thus, the practical goal of vaccination is to reduce the incidence of serious illnesses and death. Three years after the introduction of COVID-19 vaccines, the optimal vaccination strategy in the endemic period remains elusive, and health authorities worldwide have begun to adopt various approaches. Herein, we propose a COVID-19 vaccination strategy based on the data available until early 2024 and discuss aspects that require further clarification for better decision making. Drawing from comparisons between COVID-19 and influenza vaccination strategies, our proposed COVID-19 vaccination strategy prioritizes high-risk groups, emphasizes seasonal administration aligned with influenza vaccination campaigns, and advocates the co-administration with influenza vaccines to increase coverage.

Drawing a parallel between the trend of confirmed COVID-19 deaths in the winters of 2022/2023 and 2023/2024 in Italy, with a prediction

We studied the weekly number and the growth/decline rates of COVID-19 deaths of the period from October 31, 2022, to February 9, 2023, in Italy. We found that the COVID-19 winter wave reached its peak during the three holiday weeks from December 16, 2022, to January 5, 2023, and it was definitely trending downward, returning to the same number of deaths as the end of October 2022, in the first week February 2023. During this period of 15 weeks, that wave caused a number of deaths as large as 8,526. Its average growth rate was +7.89% deaths per week (10 weeks), while the average weekly decline rate was -15.85% (5 weeks). At the time of writing of this paper, Italy has been experiencing a new COVID-19 wave, with the latest 7 weekly bulletins (October 26, 2023 - December 13, 2023) showing that deaths have climbed from 148 to 322. The weekly growth rate had risen by +14.08% deaths, on average. Hypothesizing that this 2023/2024 wave will have a total duration similar to that of 2022/2023, with comparable extensions of both the growth period and the decline period and similar growth/decline rates, we predict that the number of COVID-19 deaths of the period from the end of October 2023 to the beginning of February 2024 should be less than 4100. A preliminary assessment of this forecast, based on 11 of the 15 weeks of the period, has already confirmed the accuracy of this approach.

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.

Unraveling the Dynamics of Omicron (BA.1, BA.2, and BA.5) Waves and Emergence of the Deltacton Variant: Genomic Epidemiology of the SARS-CoV-2 Epidemic in Cyprus (Oct 2021-Oct 2022)

Commencing in December 2019 with the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), three years of the coronavirus disease 2019 (COVID-19) pandemic have transpired. The virus has consistently demonstrated a tendency for evolutionary adaptation, resulting in mutations that impact both immune evasion and transmissibility. This ongoing process has led to successive waves of infections. This study offers a comprehensive assessment spanning genetic, phylogenetic, phylodynamic, and phylogeographic dimensions, focused on the trajectory of the SARS-CoV-2 epidemic in Cyprus. Based on a dataset comprising 4700 viral genomic sequences obtained from affected individuals between October 2021 and October 2022, our analysis is presented. Over this timeframe, a total of 167 distinct lineages and sublineages emerged, including variants such as Delta and Omicron (1, 2, and 5). Notably, during the fifth wave of infections, Omicron subvariants 1 and 2 gained prominence, followed by the ascendancy of Omicron 5 in the subsequent sixth wave. Additionally, during the fifth wave (December 2021-January 2022), a unique set of Delta sequences with genetic mutations associated with Omicron variant 1, dubbed "Deltacron", was identified. The emergence of this phenomenon initially evoked skepticism, characterized by concerns primarily centered around contamination or coinfection as plausible etiological contributors. These hypotheses were predominantly disseminated through unsubstantiated assertions within the realms of social and mass media, lacking concurrent scientific evidence to validate their claims. Nevertheless, the exhaustive molecular analyses presented in this study have demonstrated that such occurrences would likely lead to a frameshift mutation-a genetic aberration conspicuously absent in our provided sequences. This substantiates the accuracy of our initial assertion while refuting contamination or coinfection as potential etiologies. Comparable observations on a global scale dispelled doubt, eventually leading to the recognition of Delta-Omicron variants by the scientific community and their subsequent monitoring by the World Health Organization (WHO). As our investigation delved deeper into the intricate dynamics of the SARS-CoV-2 epidemic in Cyprus, a discernible pattern emerged, highlighting the major role of international connections in shaping the virus's local trajectory. Notably, the United States and the United Kingdom were the central conduits governing the entry and exit of the virus to and from Cyprus. Moreover, notable migratory routes included nations such as Greece, South Korea, France, Germany, Brazil, Spain, Australia, Denmark, Sweden, and Italy. These empirical findings underscore that the spread of SARS-CoV-2 within Cyprus was markedly influenced by the influx of new, highly transmissible variants, triggering successive waves of infection. This investigation elucidates the emergence of new waves of infection subsequent to the advent of highly contagious and transmissible viral variants, notably characterized by an abundance of mutations localized within the spike protein. Notably, this discovery decisively contradicts the hitherto hypothesis of seasonal fluctuations in the virus's epidemiological dynamics. This study emphasizes the importance of meticulously examining molecular genetics alongside virus migration patterns within a specific region. Past experiences also emphasize the substantial evolutionary potential of viruses such as SARS-CoV-2, underscoring the need for sustained vigilance. However, as the pandemic's dynamics continue to evolve, a balanced approach between caution and resilience becomes paramount. This ethos encourages an approach founded on informed prudence and self-preservation, guided by public health authorities, rather than enduring apprehension. Such an approach empowers societies to adapt and progress, fostering a poised confidence rooted in well-founded adaptation.

SARS-CoV-2 Survival on Surfaces. Measurements Optimisation for an Enthalpy-Based Assessment of the Risk

The present work, based on the results found in the literature, yields a consistent model of SARS-CoV-2 survival on surfaces as environmental conditions, such as temperature and relative humidity, change simultaneously. The Enthalpy method, which has recently been successfully proposed to investigate the viability of airborne viruses using a holistic approach, is found to allow us to take a reasoned reading of the data available on surfaces in the literature. This leads us to identify the domain of conditions of lowest SARS-CoV-2 viability, in a specific enthalpy range between 50 and 60 kJ/Kg_dry-air. This range appears well-superimposed with the results we previously obtained from analyses of coronaviruses' behaviour in aerosols, and may be helpful in dealing with the spread of infections. To steer future investigations, shortcomings and weaknesses emerging from the assessment of viral measurement usually carried out on surfaces are also discussed in detail. Once demonstrated that current laboratory procedures suffer from both high variability and poor standardisation, targeted implementations of standards and improvement of protocols for future investigations are then proposed.

Predictive health intelligence: Potential, limitations and sense making

We discuss the new paradigm of predictive health intelligence, based on the use of modern deep learning algorithms and big biomedical data, along the various dimensions of: a) its potential, b) the limitations it encounters, and c) the sense it makes. We conclude by reasoning on the idea that viewing data as the unique source of sanitary knowledge, fully abstracting from human medical reasoning, may affect the scientific credibility of health predictions.

A Comparison of Etiology, Pathogenesis, Vaccinal and Antiviral Drug Development between Influenza and COVID-19

Influenza virus and coronavirus, two kinds of pathogens that exist widely in nature, are common emerging pathogens that cause respiratory tract infections in humans. In December 2019, a novel coronavirus SARS-CoV-2 emerged, causing a severe respiratory infection named COVID-19 in humans, and raising a global pandemic which has persisted in the world for almost three years. Influenza virus, a seasonally circulating respiratory pathogen, has caused four global pandemics in humans since 1918 by the emergence of novel variants. Studies have shown that there are certain similarities in transmission mode and pathogenesis between influenza and COVID-19, and vaccination and antiviral drugs are considered to have positive roles as well as several limitations in the prevention and control of both diseases. Comparative understandings would be helpful to the prevention and control of these diseases. Here, we review the study progress in the etiology, pathogenesis, vaccine and antiviral drug development for the two diseases.

Analysis of reporting lag in daily data of COVID-19 in Japan

The daily announcement of positive COVID-19 cases had a major socioeconomic impact. In Japan, it is well known that the characteristic of this number as time series data is the weekly periodicity. We assume that this periodicity is generated by changes in the timing of reporting on the weekend. We analyze a lag structure that shows how congestion that occurs over the weekend affects the number of new confirmed cases at the beginning of the following week. We refer to this reporting delay as the weekend effect. Our study aims to describe the geographical heterogeneity found in the time series of reported positive cases. We use data on the number of new positives reported by the prefectures. Our results suggest that delays generally occur in prefectures with a population of more than 2 million, including Japan's three largest metropolitan areas, Tokyo, Osaka, and Nagoya. The number of new positives was higher in the more populated prefectures. This will explain the weekend effect.

Excess mortality and COVID-19 deaths in Italy: A peak comparison study

During a sanitary crisis, excess mortality measures the number of all-cause deaths, beyond what we would have expected if that crisis had not occurred. The high number of COVID-19 deaths started a debate in Italy with two opposite positions: those convinced that COVID-19 deaths were not by default excess deaths, because many COVID-19 deaths were not correctly registered, with most being attributable to other causes and to the overall crisis conditions; and those who presented the opposite hypothesis. We analyzed the curve of the all-cause excess mortality, during the period of January 5, 2020-October 31, 2022, compared to the curve of the daily confirmed COVID-19 deaths, investigating the association between excess mortality and the recurrence of COVID-19 waves in Italy. We compared the two curves looking for the corresponding highest peaks, and we found that 5 out of the 6 highest peaks (83.3%) of the excess mortality curve have occurred, on average, just a week before the concomitant COVID-19 waves hit their highest peaks of daily deaths (Mean 6.4 days; SD 2.4 days). This temporal correspondence between the moments when the excess mortality peaked and the highest peaks of the COVID-19 deaths, provides further evidence in favor of a positive correlation between COVID-19 deaths and all-cause excess mortality.

Correlations between SARS-CoV-2 Infections and the Number of COVID-19 Vaccine Doses Administered in Three Italian Provinces

The aim of this ecological study is to evaluate correlations between the number of COVID-19 vaccine doses administered in three Italian provinces-one in the south, one in the center and one in the north of the country-and the registered numbers of COVID-19 cases in the same areas. The period of January 2021-September 2022 was considered, with specific analysis for fractions of times corresponding to the spread in Italy of the different SARS-CoV-2 variants. The results confirm the reduction of the effectiveness of the vaccines in preventing new COVID-19 cases in Italy, regardless of latitude, after the appearance of the first omicron variants. The new variants omicron 4 and 5 showed an extremely high spread during the Italian summer months; fortunately, the effects of the vaccinations in preventing new cases was improved compared to the previous omicron variants, showing a negative correlation between the new COVID-19 cases and the number of vaccine doses administered.

Influence of COVID-19 vaccination on the dynamics of new infected cases in the world

The initial COVID-19 vaccinations were created and distributed to the general population in 2020 thanks to emergency authorization and conditional approval. Consequently, numerous countries followed the process that is currently a global campaign. Taking into account the fact that people are being vaccinated, there are concerns about the effectiveness of that medical solution. Actually, this study is the first one focusing on how the number of vaccinated people might influence the spread of the pandemic in the world. From the Global Change Data Lab "Our World in Data", we were able to get data sets about the number of new cases and vaccinated people. This study is a longitudinal one from 14/12/2020 to 21/03/2021. In addition, we computed Generalized log-Linear Model on count time series (Negative Binomial distribution due to over dispersion in data) and implemented validation tests to confirm the robustness of our results. The findings revealed that when the number of vaccinated people increases by one new vaccination on a given day, the number of new cases decreases significantly two days after by one. The influence is not notable on the same day of vaccination. Authorities should increase the vaccination campaign to control well the pandemic. That solution has effectively started to reduce the spread of COVID-19 in the world.

COVID-19 cases with a contact history: A modeling study of contact history-stratified data in Japan

The purpose of the present study was to develop a transmission model of COVID-19 cases with and without a contact history to understand the meaning of the proportion of infected individuals with a contact history over time. We extracted epidemiological information regarding the proportion of coronavirus disease 2019 (COVID-19) cases with a contact history and analyzed incidence data stratified by the presence of a contact history in Osaka from January 15 to June 30, 2020. To clarify the relationship between transmission dynamics and cases with a contact history, we used a bivariate renewal process model to describe transmission among cases with and without a contact history. We quantified the next-generation matrix as a function of time; thus, the instantaneous (effective) reproduction number was calculated for different periods of the epidemic wave. We objectively interpreted the estimated next-generation matrix and replicated the proportion of cases with a contact p(t) over time, and we examined the relevance to the reproduction number. We found that p(t) does not take either the maximum or minimum value at a threshold level of transmission with R(t)=1.0. With R(t) < 1 (subcritical level), p(t) was a decreasing function of R(t). Qualitatively, the minimum p(t) was seen in the domain with R(t) > 1. An important future implication for use of the proposed model is to monitor the success of ongoing contact tracing practice. A decreasing signal of p(t) reflects the increasing difficulty of contact tracing. The present study findings indicate that monitoring p(t) would be a useful addition to surveillance.

Early psychological intervention in adult patients after hospitalization during COVID-19 pandemia. A single center observational study

The coronavirus disease 2019 pandemic has represented an individual and collective trauma with an impact on mental health. COVID-19 survivors need to be screened for psychological distress regularly for timely intervention. After March 2020, an outpatients clinic for follow up of discharged COVID-19 patients was set up at Infectious Diseases Department of the Hospital of Lecco, Italy. Blood exams, specialistic visits were performed for each patients and IES-R and BDI scales were dispensed. 523 patients were referred to the clinic; 93 of them resulted positive at IES-R and/or BDI self-report and 58 agreed to have early interviews with psychologist specialist. Patients could receive only a short psychoeducation/psychological support intervention or in addition to the same, even a specific trauma-focused psychotherapeutic intervention with EMDR where clinically indicated. IES-R e BDI were administered pre- and post-intervention. The results show that the average of the post-traumatic stress scores detected at IES-R is above the clinical cut-off for the entire sample. There is an overall change in the decrease in mean scores on the IES and BDI scales before and after psychological intervention. Among the patients for whom psychopharmacological therapy was also necessary, those who had COVID-mourning in family improved the most at IES-R scale post- intervention. With respect to EMDR treatment, there is a significant improvement in depressive symptoms noticed at BDI for male patients who have received neither psychotropic drugs nor CPAP. Being hospitalized for coronavirus has a significant impact on the patient's mental health and it is a priority to arrange early screening to intercept psychological distress and give it an early response.

Community Health Resources, Globalization, Trust in Science, and Voting as Predictors of COVID-19 Vaccination Rates: A Global Study with Implications for Vaccine Adherence

The COVID-19 global pandemic requires, not only an adequate supply of, but public adherence to safe and effective vaccinations. This study analyzes the human and economic resources and political and public attitudinal factors that influence widely varying country-level coronavirus vaccination rates. Using data on up to 95 countries, we found that countries’ strength of community health training and research (CHTR), education index, globalization, and vaccine supply are associated with a greater COVID-19 vaccination rate. In a separate analysis, certain political factors, and public attitudes (perceived government effectiveness, government fiscal decentralization, trust in science, and parliamentary voter turnout) predicted vaccination rates. Perceived corruption and actual freedoms (political rights and civil liberties) related to vaccination rates in prior studies were not significantly predictive when controlling for the above factors. The results confirm our prior findings on the importance of CHTR resources for increasing COVID-19 vaccination rates. They also suggest that to motivate vaccine adherence countries need, not only an adequate vaccine supply (which depends on a country having either its own resources or effective global political, social, and economic connections) and community health workforce training and research, but also a population that trusts in science, and is actively engaged in the political process.