The role of seasonality in the spread of COVID-19 pandemic

Averted mortality by COVID-19 vaccination in Belgium between 2021 and 2023

BACKGROUND

Vaccination campaigns were rolled out primarily to limit the impact of COVID-19 on severe health outcomes, including mortality.

AIM

We aimed to estimate the number of averted deaths by COVID-19 vaccination in the Belgian population aged 65 years and older, between January 2021 and January 2023.

METHODS

Nationwide data on COVID-19 infections, vaccine administrations and all-cause mortality were individually linked. We estimated Vaccine Effectiveness against COVID-19 mortality (VE) among persons having received a vaccine dose in the last 6 months, using a Cox proportional hazards model adjusted for age, sex, time since vaccination, previous infection, underlying health conditions, province and income. COVID-19 death was defined as a person with a laboratory-confirmed SARS-CoV-2 infection who died within a specified interval. Based on obtained VE estimates, vaccine coverage and national COVID-19 mortality data, we estimated the number of averted deaths.

RESULTS

We estimated VE (confidence interval (CI)) at 0-59 days after vaccination, for 65-79 year and ≥ 80 year-olds respectively, at 81.9 % (CI 78.1 %-85.1 %) and 74.7 % (CI 71.2 %-77.7 %) during Alpha, at 90.5 % (CI 88.8 %-91.9 %) and 91.4 % (CI 90.4 %-92.4 %) during Delta and at 84.0 % (CI 81.8 %-85.9 %) and 74.5 % (CI 72.4 %-76.5 %) during Omicron period. Among the Belgian population aged 65 years and older, we estimated 12,806 deaths averted (CI 11,633-13,982), representing a 54 % reduction (CI 51 %-56 %) in the expected deaths (without vaccination). During the Delta period COVID-19 deaths were reduced by 68 %, during Omicron by 54 % and during Alpha by 31 %.

DISCUSSION

Vaccinating against COVID-19 reduced deaths by 54 % among the Belgian population aged 65 years and older, underscoring the importance of COVID-19 vaccines in reducing mortality.

Spatio-temporal forecasting of COVID-19 cases in the Netherlands for source and contact tracing

Source and contact tracing (SCT) is a core public health measure that is used to contain the spread of infectious diseases. It aims to identify a source of infection, and to advise those who have been exposed to this source. Due to the rapid increases in incidence of COVID-19 in the Netherlands, the capacity to conduct a full SCT quickly became insufficient. Therefore, the public health services (PHS) might benefit from a restricted strategy targeted to geographical regions where (predicted) case-to-case transmission is high. In this study, we set out to develop a prediction model for the number of COVID-19 cases per postal code within the Netherlands using geographic and demographic features. The study population consists of individuals residing in one of the participating nine Dutch PHS regions who tested positive for SARS-CoV-2 between 1 June 2020 and 27 February 2021. Using a machine learning random forest regression model, we predicted the top 100 postal codes with the highest number of cases with an accuracy of 49% for the current week, 42% for next week, and 44% for two weeks from present. In addition, the age groups of 20-39 and 40-64 years had a higher prediction accuracy than groups outside these age ranges. The developed model provides a starting point for targeted preventive SCT efforts that incorporate geospatial and demographic characteristics of a neighbourhood. It should nonetheless be noted that during the early stages of the outbreak, the number of available datapoints needed to inform such models are likely insufficient. Given the accuracy and data requirements of the developed model, it is unlikely that this class of models can play a pivotal role in informing policy during the early phases of a future epidemic.

Seasonal, weekly, and individual variations in long-term use of wearable medical devices for diabetes management

Wearable medical-grade devices are transforming the standard of care for prevalent chronic conditions like diabetes. Yet, adoption and long-term use remain a challenge for many people. In this study, we investigate patterns of consistent versus disrupted use of continuous glucose monitors (CGMs) through analysis of more than 118,000 days of data, with over 22 million blood glucose samples, from 108 young adults with type 1 diabetes (average: 3 years of CGM data per person). In this population, we found more consistent CGM use at the start and end of the year (e.g., January, December), and more disrupted CGM use in the middle of the year/warmer months (i.e., May to July). We also found more consistent CGM use on weekdays (Monday to Thursday) and during waking hours (6AM - 6PM), but more disrupted CGM use on weekends (Friday to Sunday) and during evening/night hours (7PM - 5AM). Only 52.7% of participants (57 out of 108) had consistent and sustained CGM use over the years (i.e., over 70% daily wear time for more than 70% of their data duration). From semi-structured interviews, we unpack factors contributing to sustained CGM use (e.g., easier and better blood glucose management) and factors contributing to disrupted CGM use (e.g., changes in insurance coverage, issues with sensor adhesiveness/lifespan, and college/life transitions). We leverage insights from this study to elicit implications for next-generation technology and interventions that can circumvent seasonal and other factors that disrupt sustained use of wearable medical devices for the goal of improving health outcomes.

Discovering the climate dependent disease transmission mechanism through learning-explaining framework

There are evidence showing that meteorological factors, such as temperature and humidity, have critical effects on transmission of some infectious diseases, while quantifying the influence is challenging. In this study we develop a learning-explaining framework to discover the particular dependence of transmission mechanisms on meteorological factors based on multiple source data. The incidence rate based on the epidemic data and epidemic model is theoretically identified, and meanwhile the practical discovery of particular formula is feasible through deep neural networks (DNN), symbolic regression (SR) and sparse identification of nonlinear dynamics (SINDy). In particular, we initially learn the incidence rate in an SIRS model based on epidemic data, then use mechanism discovery methods to explore the possible explicit forms of the incidence rate, and consequently explore the possible relationship between transmission rate and meteorological factors. We finally use information criteria and a definition of evaluation score to make model selection, and hence suggest the optimal explicit formula. We illustrate the idea by derive the incidence rate and transmission rate of respiratory infectious diseases based on the case data on influenza-like illness (ILI) in Xi'an, Shaanxi Province of China and meteorological data from 1st January 2010 to 10th November 2016. The finding reveals that the influence of meteorological factors on transmission exhibits very strong nonlinearity, and modeling the effect should be of great care.

An exploration of modeling approaches for capturing seasonal transmission in stochastic epidemic models

Seasonal variations in the incidence of infectious diseases are a well-established phenomenon, driven by factors such as climate changes, social behaviors, and ecological interactions that influence host susceptibility and transmission rates. While seasonality plays a significant role in shaping epidemiological dynamics, it is often overlooked in both empirical and theoretical studies. Incorporating seasonal parameters into mathematical models of infectious diseases is crucial for accurately capturing disease dynamics, enhancing the predictive power of these models, and developing successful control strategies. In this paper, I highlight key modeling approaches for incorporating seasonality into disease transmission, including sinusoidal functions, periodic piecewise linear functions, Fourier series expansions, Gaussian functions, and data-driven methods. These approaches are evaluated in terms of their flexibility, complexity, and ability to capture distinct seasonal patterns observed in real-world epidemics. A comparative analysis showcases the relative strengths and limitations of each method, supported by real-world examples. Additionally, a stochastic Susceptible-Infected-Recovered (SIR) model with seasonal transmission is demonstrated through numerical simulations. Important outcome measures, such as the basic and instantaneous reproduction numbers and the probability of a disease outbreak derived from the branching process approximation of the Markov chain, are also presented to illustrate the impact of seasonality on disease dynamics.

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.

Did the COVID-19 quarantine policies applied in Cochabamba, Bolivia mitigated cases successfully? an interrupted time series analysis

BACKGROUND

The COVID-19 pandemic prompted varied policy responses globally, with Latin America facing unique challenges. A detailed examination of these policies' impacts on health systems is crucial, particularly in Bolivia, where information about policy implementation and outcomes is limited.

OBJECTIVE

To describe the COVID-19 testing trends and evaluate the effects of quarantine measures on these trends in Cochabamba, Bolivia.

METHODS

Utilizing COVID-19 testing data from the Cochabamba Department Health Service for the 2020-2022 period. Stratified testing rates in the health system sectors were first estimated followed by an interrupted time series analysis using a quasi-Poisson regression model for assessing the quarantine effects on the mitigation of cases during surge periods.

RESULTS

The public sector reported the larger percentage of tests (65%), followed by the private sector (23%) with almost double as many tests as the public-social security sector (11%). In the time series analysis, a correlation between the implementation of quarantine policies and a decrease in the slope of positive rates of COVID-19 cases was observed compared to periods without or with reduced quarantine policies.

CONCLUSION

This research underscores the local health system disparities and the effectiveness of stringent quarantine measures in curbing COVID-19 transmission in the Cochabamba region. The findings stress the importance of the measures' intensity and duration, providing valuable lessons for Bolivia and beyond. As the global community learns from the pandemic, these insights are critical for shaping resilient and effective health policy responses.

Environmental predictors of SARS-CoV-2 infection incidence in Catalonia (northwestern Mediterranean)

Numerous studies have explored whether and how the spread of the SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19), responds to environmental conditions without reaching consistent answers. Sociodemographic factors, such as variable population density and mobility, as well as the lack of effective epidemiological monitoring, make it difficult to establish robust correlations. Here we carry out a regional cross-correlation study between nine atmospheric variables and an infection index (Ic ) estimated from standardized positive polymerase chain reaction (PCR) test cases. The correlations and associated time-lags are used to build a linear multiple-regression model between weather conditions and the Ic index. Our results show that surface pressure and relative humidity can largely predict COVID-19 outbreaks during periods of relatively minor mobility and meeting restrictions. The occurrence of low-pressure systems, associated with the autumn onset, leads to weather and behavioral changes that intensify the virus transmission. These findings suggest that surface pressure and relative humidity are key environmental factors that may be used to forecast the spread of SARS-CoV-2.

Confounding amplifies the effect of environmental factors on COVID-19

The global COVID-19 pandemic has severely impacted human health and socioeconomic development, posing an enormous public health challenge. Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19. However, numerous factors influence the development of pandemic outbreaks, and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19. Direct estimation of the role of environmental factors without removing the confounding effects will be biased. To overcome this critical problem, we developed a Double Machine Learning (DML) causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities. Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors. Environmental factors are not the dominant cause of widespread outbreaks in China in 2022. In addition, by further analyzing the causal effects of environmental factors, it was verified that there is significant heterogeneity in the role of environmental factors. The causal effect of environmental factors on COVID-19 changes with the regional environment. It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics, confounding factors must be handled carefully in order to obtain clean quantitative results. This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic, as well as a framework for more accurately quantifying the factors influencing the outbreak.

Immunomics in one health: understanding the human, animal, and environmental aspects of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic underscores the critical need to integrate immunomics within the One Health framework to effectively address zoonotic diseases across humans, animals, and environments. Employing advanced high-throughput technologies, this interdisciplinary approach reveals the complex immunological interactions among these systems, enhancing our understanding of immune responses and yielding vital insights into the mechanisms that influence viral spread and host susceptibility. Significant advancements in immunomics have accelerated vaccine development, improved viral mutation tracking, and broadened our comprehension of immune pathways in zoonotic transmissions. This review highlights the role of animals, not merely as carriers or reservoirs, but as essential elements of ecological networks that profoundly influence viral epidemiology. Furthermore, we explore how environmental factors shape immune response patterns across species, influencing viral persistence and spillover risks. Moreover, case studies demonstrating the integration of immunogenomic data within the One Health framework for COVID-19 are discussed, outlining its implications for future research. However, linking humans, animals, and the environment through immunogenomics remains challenging, including the complex management of vast amounts of data and issues of scalability. Despite challenges, integrating immunomics data within the One Health framework significantly enhances our strategies and responses to zoonotic diseases and pandemic threats, marking a crucial direction for future public health breakthroughs.

Collective pronouns, collective health actions: Predicting pandemic precautionary measures through online first-person plural pronoun usage across U.S. states

The COVID-19 pandemic has underscored the role of group identification in shaping collective health behaviors. Using the novel Pronoun-Influenced Collective Health Model - an integrated framework combining elements from health and social psychology theories - we investigated the relationship between online first-person plural pronoun usage and adherence to COVID-19 preventive measures across the United States. Analyzing weekly Google Trends data on English (Study 1) and Spanish (Study 2) first-person pronoun searches, alongside data on adherence to pandemic precautionary measures from early 2020 to late 2022, we found significant positive associations between relative first-person plural pronoun search volumes and adherence to social distancing, stay-at-home orders, vaccination rates, and proactive disease prevention information seeking. These associations remained robust after adjusting for potential confounding factors. A mini meta-analysis (Study 3) confirmed the consistency of our findings, revealing no significant moderation effects by language context or ecological-socio-cultural factors, suggesting broad generalizability. The implications of this research highlight the potential for tracking online collective language as a valuable indicator of and proxy for societal-level health engagement during crises. This novel digital linguistics approach, synergistically combining applied health and social psychology with big data from digital platforms such as Google, offers powerful tools for monitoring collective health actions across linguistic and cultural boundaries during large-scale health crises.

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.

As air relative humidity increases, infectivity of SARS-CoV-2 decreases within water droplets

Water droplets containing the SARS-CoV-2 virus, responsible for coronavirus 2019 transmission, were introduced into a controlled-temperature and -humidity chamber. The SARS-CoV-2 virus with green fluorescent protein tag in droplets was used to infect Caco-2 cells, with viability assessed through flow cytometry and microscopic counting. Whereas temperature fluctuations within typical indoor ranges (20°C-30°C) had minimal impact, we observed a notable decrease in infection rate as the surrounding air's relative humidity increased. By investigating humidity levels between 20% and 70%, we identified a threshold of ≥40% relative humidity as most effective in diminishing SARS-CoV-2 infectivity. We also found that damage of the viral proteins under high relative humidity may be responsible for the decrease in their activity. This outcome supports previous research demonstrating a rise in the concentration of reactive oxygen species within water droplets with elevated relative humidity.

A dynamic ensemble model for short-term forecasting in pandemic situations

During the COVID-19 pandemic, many hospitals reached their capacity limits and could no longer guarantee treatment of all patients. At the same time, governments endeavored to take sensible measures to stop the spread of the virus while at the same time trying to keep the economy afloat. Many models extrapolating confirmed cases and hospitalization rate over short periods of time have been proposed, including several ones coming from the field of machine learning. However, the highly dynamic nature of the pandemic with rapidly introduced interventions and new circulating variants imposed non-trivial challenges for the generalizability of such models. In the context of this paper, we propose the use of ensemble models, which are allowed to change in their composition or weighting of base models over time and could thus better adapt to highly dynamic pandemic or epidemic situations. In that regard, we also explored the use of secondary metadata-Google searches-to inform the ensemble model. We tested our approach using surveillance data from COVID-19, Influenza, and hospital syndromic surveillance of severe acute respiratory infections (SARI). In general, we found ensembles to be more robust than the individual models. Altogether we see our work as a contribution to enhance the preparedness for future pandemic situations.

The ocean losing its breath under the heatwaves

The world's oceans are under threat from the prevalence of heatwaves caused by climate change. Despite this, there is a lack of understanding regarding their impact on seawater oxygen levels - a crucial element in sustaining biological survival. Here, we find that heatwaves can trigger low-oxygen extreme events, thereby amplifying the signal of deoxygenation. By utilizing in situ observations and state-of-the-art climate model simulations, we provide a global assessment of the relationship between the two types of extreme events in the surface ocean (0-10 m). Our results show compelling evidence of a remarkable surge in the co-occurrence of marine heatwaves and low-oxygen extreme events. Hotspots of these concurrent stressors are identified in the study, indicating that this intensification is more pronounced in high-biomass regions than in those with relatively low biomass. The rise in the compound events is primarily attributable to long-term warming primarily induced by anthropogenic forcing, in tandem with natural internal variability modulating their spatial distribution. Our findings suggest the ocean is losing its breath under the influence of heatwaves, potentially experiencing more severe damage than previously anticipated.

Looking beyond vaccines: Cultural tightness-looseness moderates the relationship between immunization coverage and disease prevention vigilance

Advancements in vaccination technologies mitigate disease transmission risks but may inadvertently suppress the behavioral immune system, an evolved disease avoidance mechanism. Applying behavioral immune system theory and utilizing robust big data analytics, we examined associations between rising vaccination coverage and government policies, public mobility, and online information seeking regarding disease precautions. We tested whether cultural tightness-looseness moderates the relationship between mass immunization and disease prevention vigilance. Comprehensive time series analyses were conducted using American data (Study 1) and international data (Study 2), employing transfer function modeling, cross-correlation function analysis, and meta-regression analysis. Across both the US and global analyses, as vaccination rates rose over time, government COVID-19 restrictions significantly relaxed, community mobility increased, and online searches for prevention information declined. The relationship between higher vaccination rates and lower disease prevention vigilance was stronger in culturally looser contexts. Results provide initial evidence that mass immunization may be associated with attenuated sensitivity and enhanced flexibility of disease avoidance psychology and actions. However, cultural tightness-looseness significantly moderates this relationship, with tighter cultures displaying sustained vigilance amidst immunization upticks. These findings offer valuable perspectives to inform nuanced policymaking and public health strategies that balance prudent precautions against undue alarm when expanding vaccine coverage worldwide.

A Dynamic Prognostic Model for Identifying Vulnerable COVID-19 Patients at High Risk of Rapid Deterioration

PURPOSE

We aimed to validate and, if performance was unsatisfactory, update the previously published prognostic model to predict clinical deterioration in patients hospitalized for COVID-19, using data following vaccine availability.

METHODS

Using electronic health records of patients ≥18 years, with laboratory-confirmed COVID-19, from a large care-delivery network in Massachusetts, USA, from March 2020 to November 2021, we tested the performance of the previously developed prediction model and updated the prediction model by incorporating data after availability of COVID-19 vaccines. We randomly divided data into development (70%) and validation (30%) cohorts. We built a model predicting worsening in a published severity scale in 24 h by LASSO regression and evaluated performance by c-statistic and Brier score.

RESULTS

Our study cohort consisted of 8185 patients (Development: 5730 patients [mean age: 62; 44% female] and Validation: 2455 patients [mean age: 62; 45% female]). The previously published model had suboptimal performance using data after November 2020 (N = 4973, c-statistic = 0.60. Brier score = 0.11). After retraining with the new data, the updated model included 38 predictors including 18 changing biomarkers. Patients hospitalized after Jun 1st, 2021 (when COVID-19 vaccines became widely available in Massachusetts) were younger and had fewer comorbidities than those hospitalized before. The c-statistic and Brier score were 0.77 and 0.13 in the development cohort, and 0.73 and 0.14 in the validation cohort.

CONCLUSION

The characteristics of patients hospitalized for COVID-19 differed substantially over time. We developed a new dynamic model for rapid progression with satisfactory performance in the validation set.

Investigating seasonal changes in factors associated with COVID-19 concerns: Results from a serial cross-sectional survey study in Germany between 2020 and 2023

Objective

COVID-19 risk perceptions are discussed to be volatile and have been shown to be connected to the adoption of preventive public health behaviors. This study aimed to investigate changes in COVID-19 concerns and influencing factors as a function of season among the German public.

Methods

Sixty-three waves of cross-sectional telephone surveys with German participants aged 14 years and older conducted at least monthly between June 2020 and April 2023 provided the data basis (N = 63,471). After pooling participants of different waves by season (spring, summer, fall, winter), data were analyzed with regard to changes in physical health, mental health, economic, and social COVID-19 concerns. Individual characteristics (e.g., age), COVID-19 behavior (e.g., hygiene practices), and related perceptions (e.g., controllability of risk) were considered as predictors of composite concerns in different seasons.

Results

Results showed a higher between-seasons than within-seasons variability in concerns, with rises in physical and mental health and social concerns during fall. Multivariate regressions revealed being female, lower education, adopting protective measures, and higher perceived probability of infection in both public and private settings to be consistent predictors of higher COVID-19 concerns. Coefficients of these predictors remained comparatively stable over seasons and years.

Conclusion

Results indicate re-occurring changes in concerns during a prolonged crisis, with distinct characteristics being consistently associated with higher reported concerns. To ensure the application of protective measures, communicators should consider that risk perceptions are subject to fluctuations, but that certain groups of individuals tend to develop them and therefore deserve particular focus.

Pooling of samples to optimise SARS-CoV-2 detection in nasopharyngeal swabs and gargle lavage self-samples for covid-19 diagnostics and surveillance

BACKGROUND

Testing of pooled samples is an effective strategy for increasing testing capacity while saving resources and time. This study aimed to validate pooled testing and gather real-life data on its use for Covid-19 surveillance with a gargle lavage (GL) self-sampling strategy.

METHODS

Two-stage pooled testing with pools of 6 and 12 samples was used for preventive testing of an asymptomatic population and Covid-19 surveillance in Czech schools. Both GL and nasopharyngeal swabs were used for sampling.

RESULTS

In total, 61,111 samples were tested. The use of pooled testing for large-scale Covid-19 surveillance reduced consumable costs by almost 75% and increased testing capacity up to 3.8-fold compared to standard methods. RT-PCR experiments revealed a minimal loss of sensitivity (0-2.2%) when using pooled samples, enabling the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genes with Ct values >35. The minor loss of sensitivity was counterbalanced by a significantly increased throughput and the ability to substantially increase testing frequencies.

CONCLUSIONS

Pooled testing is considerably more cost-effective and less time-consuming than standard testing for large-scale Covid-19 surveillance even when the prevalence of SARS-CoV-2 is fluctuating. Gargle lavage self-sampling is a non-invasive technique suitable for sample collection without a healthcare worker's assistance.

The effect of mobility reductions on infection growth is quadratic in many cases

Stay-at-home orders were introduced in many countries during the COVID-19 pandemic, limiting the time people spent outside their home and the attendance of gatherings. In this study, we argue from a theoretical model that in many cases the effect of such stay-at-home orders on incidence growth should be quadratic, and that this statement should also hold beyond COVID-19. That is, a reduction of the out-of-home duration to, say, 70% of its original value should reduce incidence growth and thus the effective R-value to 70 % · 70 % = 49 % of its original value. We then show that this hypothesis can be substantiated from data acquired during the COVID-19 pandemic by using a multiple regression model to fit a combination of the quadratic out-of-home duration and temperature to the COVID-19 growth multiplier. We finally demonstrate that many other models, when brought to the same scale, give similar reductions of the effective R-value, but that none of these models extend plausibly to an out-of-home duration of zero.

Distribution of SARS-CoV-2 Genomes in Wastewaters and the Associated Potential Infection Risk for Plant Workers in Typical Urban and Peri-Urban Communities of the Buffalo City Region, South Africa

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater has been reported in several studies and similar research can be used as a proxy for an early warning of potential Coronavirus disease 2019 (COVID-19) outbreaks. This study focused on profiling the incidence of SARS-CoV-2 genomes in wastewater samples obtained from facilities located in the Buffalo City Municipality. Raw samples were collected weekly using the grab technique for a period of 48 weeks. Ribonucleic acids were extracted from the samples, using the QIAGEN Powersoil Total RNA Extraction kit, and extracted RNA samples were further profiled for the presence of SARS-CoV-2 genomes using Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) technique. Furthermore, various environmental matrices were utilized to estimate the potential health risk to plant operators associated with exposure to SARS-CoV-2 viral particles using the quantitative microbiological risk assessment (QMRA) model. Our findings revealed the prevalence of SARS-CoV-2 genomes with concentrations that ranged from 0.22 × 103 to 17.60 × 103 genome copies per milliliter (GC/mL). Different exposure scenarios were employed for the QMRA model, and the findings indicate a probability of infection (P(i)) ranging from 0.93% to 37.81% across the study sites. Similarly, the P(i) was highly significant (p < 0.001) for the 20 mL volumetric intake as compared to other volumetric intake scenarios, and high P(i) was also observed in spring, autumn, and winter for all WWTPs. The P(i) was significantly different (p < 0.05) with respect to the different seasons and with respect to different volume scenarios.

Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions

In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Evaluating Seasonal Variations in Human Contact Patterns and Their Impact on the Transmission of Respiratory Infectious Diseases

BACKGROUND

Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified.

METHODS

We investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period.

RESULTS

We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%).

CONCLUSIONS

Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.

Development of the second version of Global Prediction System for Epidemiological Pandemic

Coronavirus disease 2019 (COVID-19) is a severe global public health emergency that has caused a major crisis in the safety of human life, health, global economy, and social order. Moreover, COVID-19 poses significant challenges to healthcare systems worldwide. The prediction and early warning of infectious diseases on a global scale are the premise and basis for countries to jointly fight epidemics. However, because of the complexity of epidemics, predicting infectious diseases on a global scale faces significant challenges. In this study, we developed the second version of Global Prediction System for Epidemiological Pandemic (GPEP-2), which combines statistical methods with a modified epidemiological model. The GPEP-2 introduces various parameterization schemes for both impacts of natural factors (seasonal variations in weather and environmental impacts) and human social behaviors (government control and isolation, personnel gathered, indoor propagation, virus mutation, and vaccination). The GPEP-2 successfully predicted the COVID-19 pandemic in over 180 countries with an average accuracy rate of 82.7%. It also provided prediction and decision-making bases for several regional-scale COVID-19 pandemic outbreaks in China, with an average accuracy rate of 89.3%. Results showed that both anthropogenic and natural factors can affect virus spread and control measures in the early stages of an epidemic can effectively control the spread. The predicted results could serve as a reference for public health planning and policymaking.

SARS-CoV-2 Test-to-Stay in Daycare

BACKGROUND AND OBJECTIVES

Test-to-stay concepts apply serial testing of children in daycare after exposure to SARS-CoV-2 without use of quarantine. This study aims to assess the safety of a test-to-stay screening in daycare facilities.

METHODS

714 daycare facilities and approximately 50 000 children ≤6 years in Cologne, Germany participated in a SARS-CoV-2 Pool-polymerase chain reaction (PCR) screening from March 2021 to April 2022. The screening initially comprised post-exposure quarantine and was adapted to a test-to-stay approach during its course. To assess safety of the test-to-stay approach, we explored potential changes in frequencies of infections among children after the adaptation to the test-to-stay approach by applying regression discontinuity in time (RDiT) analyses. To this end, PCR-test data were linked with routinely collected data on reported infections in children and analyzed using ordinary least squares regressions.

RESULTS

219 885 Pool-PCRs and 352 305 Single-PCRs were performed. 6440 (2.93%) Pool-PCRs tested positive, and 17 208 infections in children were reported. We estimated that during a period of 30 weeks, the test-to-stay concept avoided between 7 and 20 days of quarantine per eligible daycare child. RDiT revealed a 26% reduction (Exp. Coef: 0.74, confidence interval 0.52-1.06) in infection frequency among children and indicated no significant increase attributable to the test-to-stay approach. This result was not sensitive to adjustments for 7-day incidence, season, SARS-CoV-2 variant, and socioeconomic status.

CONCLUSIONS

Our analyses provide evidence that suggest safety of the test-to-stay approach compared with quarantine measures. This approach offers a promising option to avoid use of quarantine after exposure to respiratory pathogens in daycare settings.

Wastewater-Based Epidemiology for Viral Surveillance from an Endemic Perspective: Evidence and Challenges

Wastewater-based epidemiology (WBE) is currently used to monitor not only the spread of the viral SARS-CoV-2 pandemic but also that of other viruses in endemic conditions, particularly in the absence of syndromic surveillance. The continuous monitoring of sewage requires high expenditure and significant time investments, highlighting the need for standardized methods and structured monitoring strategies. In this context, we conducted weekly wastewater monitoring in northwestern Tuscany (Italy) and targeted human adenovirus (HAdV), norovirus genogroup II (NoVggII), enterovirus (EV), and SARS-CoV-2. Samples were collected at the entrances of treatment plants and concentrated using PEG/NaCl precipitation, and viral nucleic acids were extracted and detected through real-time reverse transcription qPCR. NoVggII was the most identified target (84.4%), followed by HAdV, SARS-CoV-2, and EV. Only HAdV and EV exhibited seasonal peaks in spring and summer. Compared with data that were previously collected in the same study area (from February 2021 to September 2021), the results for SARS-CoV-2 revealed a shift from an epidemic to an endemic pattern, at least in the region under investigation, which was likely due to viral mutations that led to the spreading of new variants with increased resistance to summer environmental conditions. In conclusion, using standardized methods and an efficient monitoring strategy, WBE proves valuable for viral surveillance in pandemic and epidemic scenarios, enabling the identification of temporal-local distribution patterns that are useful for making informed public health decisions.

Seasonal variation of Covid-19 incidence and role of land surface and air temperatures: a case study in the west of Iran

.In this study, we assessed the impact of satellite-based Land Surface Temperature (LST) and Air Temperature (AT) on covid-19. First, we spatio-temporally kriged the LST and applied bias correction. The epidemic shape, timing, and size were compared after and before adjusting for the predictors. Given the non-linear behavior of a pandemic, a semi-parametric regression model was used. In addition, the interaction effect between the predictors and season was assessed. Before adjusting for the predictors, the peak happened at the end of hot season. After adjusting, it was attenuated and slightly moved forward. Moreover, the Attributable Fraction (AF) and Peak to Trough Relative (PTR) were % 23 (95% CI; 15, 32) and 1.62 (95%CI; 1.34, 1.97), respectively. We found that temperature might have changed the seasonal variation of covid-19. However, given the large uncertainty after adjusting for the variables, it was hard to provide conclusive evidence in the region we studied.

Indoor air humidity revisited: Impact on acute symptoms, work productivity, and risk of influenza and COVID-19 infection

Recent epidemiological and experimental findings reconfirm that low indoor air humidity (dry air) increases the prevalence of acute eye and airway symptoms in offices, result in lower mucociliary clearance in the airways, less efficient immune defense, and deteriorate the work productivity. New epidemiological and experimental research also support that the environmental conditions for the risk of infection of influenza and COVID-19 virus is lowest in the Goldilocks zone of 40-60% relative humidity (RH) by decrease of the airways' susceptibility, which can be elevated by particle exposure. Furthermore, low RH increases the generation of infectious virus laden aerosols exhaled from infected people. In general, elevation of the indoor air humidity from dry air increases the health of the airways concomitantly with lower viability of infectious virus. Thus, the negative effects of ventilation with dry outdoor air (low absolute air humidity) should be assessed according to 1) weakened health and functionality of the airways, 2) increased viability and possible increased transmissibility of infectious virus, and 3) evaporation of virus containing droplets to dry out to droplet nuclei (also possible at high room temperature), which increases their floating time in the indoor air. The removal of acid-containing ambient aerosols from the indoor air by filtration increases pH, viability of infectious viruses, and the risk of infection, which synergistically may further increase by particle exposure. Thus, the dilution of indoor air pollutants and virus aerosols by dry outdoor air ventilation should be assessed and compared with the beneficial health effects by control of the center zone of 40-60% RH, an essential factor for optimal functionality of the airways, and with the additional positive impact on acute symptoms, work productivity, and reduced risk of infection.

Results of the Cologne Corona Surveillance (CoCoS) project- a cross-sectional study: survey data on risk factors of SARS-CoV-2 infection, and moderate-to-severe course in primarily immunized adults

BACKGROUND

Amidst the COVID-19 pandemic, vaccination has been a crucial strategy for mitigating transmission and disease severity. However, vaccine-effectiveness may be influenced by various factors, including booster vaccination, as well as personal factors such as age, sex, BMI, smoking, and comorbidities. To investigate the potential effects of these factors on SARS-CoV-2 infection and disease severity, we analyzed data from the third round of the Cologne Corona Surveillance (CoCoS) project, a large cross-sectional survey.

METHODS

The study was conducted mid-February to mid-March 2022 in Cologne, Germany. A random sample of 10,000 residents aged 18 years and older were invited to participate in an online survey. Information on participants' demographics (age, sex), SARS-CoV-2 infections, vaccination status, smoking, and preexisting medical conditions were collected. The outcomes of the study were: (1) the occurrence of SARS-CoV-2 infection despite vaccination (breakthrough infection) and (2) the occurrence of moderate-to-severe disease as a result of a breakthrough infection. Cox proportional-hazards regression was used to investigate possible associations between the presence/absence of booster vaccination, personal factors and the occurrence of SARS-CoV-2 infection. Associations with moderate-to-severe infection were analyzed using the Fine and Gray subdistribution hazard model.

RESULTS

A sample of 2,991 residents responded to the questionnaire. A total of 2,623 primary immunized participants were included in the analysis of breakthrough infection and 2,618 in the analysis of SARS-CoV-2 infection severity after exclusions due to incomplete data. The multivariable results show that booster vaccination (HR = 0.613, 95%CI 0.415-0.823) and older age (HR = 0.974, 95%CI 0.966-0.981) were associated with a reduced hazard of breakthrough infection. Regarding the severity of breakthrough infection, older age was associated with a lower risk of moderate-to-severe breakthrough infection (HR = 0.962, 95%CI0.949-0.977). Female sex (HR = 2.570, 95%CI1.435-4.603), smoking (HR = 1.965, 95%CI1.147-3.367) and the presence of chronic lung disease (HR = 2.826, 95%CI1.465-5.450) were associated with an increased hazard of moderate-to-severe breakthrough infection.

CONCLUSION

The results provide a first indication of which factors may be associated with SARS-CoV-2 breakthrough infection and moderate-to-severe course of infection despite vaccination. However, the retrospective nature of the study and risk of bias in the reporting of breakthrough infection severity limit the strength of the results.

TRIAL REGISTRATION

DRKS.de, German Clinical Trials Register (DRKS), Identifier: DRKS00024046, Registered on 25 February 2021.

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

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

Reduction of acute respiratory infections in day-care by non-pharmaceutical interventions: a narrative review

Objective

Children who start in day-care have 2-4 times as many respiratory infections compared to children who are cared for at home, and day-care staff are among the employees with the highest absenteeism. The extensive new knowledge that has been generated in the COVID-19 era should be used in the prevention measures we prioritize. The purpose of this narrative review is to answer the questions: Which respiratory viruses are the most significant in day-care centers and similar indoor environments? What do we know about the transmission route of these viruses? What evidence is there for the effectiveness of different non-pharmaceutical prevention measures?

Design

Literature searches with different terms related to respiratory infections in humans, mitigation strategies, viral transmission mechanisms, and with special focus on day-care, kindergarten or child nurseries, were conducted in PubMed database and Web of Science. Searches with each of the main viruses in combination with transmission, infectivity, and infectious spread were conducted separately supplemented through the references of articles that were retrieved.

Results

Five viruses were found to be responsible for ≈95% of respiratory infections: rhinovirus, (RV), influenza virus (IV), respiratory syncytial virus (RSV), coronavirus (CoV), and adenovirus (AdV). Novel research, emerged during the COVID-19 pandemic, suggests that most respiratory viruses are primarily transmitted in an airborne manner carried by aerosols (microdroplets).

Conclusion

Since airborne transmission is dominant for the most common respiratory viruses, the most important preventive measures consist of better indoor air quality that reduces viral concentrations and viability by appropriate ventilation strategies. Furthermore, control of the relative humidity and temperature, which ensures optimal respiratory functionality and, together with low resident density (or mask use) and increased time outdoors, can reduce the occurrence of respiratory infections.

Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage

In this study, we developed a negative binomial regression model for one-week ahead spatio-temporal predictions of the number of COVID-19 hospitalizations in Uppsala County, Sweden. Our model utilized weekly aggregated data on testing, vaccination, and calls to the national healthcare hotline. Variable importance analysis revealed that calls to the national healthcare hotline were the most important contributor to prediction performance when predicting COVID-19 hospitalizations. Our results support the importance of early testing, systematic registration of test results, and the value of healthcare hotline data in predicting hospitalizations. The proposed models may be applied to studies modeling hospitalizations of other viral respiratory infections in space and time assuming count data are overdispersed. Our suggested variable importance analysis enables the calculation of the effects on the predictive performance of each covariate. This can inform decisions about which types of data should be prioritized, thereby facilitating the allocation of healthcare resources.

The impact of urban spatial environment on COVID-19: a case study in Beijing

Epidemics are dangerous and difficult to prevent and control, especially in urban areas. Clarifying the correlation between the COVID-19 Outbreak Frequency and the urban spatial environment may help improve cities' ability to respond to such public health emergencies. In this study, we firstly analyzed the spatial distribution characteristics of COVID-19 Outbreak Frequency by correlating the geographic locations of COVID-19 epidemic-affected neighborhoods in the city of Beijing with the time point of onset. Secondly, we created a geographically weighted regression model combining the COVID-19 Outbreak Frequency with the external spatial environmental elements of the city. Thirdly, different grades of epidemic-affected neighborhoods in the study area were classified according to the clustering analysis results. Finally, the correlation between the COVID-19 Outbreak Frequency and the internal spatial environmental elements of different grades of neighborhoods was investigated using a binomial logistic regression model. The study yielded the following results. (i) Epidemic outbreak frequency was evidently correlated with the urban external spatial environment, among building density, volume ratio, density of commercial facilities, density of service facilities, and density of transportation facilities were positively correlated with COVID-19 Outbreak Frequency, while water and greenery coverage was negatively correlated with it. (ii) The correlation between COVID-19 Outbreak Frequency and the internal spatial environmental elements of neighborhoods of different grades differed. House price and the number of households were positively correlated with the COVID-19 Outbreak Frequency in low-end neighborhoods, while the number of households was positively correlated with the COVID-19 Outbreak Frequency in mid-end neighborhoods. In order to achieve spatial justice, society should strive to address the inequality phenomena of income gaps and residential differentiation, and promote fair distribution of spatial environments.

Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe

BACKGROUND

Most government efforts to control the COVID-19 pandemic revolved around non-pharmaceutical interventions (NPIs) and vaccination. However, many respiratory diseases show distinctive seasonal trends. In this manuscript, we examined the contribution of these three factors to the progression of the COVID-19 pandemic.

METHODS

Pearson correlation coefficients and time-lagged analysis were used to examine the relationship between NPIs, vaccinations and seasonality (using the average incidence of endemic human beta-coronaviruses in Sweden over a 10-year period as a proxy) and the progression of the COVID-19 pandemic as tracked by deaths; cases; hospitalisations; intensive care unit occupancy and testing positivity rates in six Northern European countries (population 99.12 million) using a population-based, observational, ecological study method.

FINDINGS

The waves of the pandemic correlated well with the seasonality of human beta-coronaviruses (HCoV-OC43 and HCoV-HKU1). In contrast, we could not find clear or consistent evidence that the stringency of NPIs or vaccination reduced the progression of the pandemic. However, these results are correlations and not causations.

IMPLICATIONS

We hypothesise that the apparent influence of NPIs and vaccines might instead be an effect of coronavirus seasonality. We suggest that policymakers consider these results when assessing policy options for future pandemics.

LIMITATIONS

The study is limited to six temperate Northern European countries with spatial and temporal variations in metrics used to track the progression of the COVID-19 pandemic. Caution should be exercised when extrapolating these findings.

The impact of anthropogenic climate change on pediatric viral diseases

The adverse effects of climate change on human health are unfolding in real time. Environmental fragmentation is amplifying spillover of viruses from wildlife to humans. Increasing temperatures are expanding mosquito and tick habitats, introducing vector-borne viruses into immunologically susceptible populations. More frequent flooding is spreading water-borne viral pathogens, while prolonged droughts reduce regional capacity to prevent and respond to disease outbreaks with adequate water, sanitation, and hygiene resources. Worsening air quality and altered transmission seasons due to an increasingly volatile climate may exacerbate the impacts of respiratory viruses. Furthermore, both extreme weather events and long-term climate variation are causing the destruction of health systems and large-scale migrations, reshaping health care delivery in the face of an evolving global burden of viral disease. Because of their immunological immaturity, differences in physiology (e.g., size), dependence on caregivers, and behavioral traits, children are particularly vulnerable to climate change. This investigation into the unique pediatric viral threats posed by an increasingly inhospitable world elucidates potential avenues of targeted programming and uncovers future research questions to effect equitable, actionable change. IMPACT: A review of the effects of climate change on viral threats to pediatric health, including zoonotic, vector-borne, water-borne, and respiratory viruses, as well as distal threats related to climate-induced migration and health systems. A unique focus on viruses offers a more in-depth look at the effect of climate change on vector competence, viral particle survival, co-morbidities, and host behavior. An examination of children as a particularly vulnerable population provokes programming tailored to their unique set of vulnerabilities and encourages reflection on equitable climate adaptation frameworks.

The effects of meteorological factors on the COVID-19 omicron variant in Bangladesh

The COVID-19 omicron variant is exceptionally complicated and uncertain due to its rapid transmission and volume of infections. This study examines the impact of climatic factors on daily confirmed cases of COVID-19 omicron variant in Bangladesh. The secondary data of daily confirmed cases from 1 January 2022, to 31 March 2022, of eight distinct geographic divisions have been used for the current study. The multivariate generalized linear negative binomial regression model was applied to determine the effects of climatic factors on omicron transmission. The model revealed that the maximum temperature (Odds: 0.67, p < 0.05), sky clearness (Odds: 0.05, p < 0.05), wind speed (Odds: 0.76, p < 0.05), relative humidity (Odds: 1.02, p < 0.05), and air pressure (Odds: 0.27, p < 0.05) significantly impacted COVID-19 omicron transmission in Bangladesh. The study's findings can assist the concerned authorities and decision-makers take necessary measures to control the spread of omicron cases in Bangladesh.

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.

Impact of human mobility on the epidemic spread during holidays

COVID-19 has posed formidable challenges as a significant global health crisis. Its complexity stems from factors like viral contagiousness, population density, social behaviors, governmental regulations, and environmental conditions, with interpersonal interactions and large-scale activities being particularly pivotal. To unravel these complexities, we used a modified SEIR epidemiological model to simulate various outbreak scenarios during the holiday season, incorporating both inter-regional and intra-regional human mobility effects into the parameterization scheme. In addition, evaluation metrics were used to evaluate the accuracy of the model simulation by comparing the congruence between simulated results and recorded confirmed cases. The findings suggested that intra-city mobility led to an average surge of 57.35% in confirmed cases of China, while inter-city mobility contributed to an average increase of 15.18%. In the simulation for Tianjin, China, a one-week delay in human mobility attenuated the peak number of cases by 34.47% and postponed the peak time by 6 days. The simulation for the United States revealed that human mobility played a more pronounced part in the outbreak, with a notable disparity in peak cases when mobility was considered. This study highlights that while inter-regional mobility acted as a trigger for the epidemic spread, the diffusion effect of intra-regional mobility was primarily responsible for the outbreak. We have a better understanding on how human mobility and infectious disease epidemics interact, and provide empirical evidence that could contribute to disease prevention and control measures.

An overview of COVID-19 global epidemiology and discussion of potential drivers of variable global pandemic impacts

With a WHO-estimated excess mortality burden of 14.9 million over the course of 2020 and 2021, the COVID-19 pandemic has had a major human impact so far. It has also affected a range of disciplines, systems and practices from mathematical modelling to behavioural sciences, pharmaceutical development to health system management. This article explores these developments and, to set the scene, this paper summarizes the global epidemiology of COVID-19 from January 2020 to June 2021 and considers some potential drivers of variation.

Long-term spatiotemporal analysis of the climate related impact on the transmission rate of COVID-19

BACKGROUND

The association between weather conditions and the spread of COVID-19 was demonstrated by previous studies but focused on specific countries or investigated shorter periods of duration limiting the interpretation of the results.

AIM

To make an international comprehensive insight into the association between the weather conditions and the spread of COVID-19 by spanning many regions in the Northern and Southern hemispheres over a period of two years for the COVID-19 Outbreak.

METHODS

The data were analyzed by using statistical description, linear and multiple regressions, and the Spearman rank correlation test. Daily and weekly COVID-19 cases, the average temperatures, Wind Speed, the amount of precipitation as well as the relative humidity rates were collected from Irbid, Jordan as the main location of analyses, as well as comparison cities and countries in both hemispheres.

RESULTS

we found that certain climate variables are significant factors in determining the transmission rate of COVID-19 worldwide. Where, The temperature in the northern hemisphere regions was the most important climate factor that affects the increase in the transmission rate of COVID-19 (Northern Hemisphere rs = -0.65; Irbid rs = -0.74995; P < 0.001), While in southern hemisphere, the climate factor that affects the increase in the transmission rate of COVID-19 was the humidity (rs = 0.55; P < 0.01), In addition, we found the negligible and oscillated effect of wind speed on the transmission rate of COVID-19 worldwide. Moreover, we found that in Irbid 82% of COVID-19 cases were in the fall and winter seasons, while in summer the percentage of COVID-19 cases didn't exceed 3% during the total study period.

CONCLUSION

This study can help develop international strategies and policies against COVID-19-related pandemic peaks, especially during the colder seasons in the Northern Hemisphere regions from the first month of fall to the last month of winter.

Post-pandemic modeling of COVID-19: Waning immunity determines recurrence frequency

There are many factors in the current phase of the COVID-19 pandemic that signal the need for new modeling ideas. In fact, most traditional infectious disease models do not address adequately the waning immunity, in particular as new emerging variants have been able to break the immune shield acquired either by previous infection by a different strain of the virus, or by inoculation of vaccines not effective for the current variant. Furthermore, in a post-pandemic landscape in which reporting is no longer a default, it is impossible to have reliable quantitative data at the population level. Our contribution to COVID-19 post-pandemic modeling is a simple mathematical predictive model along the age-distributed population framework, that can take into account the waning immunity in a transparent and easily controllable manner. Numerical simulations show that under static conditions, the model produces periodic solutions that are qualitatively similar to the reported data, with the period determined by the immunity waning profile. Evidence from the mathematical model indicates that the immunity dynamics is the main factor in the recurrence of infection spikes, however, irregular perturbation of the transmission rate, due to either mutations of the pathogen or human behavior, may result in suppression of recurrent spikes, and irregular time intervals between consecutive peaks. The spike amplitudes are sensitive to the transmission rate and vaccination strategies, but also to the skewness of the profile describing the waning immunity, suggesting that these factors should be taken into consideration when making predictions about future outbreaks.

Analysing role of airborne particulate matter in abetting SARS-CoV-2 outbreak for scheming regional pandemic regulatory modalities

The mutating SARS-CoV-2 necessitates gauging the role of airborne particulate matter in the COVID-19 outbreak for designing area-specific regulation modalities based on the environmental state-of-affair. To scheme the protocols, the hotspots of air pollutants such as PM2.5, PM10, NH3, NO, NO2, SO2, and and environmental factors including relative humidity (RH), and temperature, along with COVID-19 cases and mortality from January 2020 till December 2020 from 29 different ground monitoring stations spanning Delhi, are mapped. Spearman correlation coefficients show a positive relationship between SARS-COV-2 with particulate matter (PM2.5 with r > 0.36 and PM10 with r > 0.31 and p-value <0·001). Besides, SARS-COV-2 transmission showed a substantial correlation with NH3 (r = 0.41), NO2 (r = 0.36), and NO (r = 0.35) with a p-value <0.001, which is highly indicative of their role in SARS-CoV-2 transmission. These outcomes are associated with the source of PM and its constituent trace elements to understand their overtone with COVID-19. This strongly validates temporal and spatial variation in COVID-19 dependence on air pollutants as well as on environmental factors. Besides, the bottlenecks of missing latent data, monotonous dependence of variables, and the role air pollutants with secondary environmental variables are discussed. The analysis set the foundation for strategizing regional-based modalities considering environmental variables (i.e., pollutant concentration, relative humidity, temperature) as well as urban and transportation planning for efficient control and handling of future public health emergencies.

Linkage between Airborne Particulate Matter and Viral Pandemic COVID-19 in Bucharest

The long-distance spreading and transport of airborne particulate matter (PM) of biogenic or chemical compounds, which are thought to be possible carriers of SARS-CoV-2 virions, can have a negative impact on the incidence and severity of COVID-19 viral disease. Considering the total Aerosol Optical Depth at 550 nm (AOD) as an atmospheric aerosol loading variable, inhalable fine PM with a diameter ≤2.5 µm (PM2.5) or coarse PM with a diameter ≤10 µm (PM10) during 26 February 2020-31 March 2022, and COVID-19's five waves in Romania, the current study investigates the impact of outdoor PM on the COVID-19 pandemic in Bucharest city. Through descriptive statistics analysis applied to average daily time series in situ and satellite data of PM2.5, PM10, and climate parameters, this study found decreased trends of PM2.5 and PM10 concentrations of 24.58% and 18.9%, respectively compared to the pre-pandemic period (2015-2019). Exposure to high levels of PM2.5 and PM10 particles was positively correlated with COVID-19 incidence and mortality. The derived average PM2.5/PM10 ratios during the entire pandemic period are relatively low (<0.44), indicating a dominance of coarse traffic-related particles' fraction. Significant reductions of the averaged AOD levels over Bucharest were recorded during the first and third waves of COVID-19 pandemic and their associated lockdowns (~28.2% and ~16.4%, respectively) compared to pre-pandemic period (2015-2019) average AOD levels. The findings of this research are important for decision-makers implementing COVID-19 safety controls and health measures during viral infections.

mRNA-1273 bivalent (original and Omicron) COVID-19 vaccine effectiveness against COVID-19 outcomes in the United States

The bivalent (original and Omicron BA.4/BA.5) mRNA-1273 COVID-19 vaccine was authorized to offer broader protection against COVID-19. We conducted a matched cohort study to evaluate the effectiveness of the bivalent vaccine in preventing hospitalization for COVID-19 (primary outcome) and medically attended SARS-CoV-2 infection and hospital death (secondary outcomes). Compared to individuals who did not receive bivalent mRNA vaccination but received ≥2 doses of any monovalent mRNA vaccine, the relative vaccine effectiveness (rVE) against hospitalization for COVID-19 was 70.3% (95% confidence interval, 64.0%-75.4%). rVE was consistent across subgroups and not modified by time since last monovalent dose or number of monovalent doses received. Protection was durable ≥3 months after the bivalent booster. rVE against SARS-CoV-2 infection requiring emergency department/urgent care and against COVID-19 hospital death was 55.0% (50.8%-58.8%) and 82.7% (63.7%-91.7%), respectively. The mRNA-1273 bivalent booster provides additional protection against hospitalization for COVID-19, medically attended SARS-CoV-2 infection, and COVID-19 hospital death.

The Effect of Select SARS-CoV-2 N-Linked Glycan and Variant of Concern Spike Protein Mutations on C-Type Lectin-Receptor-Mediated Infection

The SARS-CoV-2 virion has shown remarkable resilience, capable of mutating to escape immune detection and re-establishing infectious capabilities despite new vaccine rollouts. Therefore, there is a critical need to identify relatively immutable epitopes on the SARS-CoV-2 virion that are resistant to future mutations the virus may accumulate. While hACE2 has been identified as the receptor that mediates SARS-CoV-2 susceptibility, it is only modestly expressed in lung tissue. C-type lectin receptors like DC-SIGN can act as attachment sites to enhance SARS-CoV-2 infection of cells with moderate or low hACE2 expression. We developed an easy-to-implement assay system that allows for the testing of SARS-CoV-2 trans-infection. Using our assay, we assessed how SARS-CoV-2 Spike S1-domain glycans and spike proteins from different strains affected the ability of pseudotyped lentivirions to undergo DC-SIGN-mediated trans-infection. Through our experiments with seven glycan point mutants, two glycan cluster mutants and four strains of SARS-CoV-2 spike, we found that glycans N17 and N122 appear to have significant roles in maintaining COVID-19's infectious capabilities. We further found that the virus cannot retain infectivity upon the loss of multiple glycosylation sites, and that Omicron BA.2 pseudovirions may have an increased ability to bind to other non-lectin receptor proteins on the surface of cells. Taken together, our work opens the door to the development of new therapeutics that can target overlooked epitopes of the SARS-CoV-2 virion to prevent C-type lectin-receptor-mediated trans-infection in lung tissue.

The complex interplay between weather, social activity, and COVID-19 in the US

Empirical studies on the impact of weather and policy interventions on Covid-19 infections have dedicated little attention to the mediation role of social activity. In this study, we combine mobile locations, weather, and COVID-19 data in a two-way fixed effects mediation model to estimate the impact of weather and policy interventions on the COVID-19 infection rate in the US before the availability of vaccines, disentangling their direct impact from the part of the effect that is mediated by the endogenous response of social activity. We show that, while temperature reduces viral infectiousness, it also increases the amount of time individuals spend out of home, which instead favours the spread of the virus. This second channel substantially attenuates the beneficial effect of temperature in curbing the spread of the virus, offsetting one-third of the potential seasonal fluctuations in the reproduction rate. The mediation role of social activity is particularly pronounced when viral incidence is low, and completely offsets the beneficial effect of temperature. Despite being significant predictors of social activity, wind speed and precipitation do not induce sufficient variation to affect infections. Our estimates also suggest that school closures and lockdowns are effective in reducing infections. We employ our estimates to quantify the seasonal variation in the reproduction rate stemming from weather seasonality in the US.

Heat waves accelerate the spread of infectious diseases

COVID-19 pandemic appeared summer surge in 2022 worldwide and this contradicts its seasonal fluctuations. Even as high temperature and intense ultraviolet radiation can inhibit viral activity, the number of new cases worldwide has increased to >78% in only 1 month since the summer of 2022 under unchanged virus mutation influence and control policies. Using the attribution analysis based on the theoretical infectious diseases model simulation, we found the mechanism of the severe COVID-19 outbreak in the summer of 2022 and identified the amplification effect of heat wave events on its magnitude. The results suggest that approximately 69.3% of COVID-19 cases this summer could have been avoided if there is no heat waves. The collision between the pandemic and the heatwave is not an accident. Climate change is leading to more frequent extreme climate events and an increasing number of infectious diseases, posing an urgent threat to human health and life. Therefore, public health authorities must quickly develop coordinated management plans to deal with the simultaneous occurrence of extreme climate events and infectious diseases.

Beyond the surface: accounting for confounders in understanding the link between collectivism and COVID-19 pandemic in the United States

According to the parasite-stress theory, collectivism serves as a trait of ingroup assortative sociality, providing defense against infectious diseases. This study investigated the association between cultural collectivism and COVID-19 severity at the state (Study 1: N = 51), county (Study 2: N = 3,133), and daily (Study 3: N = 52,806) levels from the beginning of 2020 to the end of 2022. State-level collectivism was assessed using two distinct measures: the U.S. collectivism index, focusing on social interconnectedness and interdependence, and the subjective-culture individualism-collectivism index (reversed), capturing attitudes and beliefs related to religion, abortion, and same-sex marriage. By employing random-intercept multilevel models, the results demonstrated significant and negative effects of state-level collectivism, as measured by the U.S collectivism index, on COVID-19 cases per million, COVID-19 deaths per million, and composite COVID-19 severity index, after controlling for confounding factors, such as socioeconomic development, ecological threats, disease protective behaviors, cultural norms, and political influences. A mini meta-analysis (Study 4: N = 9) confirmed the significance of these effects across studies. These findings supported the proactive role of collectivism in defending against the novel coronavirus in the United States, aligning with the parasite-stress theory of sociality. However, the subjective-culture individualism-collectivism index (reversed) did not exhibit a significant relationship with COVID-19 severity when confounding factors were considered. The high correlation between the subjective-culture individualism-collectivism index (reversed) and the controlled variables suggested shared variance that could diminish its impact on COVID-19 outcomes. Accordingly, the present findings underscore the significance of accounting for confounding factors when examining the association between collectivism and COVID-19 severity at population level. By considering relevant confounding factors, researchers could gain a comprehensive understanding of the complex interplay between cultural collectivism and its influence on COVID-19 severity. Overall, this research contributes to our understanding of how cultural collectivism shapes the COVID-19 pandemic in the United States, emphasizing the importance of adjusting for confounding effects in population level studies.

Multi-source dynamic ensemble prediction of infectious disease and application in COVID-19 case

BACKGROUND

The development of an epidemic always exhibits multiwave oscillation owing to various anthropogenic sources of transmission. Particularly in populated areas, the large-scaled human mobility led to the transmission of the virus faster and more complex. The accurate prediction of the spread of infectious diseases remains a problem. To solve this problem, we propose a new method called the multi-source dynamic ensemble prediction (MDEP) method that incorporates a modified susceptible-exposed-infected-removed (SEIR) model to improve the accuracy of the prediction result.

METHODS

The modified SEIR model is based on the compartment model, which is suitable for local-scale and confined spaces, where human mobility on a large scale is not considered. Moreover, compartmental models cannot be used to predict multiwave epidemics. The proposed MDEP method can remedy defects in the compartment model. In this study, multi-source prediction was made on the development of coronavirus disease 2019 (COVID-19) and dynamically assembled to obtain the final integrated result. We used the real epidemic data of COVID-19 in three cities in China: Beijing, Lanzhou, and Beihai. Epidemiological data were collected from 17 April, 2022 to 12 August, 2022.

RESULTS

Compared to the one-wave modified SEIR model, the MDEP method can depict the multiwave development of COVID-19. The MDEP method was applied to predict the number of cumulative cases of recent COVID-19 outbreaks in the aforementioned cities in China. The average accuracy rates in Beijing, Lanzhou, and Beihai were 89.15%, 91.74%, and 94.97%, respectively.

CONCLUSIONS

The MDEP method improved the prediction accuracy of COVID-19. With further application to other infectious diseases, the MDEP method will provide accurate predictions of infectious diseases and aid governments make appropriate directives.