Effects of public-health measures for zeroing out different SARS-CoV-2 variants

SARS-CoV-2 epidemiology, kinetics, and evolution: A narrative review

Since winter 2019, SARS-CoV-2 has emerged, spread, and evolved all around the globe. We explore 4 y of evolutionary epidemiology of this virus, ranging from the applied public health challenges to the more conceptual evolutionary biology perspectives. Through this review, we first present the spread and lethality of the infections it causes, starting from its emergence in Wuhan (China) from the initial epidemics all around the world, compare the virus to other betacoronaviruses, focus on its airborne transmission, compare containment strategies ("zero-COVID" vs. "herd immunity"), explain its phylogeographical tracking, underline the importance of natural selection on the epidemics, mention its within-host population dynamics. Finally, we discuss how the pandemic has transformed (or should transform) the surveillance and prevention of viral respiratory infections and identify perspectives for the research on epidemiology of COVID-19.

Assessing the effectiveness of test-trace-isolate interventions using a multi-layered temporal network

In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) given the time required to develop targeted treatments and vaccines. One of the most common NPIs is Test-Trace-Isolate (TTI). One of the factors determining the effectiveness of TTI is the ability to identify contacts of infected individuals. In this study, we propose a multi-layer temporal contact network to model transmission dynamics and assess the impact of different TTI implementations, using SARS-CoV-2 as a case study. The model was used to evaluate TTI effectiveness both in containing an outbreak and mitigating the impact of an epidemic. We estimated that a TTI strategy based on home isolation and testing of both primary and secondary contacts can contain outbreaks only when the reproduction number is up to 1.3, at which the epidemic prevention potential is 88.2% (95% CI: 87.9%-88.5%). On the other hand, for higher value of the reproduction number, TTI is estimated to noticeably mitigate disease burden but at high social costs (e.g., over a month in isolation/quarantine per person for reproduction numbers of 1.7 or higher). We estimated that strategies considering quarantine of contacts have a larger epidemic prevention potential than strategies that either avoid tracing contacts or require contacts to be tested before isolation. Combining TTI with other social distancing measures can improve the likelihood of successfully containing an outbreak but the estimated epidemic prevention potential remains lower than 50% for reproduction numbers higher than 2.1. In conclusion, our model-based evaluation highlights the challenges of relying on TTIs to contain an outbreak of a novel pathogen with characteristics similar to SARS-CoV-2, and that the estimated effectiveness of TTI depends on the way contact patterns are modeled, supporting the relevance of obtaining comprehensive data on human social interactions to improve preparedness.

Year-round infectome profiling of acute febrile respiratory illness unveiled complex epidemiological dynamics postlifting of COVID-19 restrictions

OBJECTIVES

Following the lifting of COVID-19 nonpharmaceutical interventions in China, respiratory infections surged, though the specific causes remained unclear. This study provided a comprehensive overview of the infectome in patients with acute febrile respiratory illness (AFRI) to inform disease surveillance.

METHODS

Between March 2023 and February 2024, 1163 oropharyngeal swabs from AFRI patients and 338 from healthy individuals were collected in Shenzhen. Meta-transcriptomic sequencing was employed for microbial analysis.

RESULTS

We identified 14 viruses and 10 bacteria species known to cause human disease. Influenza virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Streptococcus pneumoniae, and redondovirus were the most common, with a negative correlation between H3N2 and SARS-CoV-2. Notably, we detected certain enterovirus subtypes (e.g., Coxsackievirus A6 and Echovirus 30), previously overlooked pathogens (e.g., redondovirus), and rare pathogens like Streptococcus pseudopneumoniae. Comparisons revealed five pathogens showed significantly higher abundance in patients than in controls, despite no significant differences for others probably due to their limited number of positive pools. Seasonal shifts in microbial diversity and composition were observed, with climate factors like temperature and precipitation playing a role. Phylogenetic analysis revealed changes in genotype diversity and dominant pathogen lineages.

CONCLUSION

This study highlighted complex pathogen infections in AFRI patients following COVID-19 restrictions, demonstrating the value of meta-transcriptomics over PCR-based methods for more detailed pathogen surveillance.

Time series analysis of the impact of air pollutants on influenza-like illness in Changchun, China

BACKGROUND

Emerging evidence links air pollution to respiratory infections, yet systematic assessments in cold regions remain limited. This study evaluates the short-term effects of six major air pollutants on influenza-like illness (ILI) incidence in Changchun, Northeast China, with implications for air quality management and respiratory disease prevention.

METHODS

ILI surveillance data from Changchun were extracted from "China Influenza Surveillance Network" and the ambient air quality monitoring data of the city were collected from 2017 to 2022. A generalized additive model (GAM) with quasi-Poisson regression analysis was employed to quantify pollutant-ILI associations, adjusting for meteorological factors and temporal trends.

RESULTS

Among 84,010 ILI cases, immediate exposure effects were observed: each 10 µg/m³ increase in PM2.5 (ER = 1.00%, 95% CI: 0.63-1.37%), PM10 (0.90%, 0.57-1.24%), and O3 (1.05%, 0.44-1.67%) significantly elevated ILI risks. Young and middle-aged individuals (25-59 years old) exhibited the highest susceptibility to five pollutants (PM2.5, PM10, SO2, O3, and CO), and age subgroups under 15 years old exhibited susceptibility to NO2. Post-COVID-19 outbreak showed amplified effects across all pollutants (p < 0.05 vs. pre-outbreak). The effects of PM2.5, PM10, SO2 and O3 on ILI cases were greater in the cold season (October to March) (p < 0.05).

CONCLUSIONS

PM2.5, PM10, and O3 exposure significantly increases ILI risks in Changchun, particularly among young/middle-aged populations during cold seasons and post-pandemic periods. These findings underscore the urgency for real-time air quality alerts and targeted protection strategies during high-risk periods to mitigate respiratory health burdens.

Mapping seasonal human mobility across Africa using mobile phone location history and geospatial data

Seasonal human mobility data are essential for understanding socioeconomic and environmental dynamics, yet much of Africa lacks comprehensive mobility datasets. Human movement, shaped by economic needs, family responsibilities, seasonal climatic variations, and displacements, is poorly documented in many regions due to limitations of traditional methods like censuses and surveys. This study addresses these gaps by leveraging the Google Aggregated Mobility Research Dataset (GAMRD) and a Bayesian spatiotemporal framework to estimate pre-pandemic monthly mobility flows at both national and regional scales across Africa for 2018-2019. We analysed 25 countries with complete GAMRD data and developed regional models to estimate mobility in 28 additional countries with sparse or missing records, filling critical data gaps. Key predictors, including GDP per capita, underweight children, infant mortality, environmental variables like stream runoff and evapotranspiration, and covariate interactions, revealed the complexity of mobility drivers. This approach provides robust estimates of seasonal mobility changes in data-limited areas, and offers a foundational understanding of African mobility dynamics, which highlights the value of innovative modelling and novel sources to bridge data gaps for supporting regional planning and policy-making.

Unlocking the power of time-since-infection models: data augmentation for improved instantaneous reproduction number estimation

The time-since-infection (TSI) models, which use disease surveillance data to model infectious diseases, have become increasingly popular due to their flexibility and capacity to address complex disease control questions. However, a notable limitation of TSI models is their primary reliance on incidence data. Even when hospitalization data are available, existing TSI models have not been crafted to improve the estimation of disease transmission or to estimate hospitalization-related parameters-metrics crucial for understanding a pandemic and planning hospital resources. Moreover, their dependence on reported infection data makes them vulnerable to variations in data quality. In this study, we advance TSI models by integrating hospitalization data, marking a significant step forward in modeling with TSI models. We introduce hospitalization propensity parameters to jointly model incidence and hospitalization data. We use a composite likelihood function to accommodate complex data structure and a Monte Carlo expectation-maximization algorithm to estimate model parameters. We analyze COVID-19 data to estimate disease transmission, assess risk factor impacts, and calculate hospitalization propensity. Our model improves the accuracy of estimating the instantaneous reproduction number in TSI models, particularly when hospitalization data is of higher quality than incidence data. It enables the estimation of key infectious disease parameters without relying on contact tracing data and provides a foundation for integrating TSI models with other infectious disease models.

A Regional-Scale Assessment-Based SARS-CoV-2 Variants Control Modeling with Implications for Infection Risk Characterization

Background

The emergence and progression of highly divergent SARS-CoV-2 variants have posed increased risks to global public health, triggering the significant impacts on countermeasures since 2020. However, in addition to vaccination, the effectiveness of non-pharmaceutical interventions, such as social distancing, masking, or hand washing, on different variants of concern (VOC) remains largely unknown.

Objective

This study provides a mechanistic approach by implementing a control measure model and a risk assessment framework to quantify the impacts of control measure combinations on the transmissions of five VOC (Alpha, Beta, Delta, Gamma, and Omicron), along with a different perspective of risk assessment application.

Materials and Methods

We applied uncontrollable ratios as an indicator by adopting basic reproduction number (R 0) data collected from a regional-scale survey. A risk assessment strategy was established by constructing VOC-specific dose-response profiles to implicate practical uses in risk characterization when exposure data are available.

Results

We found that social distancing alone was ineffective without vaccination in almost all countries and VOC when the median R 0 was greater than two. Our results indicated that Omicron could not be contained, even when all control measure combinations were applied, due to its low threshold of infectivity (~3×10-4 plague-forming unit (PFU) mL-1).

Conclusion

To facilitate better decision-making in future interventions, we provide a comprehensive evaluation of how combined control measures impact on different countries and various VOC. Our findings indicate the potential application of threshold estimates of infectivity in the context of risk communication and policymaking for controlling future emerging SARS-CoV-2 variant infections.

Impact of prior SARS-CoV-2 infection on college students' hesitancy to receive additional COVID-19 vaccine booster doses: A study from Taizhou, China

PURPOSE

This study aimed to examine the impact of a history of SARS-CoV-2 infection on the hesitancy of college students to receive additional COVID-19 vaccine booster doses.

METHODS

A population-based self-administered online survey was conducted in July 2024 in Taizhou, China. A total of 792 respondents were included in this study. Logistic regression was conducted to identify factors associated with college students' hesitation to receive booster doses of the COVID-19 vaccine.

RESULTS

Of 792 respondents, 32.2 % hesitated to receive additional doses of the COVID-19 vaccine booster. Furthermore, 23.5 % of the respondents reported an increase in hesitancy to receiving additional COVID-19 vaccine booster doses compared to before they were infected with SARS-CoV-2. In the regression analyses, college students who had a secondary infection were more hesitant to receive additional COVID-19 vaccine booster doses (OR = 0.481, 95 % CI: (0.299-0.774), P = 0.003). Moreover, students with secondary infections who were male (OR = 0.417, 95 % CI: 0.221-0.784, P = 0.007), with lower than a bachelor's degree (OR = 0.471, 95 % CI: 0.272-0.815, P = 0.007), in non-medical majors (OR = 0.460, 95 % CI: 0.248-0.856, P = 0.014), and sophomores or below (OR = 0.483, 95 % CI: 0.286-0.817, P = 0.007) were more hesitant to receive additional COVID-19 vaccine booster doses.

CONCLUSION

A history of SARS-CoV-2 infection affects college students' hesitation to receive additional COVID-19 vaccine booster doses, which was higher in those who experienced secondary infections.

Evolution of the SARS-CoV-2 Omicron Variants: Genetic Impact on Viral Fitness

Over the last three years, the pandemic of COVID-19 has had a significant impact on people's lives and the global economy. The incessant emergence of variant strains has compounded the challenges associated with the management of COVID-19. As the predominant variant from late 2021 to the present, Omicron and its sublineages, through continuous evolution, have demonstrated iterative viral fitness. The comprehensive elucidation of the biological implications that catalyzed this evolution remains incomplete. In accordance with extant research evidence, we provide a comprehensive review of subvariants of Omicron, delineating alterations in immune evasion, cellular infectivity, and the cross-species transmission potential. This review seeks to clarify the underpinnings of biology within the evolution of SARS-CoV-2, thereby providing a foundation for strategic considerations in the post-pandemic era of COVID-19.

Inactivated vaccine effectiveness against symptomatic COVID-19 in Fujian, China during the Omicron BA.2 outbreak

Objective

More than 90% of the Chinese population have completed 2 doses of inactivated COVID-19 vaccines in Mainland China. However, after China government abandoned strict control measures, many breakthrough infections appeared, and vaccine effectiveness against Omicron BA.2 infection was uncertain. This study aims to investigate the real-world effectiveness of widely used inactivated vaccines during the wave of Omicron variants.

Methods

Test-negative case-control study was conducted in this study to analyze the vaccine effectiveness against symptomatic disease caused by the Omicron variant (BA.2) in Fujian, China. Conditional logistic regression was selected to estimate the vaccine effectiveness.

Results

The study found the vaccine effectiveness against symptomatic COVID-19 is 32.46% (95% CI, 8.08% to 50.37%) at 2 to 8 weeks, and 27.05% (95% CI, 1.23% to 46.12%) at 12 to 24 weeks after receiving booster doses of the inactivated vaccine. Notably, the 3-17 years group had higher vaccine effectiveness after 2 doses than the 18-64 years and over 65 years groups who received booster doses.

Conclusion

Inactivated vaccines alone may not offer sufficient protection for all age groups before the summer of 2022. To enhance protection, other types of vaccines or bivalent vaccines should be considered.