Impact of vaccination and non-pharmacological interventions on COVID-19: a review of simulation modeling studies in Asia

Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

The effect of COVID-19 vaccination on symptomatic infection and related symptoms among preterm-born children aged 3-7 years in China

Vaccination plays a crucial role in preventing and controlling SARS-CoV-2 infections as well as their associated adverse outcomes. But there is a notable lack of research on the effectiveness of COVID-19 vaccination in children, particularly those young preterm-born children, who are more vulnerable to severe outcomes from SARS-CoV-2 infection. We aimed to determine the effect of vaccination with inactivated vaccines BBIBP-CorV and CoronaVac on symptomatic COVID-19 infection and related symptoms in preterm-born children aged 3-7 years after relaxation of the COVID-19 prevention and control measures in December 2022 in China. We performed a retrospective cohort study involving 242 preterm-born children aged 3-7 years and the data were collected in March 2023. Logistic regression models and modified Poisson regression models combined with entropy balancing were used to explore the associations of vaccination against SARS-CoV-2 with symptomatic COVID-19, specific symptoms, and persistent symptoms one month after recovery from COVID-19. Of the 242 recruited preterm-born children, 156 (64.5%) were vaccinated with inactivated vaccines BBIBP-CorV and CoronaVac. After entropy balancing, the covariates were balanced between the vaccinated and the unvaccinated groups, with standardized mean difference < 0.001. Vaccination with the said SARS-CoV-2 vaccines lowered the risk of developing symptomatic COVID-19 in preterm-born children (risk ratio [RR] = 0.783; 95% confidence interval [CI]: (0.711, 0.861). Likewise, COVID-19 vaccination was associated with a decline in the risk of pneumonia (odds ratio [OR] = 0.318; 95% CI 0.110, 0.913), fever (RR = 0.710; 95% CI 0.635, 0.794), high fever (RR = 0.542; 95% CI 0.297, 0.988), sore throat (OR = 0.304; 95% CI 0.139, 0.664), and persistent symptoms (RR = 0.425; 95% CI 0.182, 0.993). Immunization with inactivated vaccines BBIBP-CorV and CoronaVac provides protection against symptomatic COVID-19 for preterm-born children 3-7 years.

Exploring the association between ambient air pollution and COVID-19 risk: A comprehensive meta-analysis with meta-regression modelling

Introduction

Air pollution is speculated to increase the risk of Coronavirus disease-2019 (COVID-19). Nevertheless, the results remain inconsistent and inconclusive. This study aimed to explore the association between ambient air pollution (AAP) and COVID-19 risks using a meta-analysis with meta-regression modelling.

Methods

The inclusion criteria were: original studies quantifying the association using effect sizes and 95 % confidence intervals (CIs); time-series, cohort, ecological or case-crossover peer-reviewed studies in English. Exclusion criteria encompassed non-original studies, animal studies, and data with common errors. PubMed, Web of Science, Embase and Google Scholar electronic databases were systemically searched for eligible literature, up to 31, March 2023. The risk of bias (ROB) was assessed following the Agency for Healthcare Research and Quality parameters. A random-effects model was used to calculate pooled risk ratios (RRs) and their 95 % CIs.

Results

A total of 58 studies, between 2020 and 2023, met the inclusion criteria. The global representation was skewed, with major contributions from the USA (24.1 %) and China (22.4 %). The distribution included studies on short-term (43.1 %) and long-term (56.9 %) air pollution exposure. Ecological studies constituted 51.7 %, time-series-27.6 %, cohorts-17.2 %, and case crossover-3.4 %. ROB assessment showed low (86.2 %) and moderate (13.8 %) risk. The COVID-19 incidences increased with a 10 μg/m3 increase in PM2.5 [RR = 4.9045; 95 % CI (4.1548-5.7895)], PM10 [RR = 2.9427: (2.2290-3.8850)], NO2 [RR = 3.2750: (3.1420-3.4136)], SO2 [RR = 3.3400: (2.7931-3.9940)], CO [RR = 2.6244: (2.5208-2.7322)] and O3 [RR = 2.4008: (2.1859-2.6368)] concentrations. A 10 μg/m3 increase in concentrations of PM2.5 [RR = 3.0418: (2.7344-3.3838)], PM10 [RR = 2.6202: (2.1602-3.1781)], NO2 [RR = 3.2226: (2.1411-4.8504)], CO [RR = 1.8021 (0.8045-4.0370)] and O3 [RR = 2.3270 (1.5906-3.4045)] was significantly associated with COVID-19 mortality. Stratified analysis showed that study design, exposure period, and country influenced exposure-response associations. Meta-regression model indicated significant predictors for air pollution-COVID-19 incidence associations.

Conclusion

The study, while robust, lacks causality demonstration and focuses only on the USA and China, limiting its generalizability. Regardless, the study provides a strong evidence base for air pollution-COVID-19-risks associations, offering valuable insights for intervention measures for COVID-19.

Second booster dose improves antibody neutralization against BA.1, BA.5 and BQ.1.1 in individuals previously immunized with CoronaVac plus BNT162B2 booster protocol

Introduction

SARS-CoV-2 vaccines production and distribution enabled the return to normalcy worldwide, but it was not fast enough to avoid the emergence of variants capable of evading immune response induced by prior infections and vaccination. This study evaluated, against Omicron sublineages BA.1, BA.5 and BQ.1.1, the antibody response of a cohort vaccinated with a two doses CoronaVac protocol and followed by two heterologous booster doses.

Methods

To assess vaccination effectiveness, serum samples were collected from 160 individuals, in 3 different time points (9, 12 and 18 months after CoronaVac protocol). For each time point, individuals were divided into 3 subgroups, based on the number of additional doses received (No booster, 1 booster and 2 boosters), and a viral microneutralization assay was performed to evaluate neutralization titers and seroconvertion rate.

Results

The findings presented here show that, despite the first booster, at 9m time point, improved neutralization level against omicron ancestor BA.1 (133.1 to 663.3), this trend was significantly lower for BQ.1.1 and BA.5 (132.4 to 199.1, 63.2 to 100.2, respectively). However, at 18m time point, the administration of a second booster dose considerably improved the antibody neutralization, and this was observed not only against BA.1 (2361.5), but also against subvariants BQ.1.1 (726.1) and BA.5 (659.1). Additionally, our data showed that, after first booster, seroconvertion rate for BA.5 decayed over time (93.3% at 12m to 68.4% at 18m), but after the second booster, seroconvertion was completely recovered (95% at 18m).

Discussion

Our study reinforces the concerns about immunity evasion of the SARS-CoV-2 omicron subvariants, where BA.5 and BQ.1.1 were less neutralized by vaccine induced antibodies than BA.1. On the other hand, the administration of a second booster significantly enhanced antibody neutralization capacity against these subvariants. It is likely that, as new SARS-CoV-2 subvariants continue to emerge, additional immunizations will be needed over time.

Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.