Global spatiotemporal trends and determinants of COVID-19 vaccine acceptance on Twitter: a multilingual deep learning study in 135 countries and territories

BackgroundCOVID-19 vaccination has faced a range of challenges from supply-side barriers such as insufficient vaccine supply and negative information environment and demand-side barriers centring on public acceptance and confidence in vaccines. This study assessed global spatiotemporal trends in demand- and supply-side barriers to vaccine uptake using COVID-19-related social media data and explored the country-level determinants of vaccine acceptance. MethodsWe accessed a total of 13,093,406 tweets sent between November 2020 and March 2022 about the COVID-19 vaccine in 90 languages from 135 countries using Meltwater(R) (a social listening platform). Based on 8,125 manually-annotated tweets, we fine-tuned multilingual deep learning models to automatically annotate all 13,093,406 tweets. We present spatial and temporal trends in four key spheres: (1) COVID-19 vaccine acceptance; (2) confidence in COVID-19 vaccines; (3) the online information environment regarding the COVID-19 vaccine; and (4) perceived supply-side barriers to COVID-19 vaccination. Using univariate and multilevel regressions, we evaluated the association between COVID-19 vaccine acceptance on Twitter(R) and (1) country-level characteristics regarding governance, pandemic preparedness, trust, culture, social development, and population demographics; (2) country-level COVID-19 vaccine coverage; and (3) Google(R) search trends on adverse vaccine events. FindingsCOVID-19 vaccine acceptance was high among Twitter(R) users in Southeast Asian, Eastern Mediterranean, and Western Pacific countries, including India, Indonesia, and Pakistan. In contrast, acceptance was relatively low in high-income nations like South Korea, Japan, and the Netherlands. Spatial variations were correlated with country-level governance, pandemic preparedness, public trust, culture, social development, and demographic determinants. At the country level, vaccine acceptance sentiments expressed on Twitter(R) predicted higher vaccine coverage. We noted the declining trend of COVID-19 vaccine acceptance among global Twitter(R) users since March 2021, which was associated with increased searches for adverse vaccine events. Interpretation In future pandemics, new vaccines may face the potential low-level and declining trend in acceptance, like COVID-19 vaccines, and early responses are needed. Social media mining represents a promising surveillance approach to monitor vaccine acceptance and can be validated against real-world vaccine uptake data. FundingNational Natural Science Foundation of China.

Immunogenicity of heterologous BNT162b2 booster in fully vaccinated individuals with CoronaVac against SARS-CoV-2 variants Delta and Omicron: the Dominican Republic Experience

The recent emergence of the SARS-CoV-2 Omicron variant is raising concerns because of its increased transmissibility and by its numerous spike mutations with potential to evade neutralizing antibodies elicited by COVID-19 vaccines. The Dominican Republic was among the first countries in recommending the administration of a third dose COVID-19 vaccine to address potential waning immunity and reduced effectiveness against variants. Here, we evaluated the effects of a heterologous BNT162b2 mRNA vaccine booster on the humoral immunity of participants that had received a two-dose regimen of CoronaVac, an inactivated vaccine used globally. We found that heterologous CoronaVac prime followed by BNT162b2 booster regimen induces elevated virus-specific antibody levels and potent neutralization activity against the ancestral virus and Delta variant, resembling the titers obtained after two doses of mRNA vaccines. While neutralization of Omicron was undetectable in participants that had received a two-dose regimen of CoronaVac vaccine, BNT162b2 booster resulted in a 1.4-fold increase in neutralization activity against Omicron, compared to two-dose mRNA vaccine. Despite this increase, neutralizing antibody titers were reduced by 6.3-fold and 2.7-fold for Omicron compared to ancestral and Delta variant, respectively. Surprisingly, previous SARS-CoV-2 infection did not affect the neutralizing titers for Omicron in participants that received the heterologous regimen. Our findings have immediate implications for multiples countries that previously used a two-dose regimen of CoronaVac and reinforce the notion that the Omicron variant is associated with immune escape from vaccines or infection-induced immunity, highlighting the global need for vaccine boosters to combat the impact of emerging variants.

Predicting COVID-19 epidemiological trend by applying population mobility data in two-stage modeling.

:To predict the epidemiological trend of coronavirus disease 2019 (COVID-19) by mathematical modeling based on the population mobility and the epidemic prevention and control measures. : As of February 8，2020，the information of 151 confirmed cases in Yueqing，Zhejiang province were obtained，including patients' infection process，population mobility between Yueqing and Wuhan，etc. To simulate and predict the development trend of COVID-19 in Yueqing, the study established two-stage mathematical models，integrating the population mobility data with the date of symptom appearance of confirmed cases and the transmission dynamics of imported and local cases. : It was found that in the early stage of the pandemic，the number of daily imported cases from Wuhan (using the date of symptom appearance) was positively associated with the number of population travelling from Wuhan to Yueqing on the same day and 6 and 9 days before that. The study predicted that the final outbreak size in Yueqing would be 170 according to the number of imported cases estimated by consulting the population number travelling from Wuhan to Yueqing and the susceptible-exposed-infectious-recovered (SEIR) model; while the number would be 165 if using the reported daily number of imported cases. These estimates were close to the 170，the actual monitoring number of cases in Yueqing as of April 27，2020. : The two-stage modeling approach used in this study can accurately predict COVID-19 epidemiological trend.

Subjective Well-being and Family Functioning among Adolescents Left Behind by Migrating Parents in Jiangxi Province, China / 生物医学与环境科学（英文）

We sought to identify the differences between adolescents left behind in their home villages/towns (LBA) and non-left behind adolescents (NLB) on subjective well-being and family functioning due to parental migration in south China. We used a stratified cluster sampling method to recruit middle school students in a city experiencing population-emigration in Jiangxi Province in 2010. Participants included adolescents from families with: (1) one migrant parent, (2) both parents who migrated, or (3) non-left behind adolescents (i.e., no migrant parent). To determine predictors of subjective well-being, we used structural equation models. Adolescents left behind by both parents (LBB) were less likely to express life satisfaction (P = 0.038) in terms of their environments (P = 0.011) compared with NLB. A parent or parents who migrated predicts lower subjective well-being of adolescents (P = 0.051) and also lower academic performance. Being apart from their parents may affect family functioning negatively from an adolescent's viewpoint. Given the hundreds of millions of persons in China, many who are parents, migrating for work, there may be mental health challenges in some of the adolescents left behind.