Spatial priority for COVID-19 vaccine rollout against limited supply

Comparing Antibody Responses to Homologous vs. Heterologous COVID-19 Vaccination: A Cross-Sectional Analysis in an Urban Bangladeshi Population

BACKGROUND

Vaccination has played a crucial role in mitigating the spread of COVID-19 and reducing its severe outcomes. While over 90% of Bangladesh's population has received at least one COVID-19 vaccine dose, the comparative effectiveness of homologous versus heterologous booster strategies, along with the complex interplay of factors within the population, remains understudied. This study aimed to compare antibody responses between these booster approaches.

METHODS

This cross-sectional study enrolled 723 adults in urban Dhaka who had received COVID-19 booster doses within the last six months. Participants were grouped based on homologous or heterologous booster vaccination. Data were collected through structured household surveys, and 2 mL blood samples were collected for measuring antibody titers.

RESULTS

Heterologous booster recipients showed higher median antibody titers (8597.0 U/mL, IQR 5053.0-15,482.3) compared to homologous recipients (6958.0 U/mL, IQR 3974.0-12,728.5). In the adjusted analysis, the type of booster dose had no significant impact on antibody levels. However, the duration since the last booster dose was significantly associated with antibody levels, where each additional month since receiving the booster corresponded to approximately a 15-16% reduction in antibody levels (Adj. coeff: 0.85, 95% CI: 0.81, 0.88; p < 0.001). Participants over 40 years demonstrated higher antibody levels than younger individuals (Adj. coeff: 1.23, 95% CI: 1.07, 1.43; p = 0.005). Sex, BMI, and prior COVID-19 infection showed no significant associations with antibody levels after adjustment.

CONCLUSION

The results underscore the complexity of immune responses across different demographic groups and suggest potential benefits of ongoing heterologous booster strategies in sustaining immunity.

Analysis and prediction of infectious diseases based on spatial visualization and machine learning

Infectious diseases are a global public health problem that poses a threat to human society. Since the 1970s, constantly mutated new infectious viruses have been quietly attacking humanity, and at least one new type of infectious disease is discovered every year. Therefore, early warning of infectious diseases will greatly reduce the socio-economic harm of infectious diseases. This study is based on the data of COVID-19 epidemic in China (except Macau and Taiwan Province) from 2020 to 2022. Firstly, we used ArcGIS software to analyze the spatial agglomeration pattern of the number of patients in various regions of China through global spatial autocorrelation analysis, local spatial autocorrelation analysis, center of gravity trajectory migration algorithm and other statistical tools; In addition, the areas with serious COVID-19 epidemic and requiring special attention were screened out. Then, autoregressive integrated moving average model (ARIMA), extreme learning machine (ELM), support vector regression (SVR), wavelet neural network (Wavelet), recurrent neural network (RNN) and long short-term memory (LSTM) were used to predict COVID-19 epidemic data in Guangdong Province, China; And the prediction performance of each model was compared through prediction accuracy indicators. Finally, a multi algorithm fusion learning model based on stacking technology is proposed to address the problem of poor generalization ability of single algorithm models in prediction; Furthermore, radial basis function network (RBF) was used as a two-level meta learner to fuse the above models, and particle swarm optimization (PSO) was used to optimize RBF parameters to reduce generalization error. The experimental results show that the performance of the integrated model is better than that of the single model in the COVID-19 dataset. In order to better apply the stacking model to the prediction of new infectious diseases, we applied the prediction model based on the COVID-19 dataset to the prediction of the number of AIDS and pulmonary tuberculosis (PTB) cases, and verified the wide applicability of our model in the prediction of infectious diseases.

Willingness to accept malaria vaccines amongst women presenting at outpatient and immunization clinics in Enugu state, Southeast Nigeria

BACKGROUND

There are giant steps taken in the introduction of the novel malaria vaccine poised towards reducing mortality and morbidity associated with malaria.

OBJECTIVES

This study aimed to determine the knowledge of malaria vaccine and factors militating against willingness to accept the vaccine among mothers presenting in nine hospitals in Enugu metropolis.

METHODS

This was a cross-sectional study carried out among 491 mothers who presented with their children in nine hospitals in Enugu metropolis, South-East Nigeria. A pre-tested and interviewer-administered questionnaire was used in this study.

RESULTS

A majority of the respondents, 72.1% were aware of malaria vaccine. A majority of the respondents, 83.1% were willing to receive malaria vaccine. Similarly, a majority of the mothers, 92.9%, were willing to vaccinate baby with the malaria vaccine, while 81.1% were willing to vaccinate self and baby with the malaria vaccine. The subjects who belong to the low socio-economic class were five times less likely to vaccinate self and baby with malaria vaccine when compared with those who were in the high socio-economic class (AOR = 0.2, 95% CI 0.1-0.5). Mothers who had good knowledge of malaria vaccination were 3.3 times more likely to vaccinate self and baby with malaria vaccine when compared with those who had poor knowledge of malaria vaccination (AOR = 3.3, 95% CI 1-6-6.8).

CONCLUSION

Although the study documented a high vaccine acceptance among the mothers, there exists a poor knowledge of the malaria vaccine among them.

Willingness to accept malaria vaccines amongst women presenting at outpatient and immunization clinics in Enugu state, Southeast Nigeria

Background

There are giant steps taken in the introduction of the novel malaria vaccine poised towards reducing mortality and morbidity associated with malaria.

Objectives

This study aimed to determine the knowledge of malaria vaccine and factors militating against willingness to accept the vaccine among mothers presenting in nine hospitals in Enugu metropolis.

Methods

This was a cross-sectional study carried out among 491 mothers who presented with their children in nine hospitals in Enugu metropolis, South-East Nigeria. A pre-tested and interviewer-administered questionnaire was used in this study.

Results

A majority of the respondents, 72.1% were aware of malaria vaccine. A majority of the respondents, 83.1% were willing to receive malaria vaccine. Similarly, a majority of the mothers, 92.9%, were willing to vaccinate baby with the malaria vaccine, while 81.1% were willing to vaccinate self and baby with the malaria vaccine. The subjects who belong to the low socio-economic class were five times less likely to vaccinate self and baby with malaria vaccine when compared with those who were in the high socio-economic class (AOR = 0.2, 95% CI 0.1–0.5). Mothers who had good knowledge of malaria vaccination were 3.3 times more likely to vaccinate self and baby with malaria vaccine when compared with those who had poor knowledge of malaria vaccination (AOR = 3.3, 95% CI 1–6–6.8).

Conclusion

Although the study documented a high vaccine acceptance among the mothers, there exists a poor knowledge of the malaria vaccine among them.

Willingness to COVID-19 vaccination: Empirical evidence from EU

The unforgettable COVID-19 shock is most likely to be reversed by a viable vaccination strategy. In this paper, we investigate willingness to be vaccinated (WTV) against the COVID-19. Current trends suggest that only around 73% of EU inhabitants (15 and +) were immunized, with more than 104 million people still warranted to be immunized. Vaccine reluctance is a key impediment to conducting immunization programs in the setting of a pandemic. We provide first of its kind empirical evidence on the citizens (N = 11,932) of the EU-27 by employing the recent data from the European Commission. Based on the survey responses, controlling for the correlations in the error terms, we utilize a simulated multivariate probit regression model. Our results show that amongst all the statistically significant drivers of the WTV, the positive perception (vaccination works and has no side effects); R&D information (clarity on how vaccination is developed, tested, authorized) has the largest impact on the WTV. We find that the group of variables on social feedback (Positive perception; social adoption and pressure), and on trustworthy sources of information (R&D info; medical advice) are to be considered for WTV policy. The counteracting policy gaps that act against WTV include vaccination governance dissatisfaction, perception of long-term side effects, growing mistrust in information sources, uncertainty between safety and efficacy, education level, and risky age group. Strategies based on the outcomes of this study are needed to address public acceptance and willingness to vaccinate during a pandemic. This research is novel and offers authorities in-depth insights into the challenges and solutions regarding the COVID-19 pandemic and thus to its end via stimulation of the WTV.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

Lessons from COVID'S Vaccination: External-Internal Frictions and Efficiency

This paper explores some economic variables that determine the emerging of new COVID-19 variants and the determinants of vaccination advances in 108 countries during a quarterly period from March 2020 to March 2022. We found that more people being fully vaccinated and more education (measured as schooling years) decrease the probability of the emergence of new COVID-19 variants, but more crowded cities and higher percentages of urban population increase that probability. Furthermore, we found that the percentage of fully vaccinated people depends positively on the country's preparation to respond to a health crisis, educational levels, and the index of economic complexity (which measures how diverse in the production of goods and services a country is and the level of its infrastructure), and it depends negatively on the percentage of rural populations (which makes vaccination more difficult).

More Than Three-Fourths of AstraZeneca (ChAdox1 COV-19) COVID-19 Vaccinated Individuals Develop Post Immunization Adverse Event in Northwest Ethiopia

Background

Ethiopia was using the ChAdOx1 COV-19 vaccine, and health professionals were targets of the first phase of the vaccination strategy. Evidence on the adverse events following immunization (AEFI) was barely available. The study aimed to assess the magnitude and associated factors of adverse events following ChAdOx1 COV-19 immunization among health professionals of the University of Gondar Specialized and Comprehensive Hospital, 2021.

Methods

An institution-based cross-sectional study was conducted among health professionals of the University of Gondar Comprehensive and specialized referral hospital. All health professionals who took the ChAdOx1 COV-19 vaccine in the 1st phase were surveyed. A total of 314 health professionals who took the ChAdOx1 COV-19 vaccine were included. The EpiData version 4.6.0.0 and Stata 16 were used for data entry and analysis, respectively. A binary logistic regression was used to identify statistically significant factors associated with AEFI. Chi-square and multicollinearity assumptions were tested. A p-value <0.2 and 0.05 were used as cut-off values of significance in the bi- and multivariable logistic regression models, respectively. An adjusted odds ratio (AOR) with 95% CI was reported for statistically significant variables.

Results

Among 314 study participants, 263 of them had at least one mild to severe AEFI of ChAdOx1 COV-19 with a prevalence of AEFI of 83.76% (95% CI: 79.23, 87.46). The commonest AEFI observed were injection site tenderness (n=198/263), fatigue (114/263), headache (n=107/263), and muscle pain (n=85/263). Females (AOR=2.75, 95% CI: 1.15, 6.58), and participants who felt the vaccine was unsafe (AOR=2.84, 95% CI: 1.03, 7.85) were having nearly three times more odds of AEFI immunization as compared to males and those who felt the vaccine was safe, respectively.

Conclusion

Adverse event following immunization has been a public health problem in Northwest Ethiopia. Being female and having a feeling that the vaccine is unsafe were statistically significantly associated with AEFI.

COVID-19 Vaccine Hesitancy: The Role of Socioeconomic Factors and Spatial Effects

This paper investigates the spatial dimension of socioeconomic and demographic factors behind COVID-19 vaccine hesitancy. With a focus on a county with considerable sociodemographic diversity in the state of Texas, USA, we apply regression models to census-tract-level data of the unvaccinated population. In addition to disparities in accessing the vaccination service, particularly for residents in rural areas, empirical results confirm under-vaccination among lower socioeconomic neighborhoods and communities with signs of distrust in government. The spatial model regressions further underscore the impact that vaccine hesitancy among residents in one community spread to its nearby communities. This observed spatial spillover effect is attributable to the geographic interactions of similar socioeconomic groups.