Large-scale cross-sectional seroepidemiologic study of COVID-19 in Japan: Acquisition of herd immunity and the vaccines' efficacy

Background: The COVID-19 pandemic situation has been changing drastically worldwide due to the continuous appearance of SARS-CoV-2 variants and the roll-out of mass vaccination. Periodic cross-sectional studies during the surge of COVID-19 cases is essential to elucidate the pandemic situation. Methods: Sera of 1,000 individuals who underwent a health check-up in Hyogo Prefecture Health Promotion Association clinics in Japan were collected in August and December 2021. Antibodies against SARS-CoV-2 N and S antigens were detected in the sera by an electrochemiluminescence immunoassay (ECLIA) and an enzyme-linked immunosorbent assay (ELISA), respectively. The sera's neutralization activities for the conventional SARS-CoV-2 (D614G), Delta, and Omicron variants were measured. Results: The seropositive rates for the antibody against N antigen were 2.1% and 3.9% in August and December 2021 respectively, demonstrating a Delta variant endemic during that time; the actual infection rate was approximately twofold higher than the rate estimated based on the polymerase chain reaction (PCR)-based diagnosis. The anti-S seropositive rate was 38.7% in August and it reached 90.8% in December, in concordance with the vaccination rate in Japan. In the December cohort, 78.7% of the sera showed neutralizing activity against the Delta variant, whereas that against the Omicron was much lower at 36.6%. Conclusions: These analyses revealed that herd immunity against SARS-CoV-2 including the Delta variant was established in December 2021, leading to convergence of the variants. The low neutralizing activity against the Omicron variant suggests the need for the further promotion of the prompt three-dose vaccination to overcome this variant's imminent 6th wave in Japan.

Evidence of early community transmission of Omicron (B1.1.529) in Delhi- A city with very high seropositivity and past exposure!

Background: Since identification, infections by the new SARS-CoV-2 variant Omicron are rapidly increasing worldwide. There is a huge gap of knowledge regarding virus behavior in the population from low and middle-income countries. Delhi being a unique population with a high seropositivity and vaccination rate against COVID-19 infection. We aimed to study the epidemiological and clinical presentations of a few early cases of community spread of Omicron infection in the state. Methods: This is a prospective study where respiratory specimen from all RT-PCR confirmed positive cases between November 25th-December 23rd 2021 collected from five districts of Delhi were subjected to whole-genome sequencing. Complete demographic and clinical details were recorded. We also analyzed the formation of local and familial clusters and eventual community transmission. Findings: Out of the 264 cases included during the study period, 68.9% (n=182)were identified as Delta and its sub-lineages while 31.06% (n=82) were Omicron with BA.1 as the predominant sub-lineage (73.1%). Most of the Omicron cases were asymptomatic (n=50,61%) and did not require any hospitalizations. A total of 72 (87.8%) cases were fully vaccinated. 39.1% (n=32) had a history of travel and/or contacts while 60.9 (n=50) showed a community transmission. A steep increase in the daily progression of Omicron cases with its preponderance in the community was observed from 1.8% to 54%. Interpretation: This study is among the first from India to provide evidence of community transmission of Omicron with significantly increased breakthrough infections, decreased hospitalization rates, and a lower rate of symptomatic infections among individuals with high seropositivity against SARS-CoV-2 infections.

How Large Fraction of A Population Must be Vaccinated before A Disease is Controlled?

The ongoing Covid-19 pandemic has already caused more than 5 million casualties despite hard restrictions and relatively high vaccine coverage in many countries. The crucial question is therefore, how large vaccination rate and how severe restrictions are required to terminate the spread of the decease, assuming that the vaccine efficiency and the basic reproduction ratio (R0) are known? To answer this question, a mathematical equation was applied to visualize the required vaccination level as function of vaccine efficiency, restriction efficiency and basic reproduction ratio (R0). In addition to the modelling study, Covid-19 data from Europe was collected during 19/11-26/11 (2021) to assess the relation between vaccination rate and incidence. The analysis indicates that a vaccination rate of ~92% (2 doses) is required to stop Delta (B.1.617.2) without severe restrictions, under conditions like those in Europe late November 2021. A third vaccine dose, improved vaccines, higher vaccination rates and/or stronger restrictions will be required to force Omicron (B.1.1.529) to expire without infecting a large fraction of the population.

Covid-19 disease dynamics with vaccination: The effect of uncertainty

Rate parameters are critical in estimating the covid burden using mathematical models. In the Covid-19 mathematical models, these parameters are assumed to be constant. However, uncertainties in these rate parameters are almost inevitable. In this paper, we study a stochastic epidemic model of the SARS-CoV-2 virus infection in the presence of vaccination in which some parameters fluctuate around its average value. Our analysis shows that if the stochastic basic reproduction number (SBRN) of the system is greater than unity, then there is a stationary distribution, implying the long-time disease persistence. A sufficient condition for disease eradication is also prescribed for which the exposed class goes extinct, followed by the infected class. The disease eradication criterion may not hold if the rate of vaccine-induced immunity loss increases or/and the force of infection increases. Using the Indian Covid-19 data, we estimated the model parameters and showed the future disease progression in the presence of vaccination. The disease extinction time is estimated under various conditions. It is revealed that the mean extinction time is an increasing function of both the force of infection and immunity loss rate and shows the lognormal distribution. We point out that disease eradication might not be possible even at a higher vaccination rate if the vaccine-induced immunity loss rate is high. Our observation thus indicates the endemicity of the disease for the existing vaccine efficacy. The disease eradication is possible only with a higher vaccine efficacy or a reduced infection rate.

Access to COVID-19 Vaccination during the Pandemic in the Informal Settlements of Rome

With the advent of vaccines, the world has a chance to see a real end to the COVID-19 pandemic. To make this possible, however, it is necessary that all groups of people are considered. Contexts of informal settlements and populations such as the homeless and migrants are often forgotten by vaccination campaigns. In this study, carried out as a result of a collaboration with MEDU, a non-profit association aimed at bringing healthcare to vulnerable populations, we provide important data related to the vaccination campaign carried out in the informal settlements of Rome. The objectives of this work are to (1) evaluate vaccination coverage in these contexts, (2) assess the gap with the vaccination coverage of the Italian population and try to hypothesize the causes, and (3) provide recommendations for how humanitarian associations can respond to reduce this gap. We observed important differences in vaccination coverage depending on the type of settlement. The percentage of vaccinated people in these contexts at the beginning of October range between 14.4% and 55.5%, underlining an important gap with the vaccination rate of Italy’s population, which is close to 80%. The data also show that particular attention must be paid to the transiting and irregular people as they are more at risk for a lack of access to vaccination. With this study, in which we provide recommendations that integrate MEDU’s fieldwork experience with the advice of the Framework report, we hope we can help those who work in similar contexts, to carry out a fair and effective vaccination campaign.

Applying Machine Learning and AI Explanations to Analyze Vaccine Hesitancy

The paper identifies and quantifies the impact of race, poverty, politics, and age on COVID-19 vaccination rates in counties across the continental US. Both traditional Ordinary Least Square (OLS) regression analysis and Random Forest machine learning algorithms are applied to quantify contributing factors for county-level vaccination hesitancy. With the machine learning model, joint effects of multiple variables (race/ethnicity, partisanship, age etc.) are considered simultaneously to capture the unique combination of what factors affect the vaccination rate. By implementing a state-of-the-art Artificial Intelligence Explanations (AIX) algorithm, it is possible to solve the black box problem with machine learning models and provide answers to the "how much" question for each measured impact factor in every county. For most counties a higher percentage vote for Republicans, a greater African American population share, and a higher poverty rate lower the vaccination rate. While a higher Asian population share increases the predicted vaccination rate. The impact on the vaccination rate from the Hispanic population proportion is positive in the OLS model, but only positive for counties with very high Hispanic population (65% and more) in the Random Forest model. Both the proportion of seniors and the one for young people in a county have a significant impact in the OLS model - positive and negative, respectively. In contrast, the impacts are ambiguous in the Random Forest model. Because results vary between geographies and since the AIX algorithm is able to quantify vaccine impacts individually for each county, this research can be tailored to local communities. This way it is a helpful tool for local health officials and other policymakers to improve vaccination rates. An interactive online mapping dashboard that identifies impact factors for individual U.S. counties is available at https://www.cpp.edu/~clange/vacmap.html. It is apparent that the influence of impact factors is not universally the same across different geographies.

How Large Fraction of A Population Must be Vaccinated before A Disease is Controlled?

The ongoing Covid-19 pandemic has already caused more than 5 million casualties despite hard restrictions and relatively high vaccine coverage in many countries. The crucial question is therefore, how large vaccination rate and how severe restrictions are required to terminate the spread of the decease, assuming that the vaccine efficiency and the basic reproduction ratio (R0) are known? To answer this question, a simple mathematical equation was developed to visualize the required vaccination level as function of vaccine efficiency, restriction efficiency and basic reproduction ratio (R0). In addition to the modelling study, Covid-19 data from Europe was collected during 19/11-26/11 (2021) to assess the relation between vaccination rate and incidence. The analysis indicates that a vaccination rate of ~92% (2 doses) is currently required to stop Delta (B.1.617.2) without severe restrictions, using the vaccines that are most common in Europe today. A third vaccine dose, improved vaccines, higher vaccination rates and/or stronger restrictions will be required to force Omicron (B.1.1.529) to expire without infecting a large fraction of the population.

Effects of Lockdowns And Its Impacts On Age-Specific Transmission Dynamics of SARS-Cov-2 In Georgia, USA

Social distancing measures are effective in reducing overall community transmission but much remains unknown about how they have impacted finer-scale dynamics. In particular, much is unknown about how changes of contact patterns and other behaviors including adherence to social distancing, induced by these measures, may have impacted finer-scale transmission dynamics among different age groups. In this paper, we build a stochastic age-specific transmission model to systematically characterize the degree and variation of age-specific transmission dynamics, before and after lifting the lockdown in Georgia, USA. We perform Bayesian (missing-) data-augmentation model inference, leveraging reported age-specific case, seroprevalence and mortality data. We estimate that community-level transmissibility was reduced to 41.2% with 95% CI [39%, 43.8%] of the pre-lockdown level in about a week of the announcement of the shelter-in-place order. Although it subsequently increased after the lockdown was lifted, it only bounced back to 62% [58%, 67.2%] of the pre-lockdown level after about a month. We also find that during the lockdown susceptibility to infection increases with age. Specifically, relative to the oldest age group (>65+), susceptibility for the youngest age group (0-17 years) is 0.13 [0.09, 0.18], and it increases to 0.53 [0.49, 0.59] for 18-44 and 0.75 [0.68, 0.82] for 45- 64. More importantly, our results reveal clear changes of age-specific susceptibility (defined as average risk of getting infected during an infectious contact incorporating age-dependent behavioral factors) after the lockdown was lifted, with a trend largely consistent with reported age-specific adherence levels to social distancing and preventive measures. Specifically, the older groups (>45) (with the highest levels of adherence) appear to have the most significant reductions of susceptibility (e.g., post-lockdown susceptibility reduced to 31.6% [29.3%, 34%] of the estimate before lifting the lockdown for the 65+ group). Finally, we find heterogeneity in case reporting rates among different age groups, with the lowest rate occurring among the 0-18 group (9.7% [6.4%, 19%]). Our results provide a more fundamental understanding of the impacts of stringent lockdown measures, and finer evidence that other social distancing and preventive measures may be effective in reducing SARS-CoV-2 transmission. These results may be exploited to guide more effective implementations of these measures in many current settings (with low vaccination rate globally and emerging variants) and in future potential outbreaks of novel pathogens.

Global Perspective of COVID-19 Vaccine Nationalism

This study aimed to explore the global perspective of the association between GDP of various countries and progress of COVID-19 vaccinations; to explore how the global pattern holds in the continents, and investigate the spatial distribution pattern of COVID-19 vaccination progress for all countries. We have used consolidated data on COVID-19 vaccination and GDP from Our World in Data, an open-access data source. Data analysis and visualization were performed in R-Studio. There was a strong linear association between per capita income and the proportion of people vaccinated in countries with one million or more populations. GDP per capita accounts for a 50% variation in the vaccination rate across the nations. Our assessments revealed that the global pattern holds in every continent. Rich European and North-American countries are most protected against COVID-19. Less developed African countries barely initiated the vaccination program. There is a significant disparity among Asian countries. The security of wealthier nations (vac-cinated their citizens) cannot be guaranteed unless adequate vaccination covers the less-endowed countries. Therefore, the global community should take initiatives to speed up the COVID-19 vaccination program in all countries of the world, irrespective of their wealth. Keywords: COVID-19 vaccination; GDP; public health, high-income countries, developing coun-tries