Investigating vaccine-induced immunity and its effect in mitigating SARS-CoV-2 epidemics in China

Assessing the impact of interventions on the major Omicron BA.2 outbreak in spring 2022 in Shanghai

Background

Shanghai experienced a significant surge in Omicron BA.2 infections from March to June 2022. In addition to the standard interventions in place at that time, additional interventions were implemented in response to the outbreak. However, the impact of these interventions on BA.2 transmission remains unclear.

Methods

We systematically collected data on the daily number of newly reported infections during this wave and utilized a Bayesian approach to estimate the daily effective reproduction number. Data on public health responses were retrieved from the Oxford COVID-19 Government Response Tracker and served as a proxy for the interventions implemented during this outbreak. Using a log-linear regression model, we assessed the impact of these interventions on the reproduction number. Furthermore, we developed a mathematical model of BA.2 transmission. By combining the estimated effect of the interventions from the regression model and the transmission model, we estimated the number of infections and deaths averted by the implemented interventions.

Results

We found a negative association (-0.0069, 95% CI: 0.0096 to -0.0045) between the level of interventions and the number of infections. If interventions did not ramp up during the outbreak, we estimated that the number of infections and deaths would have increased by 22.6% (95% CI: 22.4-22.8%), leading to a total of 768,576 (95% CI: 768,021-769,107) infections and 722 (95% CI: 722-723) deaths. If no interventions were deployed during the outbreak, we estimated that the number of infections and deaths would have increased by 46.0% (95% CI: 45.8-46.2%), leading to a total of 915,099 (95% CI: 914,639-915,518) infections and 860 (95% CI: 860-861) deaths.

Conclusion

Our findings suggest that the interventions adopted during the Omicron BA.2 outbreak in spring 2022 in Shanghai were effective in reducing SARS-CoV-2 transmission and disease burden. Our findings emphasize the importance of non-pharmacological interventions in controlling quick surges of cases during epidemic outbreaks.

Investigating the cumulative lag effects of environmental exposure under urban differences on COVID-19

Although all walks of life are paying less attention to COVID-19, the spread of COVID-19 has never stopped. As an infectious disease, its transmission speed is closely related to the atmosphere environment, particularly the temperature (T) and PM2.5 concentrations. However, How T and PM2.5 concentrations are related to the spread of SARS-CoV-2 and how much their cumulative lag effect differ across cities is unclear. To identify the characteristics of cumulative lag effects of environmental exposure under city differences, this study used a generalized additive model to investigate the associations between T/PM2.5 concentrations and the daily number of new confirmed COVID-19 cases (NNCC) during the outbreak period in the second half of 2021 in Shaoxing, Shijiazhuang, and Dalian. The results showed that except for PM2.5 concentrations in Shaoxing, the NNCC in the three cities generally increased with the unit increase of T and PM2.5 concentrations. In addition, the cumulative lag effects of T/PM2.5 concentrations on NNCC in the three cities reached a peak at lag 26/25, lag 10/26, and lag 18/13 days, respectively, indicating that the response of NNCC to T and PM2.5 concentrations varies among different regions. Therefore, combining local meteorological and air quality conditions to adopt responsive measures is an important way to prevent and control the spread of SARS-CoV-2.

Intensity and drivers of subtypes interference between seasonal influenza viruses in mainland China: A modeling study

Subtype interference has a significant impact on the epidemiological patterns of seasonal influenza viruses (SIVs). We used attributable risk percent [the absolute value of the ratio of the effective reproduction number (Rₑ) of different subtypes minus one] to quantify interference intensity between A/H1N1 and A/H3N2, as well as B/Victoria and B/Yamagata. The interference intensity between A/H1N1 and A/H3N2 was higher in southern China 0.26 (IQR: 0.11-0.46) than in northern China 0.17 (IQR: 0.07-0.24). Similarly, interference intensity between B/Victoria and B/Yamagata was also higher in southern China 0.14 (IQR: 0.07-0.24) than in norther China 0.10 (IQR: 0.04-0.18). High relative humidity significantly increased subtype interference, with the highest relative risk reaching 20.59 (95% CI: 6.12-69.33) in southern China. Southern China exhibited higher levels of subtype interference, particularly between A/H1N1 and A/H3N2. Higher relative humidity has a more pronounced promoting effect on subtype interference.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

Priority age targets for COVID-19 vaccination in Ethiopia under limited vaccine supply

The worldwide inequitable access to vaccination claims for a re-assessment of policies that could minimize the COVID-19 burden in low-income countries. Nine months after the launch of the national vaccination program in March 2021, only 3.4% of the Ethiopian population received two doses of COVID-19 vaccine. We used a SARS-CoV-2 transmission model to estimate the level of immunity accrued before the launch of vaccination in the Southwest Shewa Zone (SWSZ) and to evaluate the impact of alternative age priority vaccination targets in a context of limited vaccine supply. The model was informed with available epidemiological evidence and detailed contact data collected across different geographical settings (urban, rural, or remote). We found that, during the first year of the pandemic, the mean proportion of critical cases occurred in SWSZ attributable to infectors under 30 years of age would range between 24.9 and 48.0%, depending on the geographical setting. During the Delta wave, the contribution of this age group in causing critical cases was estimated to increase on average to 66.7-70.6%. Our findings suggest that, when considering the vaccine product available at the time (ChAdOx1 nCoV-19; 65% efficacy against infection after 2 doses), prioritizing the elderly for vaccination remained the best strategy to minimize the disease burden caused by Delta, irrespectively of the number of available doses. Vaccination of all individuals aged ≥ 50 years would have averted 40 (95%PI: 18-60), 90 (95%PI: 61-111), and 62 (95%PI: 21-108) critical cases per 100,000 residents in urban, rural, and remote areas, respectively. Vaccination of all individuals aged ≥ 30 years would have averted an average of 86-152 critical cases per 100,000 individuals, depending on the setting considered. Despite infections among children and young adults likely caused 70% of critical cases during the Delta wave in SWSZ, most vulnerable ages should remain a key priority target for vaccination against COVID-19.

Assessing the transition of COVID-19 burden towards the young population while vaccines are rolled out in China

SARS-CoV-2 infection causes most cases of severe illness and fatality in older age groups. Over 92% of the Chinese population aged ≥12 years has been fully vaccinated against COVID-19 (albeit with vaccines developed against historical lineages). At the end of October 2021, the vaccination programme has been extended to children aged 3–11 years. Here, we aim to assess whether, in this vaccination landscape, the importation of Delta variant infections could shift COVID-19 burden from adults to children. We developed an age-structured susceptible-infectious-removed model of SARS-CoV-2 transmission to simulate epidemics triggered by the importation of Delta variant infections and project the age-specific incidence of SARS-CoV-2 infections, cases, hospitalizations, intensive care unit admissions, and deaths. In the context of the vaccination programme targeting individuals aged ≥12 years, and in the absence of non-pharmaceutical interventions, the importation of Delta variant infections could have led to widespread transmission and substantial disease burden in mainland China, even with vaccination coverage as high as 89% across the eligible age groups. Extending the vaccination roll-out to include children aged 3–11 years (as it was the case since the end of October 2021) is estimated to dramatically decrease the burden of symptomatic infections and hospitalizations within this age group (39% and 68%, respectively, when considering a vaccination coverage of 87%), but would have a low impact on protecting infants. Our findings highlight the importance of including children among the target population and the need to strengthen vaccination efforts by increasing vaccine effectiveness.

SARS-CoV-2 Seroepidemiological Investigation in Jordan: Seroprevalence, Herd Immunity, and Vaccination Coverage. A Population-Based National Study

Background

Population-based serosurveillance is a cornerstone to furthering our understanding of the COVID-19 pandemic at the community levels. In Jordan, four waves (phases) of seroprevalence epidemiological investigations were conducted using representative population-based national samples. This study aims to estimate the population-based seropositivity, herd immunity, and vaccination coverage at the fourth wave.

Methods

Multistage sampling technique was implemented to recruit a nationally representative sample for the fourth wave of the seroprevalence investigation (June to August 2021). Electronically collected data utilized a questionnaire on background demographics, chronic diseases, and COVID-19 vaccination history. Also, blood samples were collected to detect the presence of total Anti-SARS-CoV-2 IgM and IgG using Wantai/ELISA assays. Prevalence estimates were presented using percentage and 95% Confidence Intervals (C.I.).

Results

There were 8821 participants included in this study, with a mean age of 31.3 years, and 61.7% were females. COVID-19 national seroprevalence and vaccination coverage estimates were 74.1% (95% C.I.: 73.1–74.9%) and 38.4% (95% C.I.: 37.1–39.6%), respectively. Among children, seroprevalence estimates were similar to unvaccinated adults. Among COVID-19 adults, 57.2% were vaccinated. Among vaccinated participants, 91.5% were seropositive, while among unvaccinated, 63.2% were seropositive. By age group, seroprevalence ranged between 53.0% and 86.9%. Seroprevalence estimates were significantly different by gender, vaccination status and dose, and residence.

Conclusion

The reported interplay between seropositivity and vaccination coverage estimate seems insufficient to provide herd immunity levels to combat new variants of SARS-CoV-2. Children and healthcare workers seem to be an epidemiologically influential group in spreading COVID-19. As the globe is still grappling with SARS-CoV-2 infection, national seroepidemiological evidence from Jordan calls for more focus on vaccination coverage, especially among epidemiologically vulnerable groups, to optimize herd immunity.

Lessons Learned from the COVID-19 Control Strategy of the XXXII Tokyo Summer Olympics and the XXIV Beijing Winter Olympics

Coronavirus disease 2019 (COVID-19) has caused more than 500 million infections and 6.2 million deaths globally, resulting in numerous sporting events being cancelled or postponed. Therefore, effective control strategies are urgently needed to prevent COVID-19 transmission at these local and global events. This article introduced the strategies utilized at the Tokyo and Beijing Olympics and proposed several measures for future reference.

Antibody Duration After Infection From SARS-CoV-2 in the Texas Coronavirus Antibody Response Survey

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not).