Abstract:This meta-analysis aims to synthesize global evidence on the risk of reinfection among people previously infected with SARS-CoV-2. We systematically searched PubMed, Scopus, Embase and Web of Science as of April 5, 2021. We conducted: (1) meta-analysis of cohort studies containing data sufficient for calculating the incidence rate of SARS-CoV-2 reinfection; (2) systematic review of case reports with confirmed SARS-CoV-2 reinfection cases. The reinfection incidence was pooled by zero-inflated beta distribution.… Show more
“…This study also indicated that the reinfection rate varies among different countries, such as Brazil, higher than other countries, with a reinfection rate of 6.60% (95% CI : 3.94 -10.84%), which migtht be influenced by different countries’ epidemic prevention policies, susceptible populations, ecological environment, etc. [ 34 ].…”
Background
There is a significant increase in the number of SARS-CoV-2 reinfection reports in various countries. However, the trend of reinfection rate over time is not clear.
Methods
We searched PubMed, Web of Science, Medline, Embase, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, and Wanfang for cohort studies, case-control studies, and cross-sectional studies up to March 16, 2023, to conduct a meta-analysis of global SARS-CoV-2 reinfection rate. Subgroup analyses were performed for age, country, study type, and study population, and time-varying reinfection rates of SARS-CoV-2 were estimated using meta-regression. The risk of bias was assessed using the Newcastle-Ottawa Scale and the Joanna Briggs Institute critical appraisal tool.
Result
A total of 55 studies involving 111,846 cases of SARS-CoV-2 reinfection were included. The pooled SARS-CoV-2 reinfection rate was 0.94% (95% CI: 0.65 -1.35%). In the subgroup analyses, there were statistically significant differences in the pooled reinfection rates by reinfection variant, and study type (P < 0.05). Based on meta-regression, the reinfection rate fluctuated with time.
Conclusion
Meta-regression analysis found that the overall reinfection rate increased and then decreased over time, followed by a period of plateauing and then a trend of increasing and then decreasing, but the peak of the second wave of reinfection rate was lower than the first wave. SARS-CoV-2 is at risk of reinfection and the Omicron variant has a higher reinfection rate than other currently known variants. The results of this study could help guide public health measures and vaccination strategies in response to the Coronavirus Disease 2019 (COVID-19) pandemic.
“…This study also indicated that the reinfection rate varies among different countries, such as Brazil, higher than other countries, with a reinfection rate of 6.60% (95% CI : 3.94 -10.84%), which migtht be influenced by different countries’ epidemic prevention policies, susceptible populations, ecological environment, etc. [ 34 ].…”
Background
There is a significant increase in the number of SARS-CoV-2 reinfection reports in various countries. However, the trend of reinfection rate over time is not clear.
Methods
We searched PubMed, Web of Science, Medline, Embase, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, and Wanfang for cohort studies, case-control studies, and cross-sectional studies up to March 16, 2023, to conduct a meta-analysis of global SARS-CoV-2 reinfection rate. Subgroup analyses were performed for age, country, study type, and study population, and time-varying reinfection rates of SARS-CoV-2 were estimated using meta-regression. The risk of bias was assessed using the Newcastle-Ottawa Scale and the Joanna Briggs Institute critical appraisal tool.
Result
A total of 55 studies involving 111,846 cases of SARS-CoV-2 reinfection were included. The pooled SARS-CoV-2 reinfection rate was 0.94% (95% CI: 0.65 -1.35%). In the subgroup analyses, there were statistically significant differences in the pooled reinfection rates by reinfection variant, and study type (P < 0.05). Based on meta-regression, the reinfection rate fluctuated with time.
Conclusion
Meta-regression analysis found that the overall reinfection rate increased and then decreased over time, followed by a period of plateauing and then a trend of increasing and then decreasing, but the peak of the second wave of reinfection rate was lower than the first wave. SARS-CoV-2 is at risk of reinfection and the Omicron variant has a higher reinfection rate than other currently known variants. The results of this study could help guide public health measures and vaccination strategies in response to the Coronavirus Disease 2019 (COVID-19) pandemic.
“…The infectious individuals are further categorised as asymptomatic and symptomatic (mild and severe) according to their symptoms. Also, the recovered agents might become susceptible again due to the wanning effects of community 19 . Figure 7 Flowchart of the transmission process of agent’s COVID health status.…”
Section: Methodsmentioning
confidence: 99%
“…In addition, in this study, we select six major Long COVID symptoms discovered from the literature 19 and use the estimated ratios to simulate the sequela conditions in the case study. The ratios of recovered agents for getting respiratory conditions is 3.655%, diseases of the nervous system is 2.644%, mental health burden is 2.888%, metabolic disorders is 3.008%, poor general well-being is 3.705%, and cardiovascular conditions 4.864%.…”
Although the dynamic zero-COVID policy has effectively controlled virus spread in China, China has to face challenges in balancing social-economic burdens, vaccine protection, and the management of long COVID symptoms. This study proposed a fine-grained agent-based model to simulate various strategies for transitioning from a dynamic zero-COVID policy with a case study in Shenzhen. The results indicate that a gradual transition, maintaining some restrictions, can mitigate infection outbreaks. However, the severity and duration of epidemics vary based on the strictness of the measures. In contrast, a more direct transition to reopening may lead to rapid herd immunity but necessitate preparedness for potential sequelae and reinfections. Policymakers should assess healthcare capacity for severe cases and potential long-COVID symptoms and determine the most suitable approach tailored to local conditions.
“….It is also highly likely that the process of antigenic priming will begin to affect emerging variants following initial exposure to the Wuhan or subsequent strains. Acquired natural immunity (from any variant) has been shown to confer protection against the subsequent effects of further COVID-19 infection [103][104][105][106][107][108]. The extent of exposure to each variant is highly dependent on location as shown in Figures 7, A3.1 to A3.3.…”
Section: Roles For Immune Priming In Age Specificitymentioning
Since 2020 COVID-19 has caused serious mortality around the world. Given the ambiguity in es-tablishing COVID-19 as the direct cause of death we first investigate the effects of age and sex upon all-cause mortality during 2020 and 2021 in England and Wales. Since infectious agents have their own unique age profile for death, we explore several methods to adjust single year of age deaths in England and Wales during 2019 (the pre-COVID-19 base year) to a pathogen-neutral single year of age baseline. This adjusted base year is then used to confirm the widely reported higher deaths in males for most ages above 43 years in both 2020 and 2021. During 2020 (+ COVID-19 but no vac-cination) both male and female population-adjusted deaths were significantly increased above age 35. A significant reduction in all-cause mortality among both males and females aged 75+ could be demonstrated in 2021 during widespread COVID–19 vaccination, however, below age 75 deaths progressively increased. This finding arises from a mix of vaccination and year of age profiles of deaths for the different SARS-CoV-2 variants. In addition, specific effects for age around puberty were demonstrated where females had higher deaths rather than males. There is evidence that year-of-birth cohorts may also be involved, indicating that immune priming to specific pathogen outbreaks in the past may lead to lower deaths for some birth cohorts. To specifically identify the age profile for the COVID-19 variants in 2020 to 2023, we employ the proportion of total deaths at each age which are potentially due to or ‘with’ COVID-19. The original Wuhan strain, and the Alpha variant show somewhat limited divergence in the age profile with the Alpha variant shifted to a moderately higher proportion of deaths below age 84. The Delta variant specifically targeted persons below age 65. The Omicron variants showed significantly lower proportion of overall mortality, with markedly higher relative proportion of deaths above age 65 steeply increasing with age to a maximum around 100 years of age. A similar age profile for the variants can be seen in the age-banded deaths in US states – although slightly obscured by using age bands rather than single year of age. However, the US data shows that higher male deaths are greatly dependent on age and the COVID-variant. Deaths determined as ‘due to’ COVID-19 (as opposed to ‘involving’ COVID-19) in England and Wales were especially over-estimated in 2021 relative to the change in all-cause mortality. This arose as a by-product of an increase in COVID-19 testing capacity in late 2020. Potential structure-function mechanisms for the year of age specificity of SARS-CoV-2 var-iants are discussed. The question is posed as to whether vaccines based on different variants carry the specific age profile through into the vaccine.
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