Effects of vaccination and non-pharmaceutical interventions and their lag times on the COVID-19 pandemic: Comparison of eight countries

Non-pharmaceutical interventions in containing COVID-19 pandemic after the roll-out of coronavirus vaccines: a systematic review

BACKGROUND

Non-pharmaceutical interventions (NPIs) have been widely utilised to control the COVID-19 pandemic. However, it is unclear what the optimal strategies are for implementing NPIs in the context of coronavirus vaccines. This study aims to systematically identify, describe, and evaluate existing ecological studies on the real-world impact of NPIs in containing COVID-19 pandemic following the roll-out of coronavirus vaccines.

METHODS

We conducted a comprehensive search of relevant studies from January 1, 2021, to June 4, 2023 in PubMed, Embase, Web of science and MedRxiv. Two authors independently assessed the eligibility of the studies and extracted the data. A risk of bias assessment tool, derived from a bibliometric review of ecological studies, was applied to evaluate the study design, statistical methodology, and the quality of reporting. Data were collected, synthesised and analysed using qualitative and quantitative methods. The results were presented using summary tables and figures, including information on the target countries and regions of the studies, types of NPIs, and the quality of evidence.

RESULTS

The review included a total of 17 studies that examined the real-world impact of NPIs in containing the COVID-19 pandemic after the vaccine roll-out. These studies used five composite indicators that combined multiple NPIs, and examined 14 individual NPIs. The studies had an average quality assessment score of 13 (range: 10-16), indicating moderately high quality. NPIs had a larger impact than vaccination in mitigating the spread of COVID-19 during the early stage of the vaccination implementation and in the context of the Omicron variant. Testing policies, workplace closures, and restrictions on gatherings were the most effective NPIs in containing the COVID-19 pandemic, following the roll-out of vaccines. The impact of NPIs varied across different time frames, countries and regions.

CONCLUSION

NPIs had a larger contribution to the control of the pandemic as compared to vaccination during the early stage of vaccine implementation and in the context of the omicron variant. The impact of NPIs in containing the COVID-19 pandemic exhibited variability in diverse contexts. Policy- and decision-makers need to focus on the impact of different NPIs in diverse contexts. Further research is needed to understand the policy mechanisms and address potential future challenges.

The impact of triple doses vaccination and other interventions for controlling the outbreak of COVID-19 cases and mortality in Australia: A modelling study

COVID-19 is a significant public health problem around the globe, including in Australia. Despite this, Australia's Ministry of Health has expanded COVID-19 control measures widely, logistical trials exist, and the disease burden still needs more clarity. One of the best methods to comprehend the dynamics of disease transmission is by mathematical modeling of COVID-19, which also makes it possible to quantify factors in many places, including Australia. In order to understand the dynamics of COVID-19 in Australia, we examine a mathematical modeling framework for the virus in this study. Australian COVID-19 actual incidence data from January to December 2021 was used to calibrate the model. We also performed a sensitivity analysis of the model parameters and found that the COVID-19 transmission rate was the primary factor in determining the basic reproduction number (R0). Gradually influential intervention policies were established, with accurate effect and coverage regulated with the help of COVID-19 experts in Australia. We simulated data for the period from April 2022 to August 2023. To ascertain which of these outcomes is most effective in lowering the COVID-19 burden, we here assessed the COVID-19 burden (as shown by the number of incident cases and mortality) under a range of intervention scenarios. Regarding the policy of single intervention, the fastest and most efficient way to lower the incidence of COVID-19 is via increasing the first-dose immunization rate, while an improved treatment rate for the afflicted population is also helps to lower mortality in Australia. Furthermore, our results imply that integrating more therapies at the same time increases their efficacy, particularly for mortality, which significantly reduced with a moderate effort, while lowering the number of COVID-19 instances necessitates a major and ongoing commitment.

Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings

Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

COVID-19 Epidemiology during Delta Variant Dominance Period in 45 High-Income Countries, 2020-2021

The SARS-CoV-2 Delta variant, first identified in October 2020, quickly became the dominant variant worldwide. We used publicly available data to explore the relationship between illness and death (peak case rates, death rates, case-fatality rates) and selected predictors (percentage vaccinated, percentage of the population >65 years, population density, testing volume, index of mitigation policies) in 45 high-income countries during the Delta wave using rank-order correlation and ordinal regression. During the Delta-dominant period, most countries reported higher peak case rates (57%) and lower peak case-fatality rates (98%). Higher vaccination coverage was protective against peak case rates (odds ratio 0.95, 95% CI 0.91-0.99) and against peak death rates (odds ratio 0.96, 95% CI 0.91-0.99). Vaccination coverage was vital to preventing infection and death from COVID-19 during the Delta wave. As new variants emerge, public health authorities should encourage the uptake of COVID-19 vaccination and boosters.

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models

BACKGROUND

Mathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control.

METHODS

To improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc).

RESULTS

A model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (-0.81 and -0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (-0.03), Israel (-0.12), Italy (-0.02), and France (-0.03); all p < 0.05.

CONCLUSIONS

The key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a "real-time" schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.

A global analysis of COVID-19 infection fatality rate and its associated factors during the Delta and Omicron variant periods: an ecological study

Background

The Omicron variant of SARS-CoV-2 is more highly infectious and transmissible than prior variants of concern. It was unclear which factors might have contributed to the alteration of COVID-19 cases and deaths during the Delta and Omicron variant periods. This study aimed to compare the COVID-19 average weekly infection fatality rate (AWIFR), investigate factors associated with COVID-19 AWIFR, and explore the factors linked to the increase in COVID-19 AWIFR between two periods of Delta and Omicron variants.

Materials and methods

An ecological study has been conducted among 110 countries over the first 12 weeks during two periods of Delta and Omicron variant dominance using open publicly available datasets. Our analysis included 102 countries in the Delta period and 107 countries in the Omicron period. Linear mixed-effects models and linear regression models were used to explore factors associated with the variation of AWIFR over Delta and Omicron periods.

Findings

During the Delta period, the lower AWIFR was witnessed in countries with better government effectiveness index [β = -0.762, 95% CI (-1.238)-(-0.287)] and higher proportion of the people fully vaccinated [β = -0.385, 95% CI (-0.629)-(-0.141)]. In contrast, a higher burden of cardiovascular diseases was positively associated with AWIFR (β = 0.517, 95% CI 0.102-0.932). Over the Omicron period, while years lived with disability (YLD) caused by metabolism disorders (β = 0.843, 95% CI 0.486-1.2), the proportion of the population aged older than 65 years (β = 0.737, 95% CI 0.237-1.238) was positively associated with poorer AWIFR, and the high proportion of the population vaccinated with a booster dose [β = -0.321, 95% CI (-0.624)-(-0.018)] was linked with the better outcome. Over two periods of Delta and Omicron, the increase in government effectiveness index was associated with a decrease in AWIFR [β = -0.438, 95% CI (-0.750)-(-0.126)]; whereas, higher death rates caused by diabetes and kidney (β = 0.472, 95% CI 0.089-0.855) and percentage of population aged older than 65 years (β = 0.407, 95% CI 0.013-0.802) were associated with a significant increase in AWIFR.

Conclusion

The COVID-19 infection fatality rates were strongly linked with the coverage of vaccination rate, effectiveness of government, and health burden related to chronic diseases. Therefore, proper policies for the improvement of vaccination coverage and support of vulnerable groups could substantially mitigate the burden of COVID-19.

Lag-time effects of vaccination on SARS-CoV-2 dynamics in German hospitals and intensive-care units

Background

The Efficacy and effectiveness of vaccination against SARS-CoV-2 have clearly been shown by randomized trials and observational studies. Despite these successes on the individual level, vaccination of the population is essential to relieving hospitals and intensive care units. In this context, understanding the effects of vaccination and its lag-time on the population-level dynamics becomes necessary to adapt the vaccination campaigns and prepare for future pandemics.

Methods

This work applied a quasi-Poisson regression with a distributed lag linear model on German data from a scientific data platform to quantify the effects of vaccination and its lag times on the number of hospital and intensive care patients, adjusting for the influences of non-pharmaceutical interventions and their time trends. We separately evaluated the effects of the first, second and third doses administered in Germany.

Results

The results revealed a decrease in the number of hospital and intensive care patients for high vaccine coverage. The vaccination provides a significant protective effect when at least approximately 40% of people are vaccinated, whatever the dose considered. We also found a time-delayed effect of the vaccination. Indeed, the effect on the number of hospital patients is immediate for the first and second doses while for the third dose about 15 days are necessary to have a strong protective effect. Concerning the effect on the number of intensive care patients, a significant protective response was obtained after a lag time of about 15-20 days for the three doses. However, complex time trends, e.g. due to new variants, which are independent of vaccination make the detection of these findings challenging.

Conclusion

Our results provide additional information about the protective effects of vaccines against SARS-CoV-2; they are in line with previous findings and complement the individual-level evidence of clinical trials. Findings from this work could help public health authorities efficiently direct their actions against SARS-CoV-2 and be well-prepared for future pandemics.

The added effect of non-pharmaceutical interventions and lifestyle behaviors on vaccine effectiveness against severe COVID-19 in Chile: a matched case-double control study

Background

All World Health Organization approved vaccines have demonstrated relatively high protection against moderate to severe COVID-19. Prospective vaccine effectiveness (VE) designs with first-hand data and population-based controls are nevertheless rare. Neighborhood compared to hospitalized controls, may differ in non-pharmaceutical interventions (NPI) compliance, which may influence VE results in real-world settings. We aimed to determine VE against COVID-19 intensive-care-unit (ICU) admission using hospital and community-matched controls in a prospective design.

Methods

We conducted a multicenter, observational study of matched cases and controls (1:3) in adults ≧18 from May to July 2021. For each case, a hospital control and two community controls were matched by age, gender, and hospital admission date or neighborhood of residence. Conditional logistic regression models were built, including interaction terms between NPIs, lifestyle behaviors, and vaccination status; the model’s β coefficients represent the added effect these terms had on COVID-19 VE.

Results

Cases and controls differed in several factors including education level, obesity prevalence, and behaviors such as compliance with routine vaccinations, use of facemasks, and routine handwashing. VE was 98·2% for full primary vaccination and 85·6% for partial vaccination when compared to community controls. VE tended to be higher when compared to community versus hospital controls, but the difference was not significant. A significant added effect to vaccination in reducing COVID-19 ICU admission was regular facemask use and VE was higher among individuals non-compliant with the national vaccine program, nor routine medical controls during the prior year.

Conclusion

VE against COVID-19 ICU admission in this stringent prospective case-double control study reached 98% two weeks after full primary vaccination, confirming the high effectiveness provided by earlier studies. Face mask use and hand washing were independent protective factors, the former adding additional benefit to VE. VE was significantly higher in subjects with increased risk behaviors.

COVID-19: years of life lost (YLL) and saved (YLS) as an expression of the role of vaccination

When evaluating vaccine efficacy, the conventional measures include reduction of risk of hospitalization and death. The number of patients dying with or without vaccination is often in the public spotlight. However, when evaluating public health interventions or the burden of disease, it is more illustrative to use mortality metrics taking into account also prematurity of the deaths, such as years of life lost (YLL) or years of life saved (YLS) thanks to the vaccination. We develop this approach for evaluation of the difference in YLL and YLS between COVID-19 victims with or without completed vaccination in the autumn pandemic wave (2021, October-December) in Czechia. For the analysis, individual data about all COVID-19 deaths in the country (N = 5797, during the studied period) was used. While 40.6% of the deaths are in cohorts with completed vaccination, this corresponds to 35.1% of years of life lost. The role of vaccination is expressed using YLS and hypothetical numbers of deaths. The registered number of deaths is approximately 3.5 times lower than it would be expected without vaccination. The results illustrate that vaccination is more effective in saving lives than suggested by simplistic comparisons.

Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand

During an epidemic, real-time estimation of the effective reproduction number supports decision makers to introduce timely and effective public health measures. We estimate the time-varying effective reproduction number, R_t , during Aotearoa New Zealand's August 2021 outbreak of the Delta variant of SARS-CoV-2, by fitting the publicly available EpiNow2 model to New Zealand case data. While we do not explicitly model non-pharmaceutical interventions or vaccination coverage, these two factors were the leading drivers of variation in transmission in this period and we describe how changes in these factors coincided with changes in R_t . Alert Level 4, New Zealand's most stringent restriction setting which includes stay-at-home measures, was initially effective at reducing the median R_t to 0.6 (90% CrI 0.4, 0.8) on 29 August 2021. As New Zealand eased certain restrictions and switched from an elimination strategy to a suppression strategy, R_t subsequently increased to a median 1.3 (1.2, 1.4). Increasing vaccination coverage along with regional restrictions were eventually sufficient to reduce R_t below 1. The outbreak peaked at an estimated 198 (172, 229) new infected cases on 10 November, after which cases declined until January 2022. We continue to update R_t estimates in real time as new case data become available to inform New Zealand's ongoing pandemic response.

Comparison of COVID-19 Resilience Index and Its Associated Factors across 29 Countries during the Delta and Omicron Variant Periods

Our study aims to compare the pandemic resilience index and explore the associated factors during the Delta and Omicron variant periods. In addition, the study aims to identify the characteristics of countries that had good performances. We analyzed observation data among 29 countries over the first eight weeks during the two periods of Delta and Omicron variant dominance. Data were extracted from open public databases. The Omicron variant caused a lowered mortality rate per 100,000 COVID-19 patients; however, it is still imposing a colossal burden on health care systems. We found the percentage of the population fully vaccinated and high government indices were significantly associated with a better resilience index in both the Delta and Omicron periods. In contrast, the higher death rate of cancers and greater years lived with disability (YLD) caused by low bone density were linked with poor resilience index in the Omicron periods. Over two periods of Delta and Omicron, countries with good performance had a lower death rate from chronic diseases and lower YLD caused by nutrition deficiency and PM2.5. Our findings suggest that governments need to keep enhancing the vaccine coverage rates, developing interventions for populations with chronic diseases and nutrition deficiency to mitigate COVID-19 impacts on these targeted vulnerable cohorts.

Association of Trends in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Seroprevalence and State-Issued Nonpharmaceutical Interventions: United States, 1 August 2020 to 30 March 2021

BACKGROUND

We assess if state-issued nonpharmaceutical interventions (NPIs) are associated with reduced rates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection as measured through anti-nucleocapsid (anti-N) seroprevalence, a proxy for cumulative prior infection that distinguishes seropositivity from vaccination.

METHODS

Monthly anti-N seroprevalence during 1 August 2020 to 30 March 2021 was estimated using a nationwide blood donor serosurvey. Using multivariable logistic regression models, we measured the association of seropositivity and state-issued, county-specific NPIs for mask mandates, gathering bans, and bar closures.

RESULTS

Compared with individuals living in a county with all three NPIs in place, the odds of having anti-N antibodies were 2.2 (95% confidence interval [CI]: 2.0-2.3) times higher for people living in a county that did not have any of the 3 NPIs, 1.6 (95% CI: 1.5-1.7) times higher for people living in a county that only had a mask mandate and gathering ban policy, and 1.4 (95% CI: 1.3-1.5) times higher for people living in a county that had only a mask mandate.

CONCLUSIONS

Consistent with studies assessing NPIs relative to COVID-19 incidence and mortality, the presence of NPIs were associated with lower SARS-CoV-2 seroprevalence indicating lower rates of cumulative infections. Multiple NPIs are likely more effective than single NPIs.

Dynamics of influenza-like illness under urbanization procedure and COVID-19 pandemic in the sub-center of Beijing during 2013-2021

Influenza‐like illness (ILI) varies in intensity year by year, generally keeping a stable pattern except for great changes of its epidemic pattern. Of the most impacting factors, urbanization has been suggested as shaping the intensity of influenza epidemics. Besides, growing evidence indicates the nonpharmaceutical interventions (NPIs) to severe acute respiratory syndrome coronavirus 2 offer great advantages in controlling infectious diseases. The present study aimed to evaluate the impact of urbanization and NPIs on the dynamic of ILI in Tongzhou, Beijing, during January 2013 to March 2021. ILI epidemiological surveillance data in Tongzhou district were obtained from Beijing Influenza Surveillance Network and separated into three periods of urbanization and four intervals of coronavirus disease 2019 pandemic. Standardized average incidence rates of ILI in each separate stages were calculated and compared by using Wilson method and time series model of seasonal ARIMA. Influenza seasonal outbreaks showed similar epidemic size and intensity before urbanization during 2013–2016. Increased ILI activity was found during the process of Tongzhou's urbanization during 2017–2019, with the rate difference of 2.48 (95% confidence interva [CI]: 2.44, 2.52) and the rate ratio of 1.75 (95% CI: 1.74, 1.76) of ILI incidence between preurbanization and urbanization periods. ILI activity abruptly decreased from the beginning of 2020 and kept at the bottom level almost in every epidemic interval. The top decrease in ILI activity by NPIs was shown in 5–14 years group in 2020–2021 influenza season, as 92.2% (95% CI: 78.3%, 95.2%). The results indicated that both urbanization and NPIs interrupted the epidemic pattern of ILI. We should pay more attention to public health when facing increasing population density, human contact, population mobility, and migration in the process of urbanization. NPIs and influenza vaccination should be implemented as necessary measures to protect people from common infectious diseases like ILI.