scholarly journals Simulating the impact of vaccination rates on the initial stages of a COVID-19 outbreak in New Zealand (Aotearoa) with a stochastic model

2021 ◽  
Author(s):  
Leighton M Watson
Keyword(s):  
New Zealand ◽  
Stochastic Model ◽  
Process Model ◽  
Branching Process ◽  
Total Population ◽  
Median Number ◽  
Vaccination Rate ◽  
Vaccination Rates ◽  
The Impact ◽  
Different Levels

Aim: The August 2021 COVID-19 outbreak in Auckland has caused the New Zealand government to transition from an elimination strategy to suppression, which relies heavily on high vaccination rates in the population. As restrictions are eased and as COVID-19 leaks through the Auckland boundary, there is a need to understand how different levels of vaccination will impact the initial stages of COVID-19 outbreaks that are seeded around the country. Method: A stochastic branching process model is used to simulate the initial spread of a COVID-19 outbreak for different vaccination rates. Results: High vaccination rates are effective at minimizing the number of infections and hospitalizations. Increasing vaccination rates from 20% (approximate value at the start of the August 2021 outbreak) to 80% (approximate proposed target) of the total population can reduce the median number of infections that occur within the first four weeks of an outbreak from 1011 to 14 (25th and 75th quantiles of 545-1602 and 2-32 for V=20% and V=80%, respectively). As the vaccination rate increases, the number of breakthrough infections (infections in fully vaccinated individuals) and hospitalizations of vaccinated individuals increases. Unvaccinated individuals, however, are 3.3x more likely to be infected with COVID-19 and 25x more likely to be hospitalized. Conclusion: This work demonstrates the importance of vaccination in protecting individuals from COVID-19, preventing high caseloads, and minimizing the number of hospitalizations and hence limiting the pressure on the healthcare system.

scholarly journals Likelihood of infecting or getting infected with COVID-19 as a function of vaccination status, as investigated with a stochastic model for New Zealand (Aotearoa)

2021 ◽  
Author(s):  
Leighton M Watson
Keyword(s):  
New Zealand ◽  
Process Model ◽  
Branching Process ◽  
Total Population ◽  
Population Level ◽  
Vaccination Rate ◽  
Vaccination Status ◽  
Government Action ◽  
Vaccination Rates ◽  
Eligible Population

Aim: The New Zealand government is transitioning from the Alert Level framework, which relies on government action and population level controls, to the COVID-19 Protection Framework, which relies on vaccination rates and allows for greater freedoms (for the vaccinated). As restrictions are eased, there is significant interest in understanding the relative risk of spreading COVID-19 posed by unvaccinated and vaccinated individuals. Methods: A stochastic branching process model is used to simulate the spread of COVID-19 for outbreaks seeded by unvaccinated or vaccinated individuals. The likelihood of infecting or getting infected with COVID-19 is calculated based on vaccination status. Results: A vaccinated traveler infected with COVID-19 is 9x less likely to seed an outbreak than an unvaccinated traveler infected with COVID-19. For a vaccination rate of 50%, unvaccinated individuals are responsible for 87% of all infections whereas 3% of infections are from vaccinated to vaccinated. When normalized by population, a vaccinated individual is 6.8x more likely to be infected by an unvaccinated individual than by a vaccinated individual. For a total population vaccination rate of 78.7%, which is equivalent to the 90% vaccination target for the eligible population (over 12 years old), this means that vaccinated individuals are 1.9x more likely to be infected by an unvaccinated individual than by a vaccinated, even though there are 3.7x more vaccinated individuals in the population. Conclusions: This work demonstrates that most new infections are caused by unvaccinated individuals. These simulations illustrate the importance of vaccination in stopping individuals from becoming infected with COVID-19 and in preventing onward transmission.

scholarly journals Overview: The international group for indigenous health measurement and COVID-19

2021 ◽  
pp. 1-6
Author(s):  
Michele Connolly ◽  
Kalinda Griffiths ◽  
John Waldon ◽  
Malcolm King ◽  
Alexandra King ◽  
...  
Keyword(s):  
New Zealand ◽  
Total Population ◽  
Indigenous Health ◽  
Health Data ◽  
Indigenous Populations ◽  
Health Measurement ◽  
Country Group ◽  
International Group ◽  
The Us ◽  
The Impact

The International Group for Indigenous Health Measurement (IGIHM) is a 4-country group established to promote improvements in the collection, analysis, interpretation and dissemination of Indigenous health data, including the impact of COVID-19. This overview provides data on cases and deaths for the total population as well as the Indigenous populations of each country. Brief summaries of the impact are provided for Canada and New Zealand. The Overview is followed by. separate articles with more detailed discussion of the COVID-19 experience in Australia and the US.

scholarly journals The Effect of Vaccination Rates on the Infection of COVID-19 under the Vaccination Rate below the Herd Immunity Threshold

2021 ◽  
Vol 18 (14) ◽  
pp. 7491
Author(s):  
Yi-Tui Chen
Keyword(s):  
Infection Rate ◽  
Herd Immunity ◽  
The United States ◽  
Vaccination Rate ◽  
Infection Rates ◽  
Vaccination Rates ◽  
The United Kingdom ◽  
The World ◽  
Almost All ◽  
The Impact

Although vaccination is carried out worldwide, the vaccination rate varies greatly. As of 24 May 2021, in some countries, the proportion of the population fully vaccinated against COVID-19 has exceeded 50%, but in many countries, this proportion is still very low, less than 1%. This article aims to explore the impact of vaccination on the spread of the COVID-19 pandemic. As the herd immunity of almost all countries in the world has not been reached, several countries were selected as sample cases by employing the following criteria: more than 60 vaccine doses per 100 people and a population of more than one million people. In the end, a total of eight countries/regions were selected, including Israel, the UAE, Chile, the United Kingdom, the United States, Hungary, and Qatar. The results find that vaccination has a major impact on reducing infection rates in all countries. However, the infection rate after vaccination showed two trends. One is an inverted U-shaped trend, and the other is an L-shaped trend. For those countries with an inverted U-shaped trend, the infection rate begins to decline when the vaccination rate reaches 1.46–50.91 doses per 100 people.

Use Of A Mobile Cart Influenza Program For Vaccination Of Hospital Employees

2004 ◽  
Vol 25 (11) ◽  
pp. 918-922 ◽  
Author(s):  
Catherine Sartor ◽  
Herve Tissot-Dupont ◽  
Christine Zandotti ◽  
Francoise Martin ◽  
Pierre Roques ◽  
...  
Keyword(s):  
Influenza Vaccination ◽  
Vaccination Rate ◽  
Associate Professor ◽  
University Hospital ◽  
Vaccination Program ◽  
Vaccination Rates ◽  
Vaccination Acceptance ◽  
The Impact ◽  
Haenszel Test

AbstractObjective:Rates of annual influenza vaccination of healthcare workers (HCWs) remained low in our university hospital. This study was conducted to evaluate the impact of a mobile cart influenza vaccination program on HCW vaccination.Methods:From 2000 to 2002, the employee health service continued its annual influenza vaccination program and the mobile cart program was implemented throughout the institution. This program offered influenza vaccination to all employees directly on the units. Each employee completed a questionnaire. Vaccination rates were analyzed using the Mantel–Haenszel test.Results:The program proposed vaccination to 50% to 56% of the employees. Among the nonvaccinated employees, 52% to 53% agreed to be vaccinated. The compliance with vaccination varied from 61% to 77% among physicians and medical students and from 38% to 55% among nurses and other employees. Vaccination of the chief or associate professor of the unit was associated with a higher vaccination rate of the medical staff (P < .01). Altogether, the vaccination program led to an increase in influenza vaccination among employees from 6% in 1998 and 7% in 1999 before the mobile cart program to 32% in 2000, 35% in 2001, and 32% in 2002 (P < .001).Conclusions:The mobile cart program was associated with a significantly increased vaccination acceptance. Our study was able to identify HCW groups for which the mobile cart was effective and highlight the role of the unit head in its success.

scholarly journals The influence of a community intervention on influenza vaccination knowledge and behavior among diabetic patients

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Lili Tao ◽  
Ming Lu ◽  
Xiaoning Wang ◽  
Xiaoyan Han ◽  
Shuming Li ◽  
...  
Keyword(s):  
Influenza Vaccine ◽  
Influenza Vaccination ◽  
Community Intervention ◽  
Vaccination Rate ◽  
Control Group ◽  
Diabetic Patients ◽  
Vaccination Rates ◽  
Significant Difference ◽  
The Impact ◽  
Experimental Group

Abstract Background This study was conducted to evaluate the impact of a comprehensive community intervention on cognition and inoculation behaviors of diabetic patients immunized with influenza vaccine. Methods A total of 1538 diabetic patients aged 35 years and above for outpatient visits and follow-up treatments were selected from six community health service centers (three for the experimental group, and the other three for the control group) in Chaoyang District, Beijing. Comprehensive interventions applied to the experimental group include patient intervention and community climate interventions. We compared the total awareness of influenza vaccine knowledge and influenza vaccination rates between the two groups before and after the intervention. Results Before the intervention, the total awareness rate of influenza vaccine in the experimental group and the control group was similar (50.6 and 50.2%, respectively. P = 0.171). After the intervention, the awareness rate of influenza vaccine in the experimental group and the control group increased. The amplitude of the increase was similar (70.3 and 70.1%, respectively. P = 0.822,). Before the intervention, there was no significant difference in the influenza vaccination rate between the experimental group and the control group (29.0 and 26.8%, respectively. P = 0.334). After the intervention, the vaccination rate of the experimental group was higher than that of the control group. The difference was statistically significant (The vaccination rate 45.8 and 27.4% for the experimental group and the control group, respectively. P < 0.001). Conclusion Comprehensive community interventions had a positive effect on vaccination in diabetic patients. Trial registration ChiCTR1900025194, registered in Aug,16th, 2019. Retrospectively registered.

scholarly journals Higher COVID-19 vaccination rates are linked to decreased county-level COVID-19 incidence across USA

2021 ◽  
Author(s):  
Arjun Puranik ◽  
AJ Venkatakrishnan ◽  
Colin Pawlowski ◽  
Bharathwaj Raghunathan ◽  
Eshwan Ramudu ◽  
...  
Keyword(s):  
Incidence Rate ◽  
Vaccine Efficacy ◽  
Vaccine Dose ◽  
The United States ◽  
Vaccination Rate ◽  
Incidence Rates ◽  
P Value ◽  
County Level ◽  
Vaccination Rates ◽  
The Impact

Real world evidence studies of mass vaccination across health systems have reaffirmed the safety1 and efficacy2,3 of the FDA-authorized mRNA vaccines for COVID-19. However, the impact of vaccination on community transmission remains to be characterized. Here, we compare the cumulative county-level vaccination rates with the corresponding COVID-19 incidence rates among 87 million individuals from 580 counties in the United States, including 12 million individuals who have received at least one vaccine dose. We find that cumulative county-level vaccination rate through March 1, 2021 is significantly associated with a concomitant decline in COVID-19 incidence (Spearman correlation ρ = −0.22, p-value = 8.3e-8), with stronger negative correlations in the Midwestern counties (ρ = −0.37, p-value = 1.3e-7) and Southern counties (ρ = −0.33, p-value = 4.5e-5) studied. Additionally, all examined US regions demonstrate significant negative correlations between cumulative COVID-19 incidence rate prior to the vaccine rollout and the decline in the COVID-19 incidence rate between December 1, 2020 and March 1, 2021, with the US western region being particularly striking (ρ = −0.66, p-value = 5.3e-37). However, the cumulative vaccination rate and cumulative incidence rate are noted to be statistically independent variables, emphasizing the need to continue the ongoing vaccination roll out at scale. Given confounders such as different coronavirus restrictions and mask mandates, varying population densities, and distinct levels of diagnostic testing and vaccine availabilities across US counties, we are advancing a public health resource to amplify transparency in vaccine efficacy monitoring (https://public.nferx.com/covid-monitor-lab/vaccinationcheck). Application of this resource highlights outliers like Dimmit county (Texas), where infection rates have increased significantly despite higher vaccination rates, ostensibly owing to amplified travel as a “vaccination hub”; as well as Henry county (Ohio) which encountered shipping delays leading to postponement of the vaccine clinics. This study underscores the importance of tying the ongoing vaccine rollout to a real-time monitor of spatio-temporal vaccine efficacy to help turn the tide of the COVID-19 pandemic.

scholarly journals Counties with Lower Insurance Coverage and Housing Problems Are Associated with Both Slower Vaccine Rollout and Higher COVID-19 Incidence

Vaccines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 973
Author(s):  
Gregory Donadio ◽  
Mayank Choudhary ◽  
Emily Lindemer ◽  
Colin Pawlowski ◽  
Venky Soundararajan
Keyword(s):  
Socioeconomic Factors ◽  
Insurance Coverage ◽  
Vaccination Rate ◽  
Incidence Rates ◽  
P Value ◽  
Strongly Correlated ◽  
Marginalized Communities ◽  
Vaccination Rates ◽  
Using Data ◽  
The Impact

Equitable vaccination distribution is a priority for outcompeting the transmission of COVID-19. Here, the impact of demographic, socioeconomic, and environmental factors on county-level vaccination rates and COVID-19 incidence changes is assessed. In particular, using data from 3142 US counties with over 328 million individuals, correlations were computed between cumulative vaccination rate and change in COVID-19 incidence from 1 December 2020 to 6 June 2021, with 44 different demographic, environmental, and socioeconomic factors. This correlation analysis was also performed using multivariate linear regression to adjust for age as a potential confounding variable. These correlation analyses demonstrated that counties with high levels of uninsured individuals have significantly lower COVID-19 vaccination rates (Spearman correlation: −0.460, p-value: <0.001). In addition, severe housing problems and high housing costs were strongly correlated with increased COVID-19 incidence (Spearman correlations: 0.335, 0.314, p-values: <0.001, <0.001). This study shows that socioeconomic factors are strongly correlated to both COVID-19 vaccination rates and incidence rates, underscoring the need to improve COVID-19 vaccination campaigns in marginalized communities.

scholarly journals The Impact of Healthcare Worker Influenza Vaccination on Nosocomial Influenza in a Tertiary Hospital: An Ecological Study

2020 ◽  
Author(s):  
Wycliffe Enli Wei ◽  
Stephanie Fook-Chong ◽  
Wen Kai Chen ◽  
Maciej Piotr Chlebicki ◽  
Wee Hoe Gan
Keyword(s):  
Protective Effect ◽  
Influenza Vaccination ◽  
Healthcare Worker ◽  
Healthcare Workers ◽  
Ecological Study ◽  
Tertiary Hospital ◽  
Vaccination Rate ◽  
Incidence Rates ◽  
Vaccination Rates ◽  
The Impact

Abstract Background: To protect hospitalized patients who are more susceptible to complications of influenza, seasonal influenza vaccination of healthcare workers (HCW) has been recommended internationally. However, its effectiveness is still being debated. To assess the effectiveness of HCW influenza vaccination, we performed an ecological study to evaluate the association between healthcare worker influenza vaccination and the incidence of nosocomial influenza in a tertiary hospital within Singapore between 2013-2018. Methods: Nosocomial influenza was defined by influenza among inpatients diagnosed 7 days or more post-admission by laboratory testing, while healthcare worker influenza vaccination rate was defined as the proportion of healthcare workers that was vaccinated at the end of each annual seasonal vaccination exercise. A modified Poisson regression was performed to assess the association between the HCW vaccination rates and monthly nosocomial influenza incidence rates. Results: Nosocomial influenza incidence rates followed the trend of non-nosocomial influenza, showing a predominant mid-year peak. Across 2,480,010 patient-days, there were 256 nosocomial influenza cases (1.03 per 10,000 patient-days). Controlling for background influenza activity and the number of influenza tests performed, no statistically significant association was observed between vaccination coverage and nosocomial influenza incidence rate although a protective effect was suggested (IRR 0.89, 95%CI:0.69-1.15, p =0.37). Conclusion: No significant association was observed between influenza vaccination rates and nosocomial influenza incidence rates, although a protective effect was suggested. Aligning local HCW vaccine timing and formulation to that of the Southern Hemisphere may improve effectiveness. HCW vaccination remains important but demonstrating its effectiveness in preventing nosocomial influenza is challenging.

scholarly journals Simulation of the impact of people mobility, vaccination rate, and virus variants on the evolution of Covid-19 outbreak in Italy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Corrado Spinella ◽  
Antonio Massimiliano Mio
Keyword(s):  
Diffusion Coefficient ◽  
Time Evolution ◽  
Dynamical Evolution ◽  
Vaccination Rate ◽  
Single Person ◽  
Potential Impact ◽  
The Impact ◽  
Index Effect ◽  
Daily Displacement ◽  
Different Levels

AbstractWe have further extended our compartmental model describing the spread of the infection in Italy. As in our previous work, the model assumes that the time evolution of the observable quantities (number of people still positive to the infection, hospitalized and fatalities cases, healed people, and total number of people that has contracted the infection) depends on average parameters, namely people diffusion coefficient, infection cross-section, and population density. The model provides information on the tight relationship between the variation of the reported infection cases and a well-defined observable physical quantity: the average number of people that lie within the daily displacement area of any single person. With respect to our previous paper, we have extended the analyses to several regions in Italy, characterized by different levels of restrictions and we have correlated them to the diffusion coefficient. Furthermore, the model now includes self-consistent evaluation of the reproduction index, effect of immunization due to vaccination, and potential impact of virus variants on the dynamical evolution of the outbreak. The model fits the epidemic data in Italy, and allows us to strictly relate the time evolution of the number of hospitalized cases and fatalities to the change of people mobility, vaccination rate, and appearance of an initial concentration of people positives for new variants of the virus.

scholarly journals Mobility network reveals the impact of geographic vaccination heterogeneity on COVID-19

2021 ◽  
Author(s):  
Yuan Yuan ◽  
Eaman Jahani ◽  
Shengjia Zhao ◽  
Yong-Yeol Ahn ◽  
Alex Pentland
Keyword(s):  
Computational Models ◽  
Negative Impact ◽  
Vaccination Campaign ◽  
Vaccination Rate ◽  
Control Measures ◽  
Vaccine Uptake ◽  
Vaccination Rates ◽  
The Impact ◽  
Hub Effect ◽  
The U.S

ABSTRACTMassive vaccination is one of the most effective epidemic control measures. Because one’s vaccination decision is shaped by social processes (e.g., socioeconomic sorting and social contagion), the pattern of vaccine uptake tends to show strong social and geographical heterogeneity, such as urban-rural divide and clustering. Yet, little is known to what extent and how the vaccination heterogeneity affects the course of outbreaks. Here, leveraging the unprecedented availability of data and computational models produced during the COVID-19 pandemic, we investigate two network effects—the “hub effect” (hubs in the mobility network usually have higher vaccination rates) and the “homophily effect” (neighboring places tend to have similar vaccination rates). Applying Bayesian deep learning and fine-grained simulations for the U.S., we show that stronger homophily leads to more infections while a stronger hub effect results in fewer cases. Our simulation estimates that these effects have a combined net negative impact on the outcome, increasing the total cases by approximately 10% in the U.S. Inspired by these results, we propose a vaccination campaign strategy that targets a small number of regions to further improve the vaccination rate, which can reduce the number of cases by 20% by only vaccinating an additional 1% of the population according to our simulations. Our results suggest that we must examine the interplay between vaccination patterns and mobility networks beyond the overall vaccination rate, and that the government may need to shift policy focus from overall vaccination rates to geographical vaccination heterogeneity.

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