How Often Do I Need to Get Covid to Be Immune? Bloomberg
In Uganda the corona virus situation has rapidly improved and this shown with low levels of infection as evidenced with the statics released by Ministry of Health .
The rapid improvement in the corona virus situation has been basically due to the vaccination policy
Though the vaccination campaign is moving on well they are still challenges that prevent Uganda as a country from having a fully vaccinated population and this is due to things like religious beliefs and misinformation spread on social media and the arguments presented in this article show why most people in Uganda should support vaccination as a way of building a road to a new normal.
Currently am of the belief that vaccination is important in the day to day setting of the new normal and this is due to the fact that Covid vaccination is important in that it reduces the risk of infection and its clearly seen when once the body is inoculated with a corona virus vaccine the body immediately begins producing antibodies to fight coronavirus . The antibodies help the immune system to fight the corona virus in case a person has been exposed to the disease. Herd immunity is gained when people are vaccinated against Covid 19. Herd immunity refers to a population being protected against an infectious disease. Thus this suggests covid 19 vaccination reduces the chances of people getting infected and this in turn contributes to community protection reducing the likelihood of virus transmission.
After receiving the corona virus jab am certain that it also protects against severe illness and this clearly depicted with the exceptional studies of three vaccines Johnson and Johnson , AstraZeneca and Pfizer which have been medically proven to be effective in enabling the body to fight against severe illness that are caused by the Covid 19.
Inoculation against covid 19 has made me and many others have the confidence that its a safer way for people to build protection against covid ,and this is evidently seen when the body easily develops anti bodies that can respond to Covid 19 without somebody having to experience sickness.
I would strongly encourage Ugandans to watch and listen to the short adverts and plays on television and radio stations that talk about covid and this in the long run provides enlightenment to people and thus it encourages people to get inoculated against covid 19.
Social media is one of the greatest tools of influence in Uganda currently and this basically due to the persuasive and informative covid vaccination messages that can be used to influence people in Uganda to go and get inoculated against covid 19 with the vaccines available like Pfizer AstraZeneca , and Sinovac.
Self groups are one of the ways to encourage covid 19 vaccination and in this opinion leaders in the community can further more elaborate the dangers of covid 19 and also inform the masses about covid vaccines available in the country and this in turn will encourage people to take up vaccination in order to develop herd immunity that will help mitigate the spread of covid.
The path leading to a new normal with a world of covid 19 might be tough ,but in order for this transition to be smooth its important for you and me to support the vaccination programme to create a world that is free of the nuisance known as covid 19.
Mr Timothy Nsubuga is a Uganda Christian University Intern with the Public relations department.
By Julie M. Posted Apr 29, 2022 at 03:00
The entire world is facing an unprecedented health crisis due to the COVID-19 pandemic. There are more than 4,833,31055 cases of coronavirus worldwide and 6,199,842 deaths. Find out the results of countries and the evolution of the world in relation to the coronavirus pandemic on Friday, April 29, 2022.
at Thursday 28 April 2022The virus COVID-19 touch. Contact. Link 483,331,055 (-213,819) confirmed cases And I did in total 6,199,842 (+642) dead In the Globalism. We now use open data provided by Google.
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COVID-19 vaccinations in New Mexico picked up a bit more over the last month, despite the recent uptick in new cases.
More than 84,000 New Mexico adults have received COVID-19 vaccine doses over the previous four weeks, per the state Department of Healths website. Thats an increase of more than 65 percent from the prior month, when just over 29,000 doses were administered.
As of Tuesday, 84.1 percent of Sandoval County adults have completed their primary vaccination series. 82 percent of Bernalillo County adults have completed their primary vaccination series.
Statewide, 80 percent of adults have completed their primary vaccination series. For children ages 12 to 17, 61.9 percent of that age group has completed their primary vaccination series. For children ages five to 11, 32.5 percent of that age group has completed their primary vaccination series.
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UTAH (ABC4) Salt Lake Community recently lifted its COVID-19 vaccine requirement on Thursday, but some colleges in Utah are still requiring it.
University of Utah
Students who attend the University of Utah are required to be fully vaccinated against measles, mumps, rubella, and COVID-19.
Students who do not comply by either showing that they have received the vaccines or indicated a reasonable exemption will have a hold placed on their record that will prevent registration for future classes.
Utah State University
USU announced that all students would be required to be vaccinated for COVID-19 for Spring Semester 2022.
USU did however announce that for the Fall Semester 2022 students will not be required to have the vaccine.
Weber State University
Weber State University announced that it will require COVID-19 vaccinations for all students, except for concurrent enrollment students and students who qualify for an exemption, for the 2022 spring semester.
Westminster College
Westminster requires all its employees and students to be fully vaccinated or have an approved exemption.
Utah Valley University
UVU announced that the COVID-19 vaccine is required for all students for the Spring 2022 semester.
Dixie State and Southern Utah University
Both colleges have never required the COVID-19 for staff or students.
BYU and Ensign College
BYU announced they will discontinue the requirement for students and employees to report their vaccination status. The University says they still encourage vaccinations and boosters.
BYU and Ensign College have never required the vaccine for students but have always strongly encouraged it.
Starting in August 2020, the World Bank collaborated with the World Food Programme (WFP) and implemented nine (9) rounds of the Iraq High Frequency Phone Survey (IHFPS) as part of the WFPs monthly mVAM survey. For each round, more than 1,600 adult respondents from across Iraq (nationally representative) were interviewed using mobile phones. While the first six (6) rounds of the survey were implemented between August 2020 and January 2021, the last three rounds were conducted between June and August 2021.
This brief presents findings on Covid-19 vaccination disparities and hesitancy from the last three rounds of the IHFPS. Findings from the survey suggest a low but increasing vaccination trend among adult Iraqis but also high levels of resistance to the vaccine. Concern regarding possible side effects of the vaccine is the single most cited reason why a significant number of adults in Iraq remain hesitant to the Covid-19 vaccine. The survey also revealed notable disparities in vaccination and vaccine hesitancy. Iraqis with higher levels of education, with formal public-sector jobs, men, and those in urban areas are more likely to have been vaccinated than those with lower levels of education, with informal private-sector jobs and self-employment, women, and those in rural areas. Moreover, vaccination among the elderly, who are most vulnerable, and those with higher risk of exposure poorer Iraqis that are more likely to live in large households in cramped conditions and often do informal jobs that require direct interaction with people remain significantly low.
Huntington, WV (WOWK) Valley Health Systems is embracing National Minority Health Month and its theme to Give Your Community a Boost. The health system will use its COVID-19 Vaccine Equity Grant to ensure vaccine access in disproportionately affected communities, including racial and ethnic minority groups.
The $178,920 grant issued by the State of West Virginia will be used to focus on Cabell, Lincoln, Kanawha and Wayne counties.
Valley Health recognizes that a patients environment significantly impacts their health. Conditions like chronic lung diseases, diabetes, and obesity are associated with an increased risk of severe COVID-19 illness, and all of these conditions are prevalent in these counties.
Valley Health will offer mobile vaccination clinics and vaccine education at churches, food banks, public housing complexes, homeless shelters, recovery homes and more. Pharmacists, nurses and other healthcare professionals will administer the vaccines.
If an individual needs to travel to get their vaccine, Valley Health can provide gift cards to help with transportation costs.
The health system will also collaborate with trusted local leaders to host listening sessions in which the community can voice their concerns regarding vaccine hesitancy.
Additionally, Valley Health will seek to understand the needs of each disproportionately impacted group.
By focusing our implementation on presenting a multi-faceted approach, we can utilize this grant to provide vaccine hesitancy outreach to overcome barriers to meet the needs of individuals in our communities. This is especially important given racial, ethnic, and other disparities seen throughout the COVID-19 pandemic, and our efforts will be undertaken with a particular focus on these disparities.
Valley Health operates over 40 health centers and public health programs in southeastern West Virginia and southern Ohio. To learn more, visit the Valley Health Systems website or call your local health center.
Our key independent variable is the mean percentage of vaccine-related misinformation shared via Twitter at the U.S. state or county level. We used 55M tweets from the CoVaxxy dataset17, which were collected between January 4th and March 25th from the Twitter filtered stream API using a comprehensive list of keywords related to vaccines (see Supplementary Information). We leveraged the Carmen library29 to geolocate almost 1.67M users residing in 50 U.S. states, and a subset of approximately 1.15M users residing in over 1,300 counties. The larger set of users accounts for a total of 11M shared tweets. Following a consolidated approach in the literature25,26,27,28, we identified misinformation by considering tweets that contained links to news articles from a list of low-credibility websites compiled by a politically neutral thirdparty (see details in the Supplementary Information). We measured the prevalence of misinformation about vaccines in each region by (i)calculating the proportion of vaccine-related misinformation tweets shared by each geo-located account; and (ii)taking the average of this proportion across accounts within a specific region. The Twitter data collection was evaluated and deemed exempt from review by the Indiana University IRB (protocol 1102004860).
Our dependent variables include vaccination uptake rates at the state level and vaccine hesitancy at the state and county levels. Vaccination uptake is measured from the number of daily vaccinations administered in each state during the week of 1925 March 2021, and measurements are derived from the CDC9. Vaccine hesitancy rates are based on Facebook Symptom Surveys provided by the Delphi Group24 at Carnegie Mellon University. Vaccine hesitancy is likely to affect uptake rates, so we specify a longer time window to measure this variable, i.e., the period January 4thMarch 25th 2021. We computed hesitancy byinverting the proportion of individuals who either have already received a COVID vaccine or would definitely or probably choose to get vaccinated, if a vaccine were offered to them today. See Supplementary Information for further details.
There are no missing vaccine-hesitancy survey data at the state level. Observations are missing at the county level because Facebook survey data are available only when the number of respondents is at least 100. We use the same threshold on the minimum number of Twitter accounts geolocated in each county, resulting in a sample size of N=548 counties.
Our multivariate regression models adjust for six potential confounding factors: percentage of the population below the poverty line, percentage aged 65+, percentage of residents in each racial and ethnic group (Asian, Black, Native American, and Hispanic; White non-Hispanic is omitted), ruralurban continuum code (RUCC, county level only), number of COVID-19 deaths per thousand, and percentage Republican vote (in 10 percent units). Other covariates, including religiosity, unemployment rate, and population density, were also considered (full list in Supplementary Table S9).
We also conduct a large number of sensitivity analyses, including different specifications of the misinformation variable (with a restricted set of keywords and different thresholds for the inclusion of Twitter accounts) as well as logged versions of misinformation (to correct positive skew). These results are presented in Supplementary Information (Tables S3-S8).
We conduct multiple regression models predicting vaccination rate and vaccine hesitancy. Both dependent variables are normally distributed, making weighted least squares regression the appropriate model. Data are observed (aggregated) at the state or county level rather than at the individual level. Analytic weights are applied to give more influence to observations calculated over larger samples. The weights are inversely proportional to the variance of an observation such that the variance of the j-th observation is assumed to be 2/wj where wj is the weight. The weights are set equal to the size of the sample from which the average is calculated. We estimate weighted regression with the aweights command in Stata 16. In addition, because counties are nested hierarchically within states, we use cluster robust standard errors to correct for lack of independence between county-level observations.
We investigate Granger causality between vaccine hesitancy and misinformation by comparing two auto-regressive models. The first considers daily vaccine hesitancy rates (x) at time (t) in geographical region (r) (state or county):
$$x_{t,r} = mathop sum limits_{i}^{n} a_{i} x_{t - i,r} + epsilon_{t,r} ,$$
where (n) is the length of the time window. The second model adds daily misinformation rates per account as an exogenous variable (y):
$$x_{t,r} = mathop sum limits_{i}^{n} (a_{i} x_{t - i,r} + b_{i} y_{t - i,r} ) + epsilon^{{prime }}_{t,r} .$$
The variable (y) is said to be Granger causal30,31 on (x) if, in statistically significant terms, it reduces the error term (epsilon^{prime}_{t}), i.e., if
$$E_{{a,b}} = sumlimits_{{t,r}} {epsilon _{{t,r}} ^{2} } - sumlimits_{{t,r}}^{{}} {epsilon _{{t,r}}^{{prime 2}} } > 0,$$
meaning that misinformation rates y help forecast hesitancy rates x. We assume geographical regions to have equivalence and independence in terms of the way misinformation influences vaccine attitudes. Thus, we use the same parameters for (a_{i}) and (b_{i}) across all regions. We employ Ordinary Least Squares (using the Python statsmodels package version 0.11.1) linear regression to fit (a) and (b), standardizing the two variables and removing trends in the time series of each region. We select the value of the time window (n) that maximizes (E_{a,b}). For both counties and states, this was (n = 6)days and we present results using this value. We also tested nearby values of (n pm 2) to confirm these provide similar results. We use data points with at least 1 tweet and at least 100 survey responses for every day in the time window for the specified region.
The traditional statistic used to assess the significance of Granger Causality is the F-statistic30. However, in our case, there are several reasons why this is not appropriate. First, we have missing timewindows in some of our regions. Second, our assumptions of equivalence and independence for regions may not be accurate. For these reasons, we use a bootstrap method to estimate the expected random distribution of (E_{a,b}) with the time signal removed. To this end, we generate trial surrogates for (y) by randomly shuffling the data points. With each random reshuffled trial, we can then use the same procedure to calculate the reduction in error, which we call (E^{*}_{a,b}). The p-value of our Granger Causality analysis is then given by the proportion of trials ((N)=10,000) for which (E^{{*}{}}_{a,b} > E_{a,b}). A potential issue with Granger Causality analysis is that it may detect an underlying trend. We tested for this by linearly detrending both time series before running the Granger analysis, finding similar results.
