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Post by Admin on May 26, 2020 6:48:02 GMT
Unfortunately, Sweden's capital will not reach this milestone in May. "No that will not happen," Tegnell said on Monday in an email to NPR. "Current investigations show different numbers, but [Stockholm's immunity rate] is likely lower [than 30%]. As you might be aware, there is a problem with measuring immunity for this virus." Sweden's Public Health Agency last week released the initial findings of an ongoing antibodies study that showed that only 7.3% of people in Stockholm had developed antibodies against COVID-19 by late April. Tegnell later described the study's figure as a "bit lower than we'd thought," adding that the study represented a snapshot of the situation some weeks ago and he believed that by now "a little more than 20%" of Stockholm's population should have contracted the virus. It's the same figure that he mentioned in the CNBC interview over a month ago. The study's results have provided further fuel for the critics of the Swedish approach. With 39.26 deaths per 100,000, Sweden's mortality rate is not only higher than that of the U.S. (29.87 deaths per 100,000) but also exponentially higher than those of its neighbors Norway (4.42 per 100,000) and Finland (5.56 per 100,000), which both enacted strict lockdown measures, according to data from Johns Hopkins University. A protest against the government's anti-lockdown strategy at Stockholm's Sergels Torg square attracted a few dozen people on Sunday. One of the protest signs read, "In memory of everyone who Sweden couldn't save with its strategy." The Swedish Embassy in Washington, D.C., said in a statement provided to NPR that the country's government as well as its public health agency believe it's still "far too early to draw any clear conclusions or comparisons connected to the coronavirus pandemic," but "we are open with that the strategy has failed to protect the elderly living in care homes." Nearly half of the country's more than 4,000 COVID-19 deaths have occurred in elderly care facilities. A majority of Swedes, 63%, according to one recent poll, support the measures Tegnell's agency has recommended. For some anti-lockdown protesters in the U.S., "Be like Sweden" has become a rallying cry at protest rallies. But given the political, social and cultural differences between the two countries, simply adopting the Swedish model might not work. "Every country and region is different, and every country and region needs do what they think is best for their place," Olofsdotter said. "In Sweden, there's a fairly big trust between the population and the government and its agencies and vice versa. Of course, if we can be an inspiration to others, and they find measures that we have used useful in either a state or region, that's good, because we are all in this together." Even without a nationwide lockdown, the Sweden's economy has taken a hit as people continue to follow their government's guidelines and stay at home. Google records indicated that trips to retail and recreational destinations in Stockholm are down 23%, while passenger numbers on public transit declined 29% between March 28-May 9. Sweden's central bank, the Riksbank, provided two potential scenarios for the country's economic outlook in 2020. "Despite the comprehensive measures both in Sweden and abroad, the economic consequences of the pandemic will be considerable. The consequences for the economy will vary depending on how long the spread of infection continues and on how long the restrictions implemented to slow it down are in place," the Riksbank said in a statement in April. Both scenarios predict a rise in unemployment rate and a contraction of the country's gross domestic product. The central bank expects unemployment to rise from 6.8% to 10.1% and GDP to shrink by up to 9.7% this year as result of the pandemic. Earlier this month, Tegnell admitted that he is not sure Sweden's strategy was the right call. "I'm not convinced at all - we are constantly thinking about this," he told Swedish newspaper Aftonbladet.
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Post by Admin on Jun 1, 2020 21:22:32 GMT
The number of novel coronavirus infections in Sweden increased by 272 in the past 24 hours, bringing the total to 37,814 (378 infections per 100,000 people), while the number of coronavirus-related deaths in the country climbed by eight to reach 4,403, the national healthcare agency reported Monday. Sweden reports that 2,083 patients are in ICU. The highest numbers of infections (12,208) and fatalities (2,058) are identified in the capital region. The number of coronavirus-related deaths in the country climbed by eight to reach 4,403, according to the national healthcare agency. Swedish Prime Minister Stefan Lofven announced on Monday that the country will launch an inquiry into its handling of the pandemic before the summer. The decision comes amid rising criticisms from opposition parties on both Sweden's right and left. Sweden's two biggest opposition parties – the conservative Moderate Party and the populist Sweden Democrats – urged on Friday for an independent commission to be in place before the summer to investigate the country's response to the outbreak. Lofven had previously said a special commission would be appointed once the pandemic is over but he and his Social Democrats party – which rule in coalition with the Greens – have faced mounting pressure to take action sooner. "We need to take an overall approach to see how it has worked at national, regional and local levels," Lofven told the Swedish daily Aftonbladet. "We will make a decision for a commission before the summer," he added. High fatalities in nursing homes Sweden has lost more than 4,000 people to the pandemic, with roughly half of them having been nursing home residents. Read more: Sweden says exclusion from Nordic travel zone would be 'political' Testing in Sweden has also been significantly lower than in other Scandinavian countries – reaching only a third of the government's target of 100,000 tests per week. Whe the mortality rate over the course of the coronavirus outbreak has been lower than in some countries which had stricter measurements to contain the virus, Sweden had the highest number of COVID-19 related deaths in Europe relative to the size of the population through parts of May. Read more: Denmark asks lovestruck travelers to show proof of romance The Nordic country has made global headlines for its more liberal approach to handling the coronavirus. Sweden has relied on voluntary measures based on hygiene and social distancing practices and kept most businesses, restaurants and schools open even during the peak of the pandemic. The commission would also be likely to assess Sweden's economic response to the crisis.
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Post by Admin on Jun 6, 2020 19:15:18 GMT
The epidemiologist who led Sweden's controversial COVID-19 response, which did not involve a strict lockdown, now says that the country should have done more to stop the spread of the virus, according to news reports. "If we were to run into the same disease, knowing exactly what we know about it today, I think we would end up doing something in between what Sweden did and what the rest of the world has done," Anders Tegnell, the state epidemiologist of the Public Health Agency of Sweden, told Swedish Radio on June 3, according to Reuters. As compared with other countries in Europe, Sweden took a comparatively relaxed approach to COVID-19 by choosing not to institute strict lockdown policies, NPR reported in April. With no mandatory quarantine in place, museums, bars, restaurants, gyms, malls, schools and nightclubs remained open while residents were encouraged to follow guidelines for personal hygiene and social distancing. Health officials also banned gatherings of 50 or more people, recommended that residents avoid nonessential travel and encouraged those over age 70 to stay home as much as possible. In late March, Sweden prohibited residents from visiting nursing homes, but the measure did not prevent the virus from reaching elderly care facilities throughout the country. As of June 4, Sweden has reported more than 4,500 deaths associated with the virus, according to the Johns Hopkins virus dashboard, and about half of those deaths occurred among elderly people living in nursing homes, Reuters reported. The trend had already emerged in April, when Sweden's ambassador to the U.S., Karin Ulrika Olofsdotter, told NPR, "Once we know how the virus got into our elderly care facilities, the government can make recommendations and take measures to try to stop that, because that is the biggest tragedy of all this, that it has gotten into the nursing homes." Now, more than a month later, nursing homes still bear the brunt of Sweden's COVID-19 deaths. "We have to admit that when it comes to elderly care and the spread of infection, that has not worked," Prime Minister Stefan Löfven told Swedish newspaper The Aftonbladet Daily, according to Reuters. "Too many old people have died here."
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Post by Admin on Jun 24, 2020 7:24:21 GMT
Mathematicians from the University of Nottingham and University of Stockholm devised a simple model categorizing people into groups reflecting age and social activity level. When differences in age and social activity are incorporated in the model, the herd immunity level reduces from 60% to 43%. The figure of 43% should be interpreted as an illustration rather than an exact value or even a best estimate. The research has been published today in Science. Herd immunity happens when so many people in a community become immune to an infectious disease that is stops the disease from spreading. This happens by people contracting the disease and building up natural immunity and by people receiving a vaccine. When a large percentage of the population becomes immune to a disease, the spread of that disease slows down or stops and the chain of transmission is broken. This research takes a new mathematical approach to estimating the herd immunity figure for a population to an infectious disease, such as the current COVID-19 pandemic. The herd immunity level is defined as the fraction of the population that must become immune for disease spreading to decline and stop when all preventive measures, such as social distancing, are lifted. For COVID-19 it is often stated that this is around 60%, a figure derived from the fraction of the population that must be vaccinated (in advance of an epidemic) to prevent a large outbreak. The figure of 60% assumes that each individual in the population is equally likely to be vaccinated, and hence immune. However, that is not the case if immunity arises as a result of disease spreading in a population consisting of people with many different behaviors. Professor Frank Ball from the University of Nottingham participated in the research and explains: "By taking this new mathematical approach to estimating the level for herd immunity to be achieved we found it could potentially be reduced to 43% and that this reduction is mainly due to activity level rather than age structure. The more socially active individuals are then the more likely they are to get infected than less socially active ones, and they are also more likely to infect people if they become infected. Consequently, the herd immunity level is lower when immunity is caused by disease spreading than when immunity comes from vaccination. Our findings have potential consequences for the current COVID-19 pandemic and the release of lockdown and suggests that individual variation (e.g. in activity level) is an important feature to include in models that guide policy." More information: A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2, Science (2020).
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Post by Admin on Jun 25, 2020 5:57:39 GMT
A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2 Tom Britton1,*, Frank Ball2, Pieter Trapman1
Science 23 Jun 2020: eabc6810 DOI: 10.1126/science.abc6810
Abstract Despite various levels of preventive measures, in 2020 many countries have suffered severely from the coronavirus 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. We show that population heterogeneity can significantly impact disease-induced immunity as the proportion infected in groups with the highest contact rates is greater than in groups with low contact rates. We estimate that if R0 = 2.5 in an age-structured community with mixing rates fitted to social activity then the disease-induced herd immunity level can be around 43%, which is substantially less than the classical herd immunity level of 60% obtained through homogeneous immunization of the population. Our estimates should be interpreted as an illustration of how population heterogeneity affects herd immunity, rather than an exact value or even a best estimate.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally despite the many different preventive measures that have been put in place to reduce transmission. Some countries aimed for suppression by extreme quarantine measures (lockdown), and others for mitigation by slowing the spread using certain preventive measures in combination with protection of the vulnerable (1). An important question for both policies has been when to lift some or all the restrictions. A closely related question is if and when herd immunity can be achieved. Herd immunity is defined as a level of population immunity such that disease spreading will decline and stop even after all preventive measures have been relaxed. If all preventive measures are relaxed when the immunity level from infection is below the herd immunity level, then a second wave of infection may start once restrictions are lifted.
By 1 May 2020, some regions and countries reached high estimates for the population immunity level, with for example 26% infected (and large confidence interval) in metropolitan Stockholm region, as based on a mathematical model (2). At the same time, population studies in Spain show that in second half of May over 10% of the population of Madrid had antibodies for coronavirus disease 2019 (COVID-19) (3). It is debatable whether (classical) herd immunity for COVID-19, which is believed to lie between 50% and 75%, can be achieved without unacceptably high case fatality rates (4–6).
The definition of classical herd immunity originates from mathematical models for the impact of vaccination. The classical herd immunity level hC is defined as hC = 1 – 1/R0, where R0 is the basic reproduction number, defined as the average number of new infections caused by a typical infected individual during the early stage of an outbreak in a fully susceptible population (7). Thus, if a fraction v is vaccinated (with a vaccine giving 100% immunity) and vaccinees are selected uniformly in the community, then the new reproduction number is Rv = (1 – v)R0. From this the critical vaccination coverage vc = 1 – 1/R. So, if at least this fraction is vaccinated and hence immune, the community has reached herd immunity, as Rv ≤ 1, and no outbreak can take place. If the vaccine is not perfect but instead reduces susceptibility by a fraction E (so E = 1 corresponds to 100% efficacy), then the critical vaccination coverage is given by vc = E–1(1 – 1/R0) (7), implying that a bigger fraction needs to be vaccinated if the vaccine is not perfect.
No realistic model will depict human populations as homogenous, there are many heterogeneities in human societies that will influence virus transmission. Here, we illustrate how population heterogeneity can cause significant heterogeneity among the people infected during the course of an infectious disease outbreak, with consequent impact on the herd immunity level and the performance of exit policies aimed at minimizing the risk of future infection spikes.
One of the simplest of all epidemic models is to assume a homogeneously mixing population in which all individuals are equally susceptible, and equally infectious if they become infected. Before becoming infectious, infected individuals first go through a latent/exposed period, i.e., a Susceptible-Exposed-Infected-Recovered (SEIR) model (7). The basic reproduction number R0 denotes the average number of infectious contacts an infected individual has before recovering and becoming immune (or dying). An infectious contact is defined as one close enough to infect the other individual if this individual is susceptible (contacts with already infected individuals have no effect).
To this simple model we add two important features known to play an important role in disease spreading (the model is described in full detail in the Supplementary material). The first is to include age structure by dividing the community into different age cohorts, with heterogeneous mixing between the different age cohorts. We categorize a community into six age groups and fit contact rates derived from an empirical study of social contacts (8) (see Supplementary material for details on the community structure). The person-to-person infectious contact rate between two individuals depends on the age groups of both individuals. The average number of infectious contacts an infected person in age group, i, has with individuals in (another or the same) age group, j, now equals aijπj, where aij reflects both how much an i-individual has contact with a specific j-individual. It also reflects the typical infectivity of i-individuals and susceptibility of j-individuals. The population fraction of individuals belonging to age cohort j is denoted by πj.
The second population structure element categorizes individuals according to their social activity level. A common way to do this is by means of network models (e.g., (9)). Here we take a simpler approach and categorize individuals into three different activity levels, which are arbitrary and chosen for illustration purposes: 50% of each age cohort have normal activity, 25% have low activity corresponding to half as many contacts compared to normal activity, and 25% have high activity corresponding to twice as many contacts as normal activity. By this we mean that, for example, a high-activity individual in age group i on average has 2*aijπj*0.5*0.25 infectious contacts with low-activity individuals of age group j. The factor 2 comes from the infective having high activity, the factor 0.5 from the contacted person having low activity, and the factor 0.25 from low-activity individuals making up 25% of each age cohort. The basic reproduction number R0 for this model is given by the dominant eigenvalue of the (next-generation) matrix M having these elements as its entries. (7).
The final fractions of the different groups in the population becoming infected in the epidemic are obtained by solving a set of equations (the final-size equations given in the supplementary material). To be able to say something about the time evolution of the epidemic we assume a classical SEIR epidemic model. More precisely, we assume that individuals who get infected are initially latent for a mean of 3 days, followed by an infectious period of a mean of 4 days, thus approximately mimicking the situation for COVID-19 (e.g., (1)). During the infectious period an individual makes infectious contacts at rates such that the mean numbers of infectious contacts agree with those of the next-generation matrix M.
We assume that the basic reproduction number satisfies R0 = 2.5 (a few other values are also evaluated) and that the epidemic is initiated with a small fraction of infectious individuals on February 15. On March 15, when the fraction infected is still small, preventive measures are implemented such that all averages in the next-generation matrix are scaled by the same factor α < 1, so the next-generation matrix becomes αM. Consequently, the new reproduction number is αR0. These preventive measures are kept until the ongoing epidemic is nearly finished. That is, all preventive measures are relaxed thus setting α back to 1 on June 30. If herd immunity is not reached there will then be a second wave, whereas if herd immunity has been achieved the epidemic continues to decline.
We used the model to investigate the effect of the preventive measures and for two scenarios we analyze whether or not a given level of preventive measures yields disease-induced herd immunity. We do this for populations that are (i) homogeneous, (ii) categorized by age groups but not by activity levels, (iii) not categorized by age but are assigned different activity levels, and (iv) have both age-related and activity structures.
For each of the four population structures described above, we show overall disease-induced herd immunity in Table 1. This was obtained by assuming that preventive measures having factor α < 1 are implemented at the start of an epidemic, running the resulting model epidemic to its conclusion and then exposing the population to a second epidemic with α = 1. We obtain α*, the greatest value of α such that a second epidemic cannot occur. The disease-induced herd immunity level hD is given by the fraction of the population that is infected by the first epidemic. This approximates the situation where preventive measures are implemented early and lifted late in an outbreak. Note that hD, given the next-generation matrix, is independent of the distributions of the latent and infectious periods.
Table 1 Disease-induced herd immunity level hD and classical herd immunity level hC for different population structures. Numbers correspond to percentages.
R0 = 2.0 R0 = 2.5 R0 = 3.0 Population structure hD hC hD hC hD hC Homogeneous 50.0 50.0 60.0 60.0 66.7 66.7 Age structure 46.0 50.0 55.8 60.0 62.5 66.7 Activity structure 37.7 50.0 46.3 60.0 52.5 66.7 Age and activity structure 34.6 50.0 43.0 60.0 49.1 66.7
As seen in Table 1, all three structured populations have lower disease-induced herd immunity hD compared to the classical herd immunity hC, which assumes immunity is uniformly distributed among the different types of individual. From the table it is clear that the different activity levels have a greater effect on reducing hD than age structure.
In Table 2, the final fractions infected in the different age activity groups for α = α* just barely reaching disease-induced herd immunity are given. This is done for the age and activity group structure and assuming R0 = 2.5. The overall fraction infected equals hD = 43.0%, in agreement with Table 1. Table S1 is a similar table where only activity groups are considered.
Table 2 Final outcome fractions infected in different groups. Assuming R0 = 2.5 and preventive measures put in place such that α = α* just barely reaching herd immunity for R0 = 2.5. Population structure includes both age and activity and fractions infected are given as percentages.
Age group Low activity Average activity High activity 0–5 years 17.6 32.1 53.9 6–12 years 25.8 44.9 69.7 13–19 years 31.4 52.9 77.8 20–39 years 27.4 47.2 72.1 40–59 years 22.8 40.3 64.4 ≥60 years 14.6 27.0 46.7
We also illustrate the time evolution of the epidemic for R0 = 2.5, assuming both age and activity structure and starting with a small fraction externally infected in mid-February. For this we show the epidemic over time for four different levels of preventive measures put in place early in the epidemic outbreak (mid-March) and being relaxed once transmission has dropped to low levels (June 30). In Fig. 1, the community proportion that is infectious is plotted during the course of the epidemic.
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