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Post by Admin on Jun 25, 2020 19:19:30 GMT
Fig. 1 Plot of the overall fraction infected over time for the age and activity structured community with R0 = 2.5, for four different preventive levels inserted March 15 (day 30) and lifted June 30 (day 135). The blue, red, yellow and purple curves correspond to no, light, moderate, and severe preventive measures, respectively. On March 15 preventive measures (at four different levels for α) are put in place and in every case the growth rate is reduced except when no preventative measures are applied (the blue curve; α = 1). Moreover, the preventive measures reduce the size of, and delay the time of, the peak. Sanctions are lifted on June 30 putting transmission rates back to their original levels, but only in the curve with highest sanctions is there a clear second wave, since the remaining curves have reached (close to) herd immunity. The yellow curve finishes below 50% getting infected. The reason this exceeds the 43% infected shown in Table 1 is that preventive measures were not imposed from the start and were lifted before the epidemic was over. The corresponding cumulative fraction infected as a function of time are shown in Fig. 2. An interesting observation is that the purple curve results in a higher overall fraction infected even though this scenario had more restrictions applied than the scenario of the yellow curve. This is because this epidemic was further from completion when sanctions were lifted. Fig. 2 Plot of the cumulative fraction infected over time for the age and activity structured community and R0 = 2.5, for a four different preventive levels inserted March 15 and lifted June 30. The blue curve corresponds to no preventive measures, the red with light preventive measure, the yellow to moderate preventive measures and the purple corresponding to severe preventive measures. Only the curve corresponding to greatest preventive measures shows a severe second wave when restrictions are lifted. In most cases no (strong) second wave of outbreak occurs once preventive measures are lifted. Note also that the yellow curve, in which the overall fraction infected is well below the classical herd immunity level hC = 60%, is in fact protected by herd immunity since no second wave appears. See the supplement for depictions of when restrictions are lifted continuously between June 1 and August 31 (see figs. S1 and S2), and how the effective reproduction number evolves as a function of the time when restrictions are lifted (see fig. S3). Our simple model shows how the disease-induced herd immunity level may be substantially lower than the classical herd immunity level derived from mathematical models assuming homogeneous immunization. Our application to COVID-19 indicates a reduction of herd immunity from 60% under homogeneous immunization down to 43% (assuming R0 = 2.5) in a structured population, but this should be interpreted as an illustration, rather than an exact value or even a best estimate. To try to quantify more precisely the size of this effect remains to be done. In our model we have taken age cohorts and social activity levels into account. However, more complex and realistic models have many other types of heterogeneities: for instance, increased spreading within households (of different sizes) or within schools and workplaces. Those activity levels and social structures are country or region specific and should be modeled as such. Further, spatial heterogeneity arises with rural areas having lower contact rates than metropolitan regions. It seems reasonable to assume that most such additional heterogeneities will have the effect of reducing the disease-induced immunity level hD even further, in that high contact environments, such as metropolitan regions, large households and extensive, big workplaces for example will have a higher infected fraction and immunity will be concentrated even more among highly-active and connected individuals. Some complex models (e.g., (1)) categorize by, for example, age and spatial location but omit individual variation within each category. The latter can be incorporated by including different activity levels, or by adding a social network in which individuals have differing numbers of acquaintances. As we have illustrated, differences in social activity play a greater role in reducing the disease-induced herd immunity level than heterogeneous age-group mixing. Thus models excluding such features will see a smaller difference between hD and hC. Our choice to have 50% having average activity, 25% having half and 25% having double activity is of course arbitrary. An important future task is to determine the size of differences in social activity within age groups for different types of populations. The greater the social heterogeneity there is between groups, the greater the difference between hD and hC. One assumption of our model is that preventive measures act proportionally on all contact rates. This may not always hold. For example, most countries aim to protect elderly and other risk groups, which does not obey this assumption. Again, we expect the effect of discriminatory protection would be to reduce the disease-induced immunity level, because the oldest age group has the fewest contacts. For a model including schools and workplaces, it is not obvious what effect school closure and strong recommendations to work from home would have on the disease-induced herd immunity level. A different model extension would be to allow individuals to change their activity level over time. The effect of such changes in activity levels, in particular if they vary between different categories, remains unknown. In our model we assume that infection with and subsequent clearance of the virus leads to immunity against further infection for an extended period of time. If there is relatively quick loss of immunity or if we want to consider a time scale where the impact of demographic processes, such as births and people changing age-group becomes substantial, then we need further models. Different forms of immunity levels have been discussed previously in the literature although, as far as we know, not when considering early-introduced preventions that are lifted toward the end of an epidemic outbreak. Anderson and May (10) concluded that immunity level may differ between uniformly distributed, disease-induced and optimally located immunity (see also (11)), and vaccination policies selecting individuals to immunize in an optimal manner have been discussed in many papers, e.g., (12). A recent independent paper by Gomes et al. (13) shows similar results to those of the present paper but considers heterogeneities in terms of continuously varying susceptibilities. That model is solved numerically similarly to our Fig. 1, but the analytical results for the final number of infected people and hD are missing. Rather than lifting all restrictions simultaneously, most countries are gradually lifting COVID-19 preventive measures. That strategy can avoid seeing the type of overshoot illustrated by the purple curve in Fig. 2, which results in a greater fraction infected than if milder restrictions are enacted (yellow curve).
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Post by Admin on Jun 27, 2020 6:45:16 GMT
Swedish state epidemiologist Anders Tegnell says the World Health Organization made "a total mistake" when it included Sweden in a list of countries seeing a resurgence of the coronavirus. The WHO misinterpreted the Swedish data, Tegnell said. "It is a total mistake," Tegnell said, according to Swedish public broadcaster Sveriges Radio. Sweden has been closely watched because of its controversial approach to the COVID-19 pandemic. While the country has urged people to practice physical distancing and follow other safety precautions, it has not ordered a strict shutdown. While many of its European neighbors endured three months of austere conditions, Sweden allowed most of its bars, restaurants, schools and retail stores to remain open. Tegnell's remarks were a retort to a warning from Dr. Hans Henri P. Kluge, the WHO's regional director for Europe, who said on Thursday that in Sweden and 10 other countries, "accelerated transmission has led to very significant resurgence that if left unchecked will push health systems to the brink once again in Europe." Along with Sweden, the warning included countries such as Armenia, Azerbaijan, Kazakhstan and Ukraine. Days before the WHO issued its warning, Sweden reported 1,699 new coronavirus cases — its biggest spike yet, according to data from the Swedish Ministry of Health and Social Affairs. So far this month, Sweden has set a new record for daily case numbers every week. But Tegnell objected to Kluge's warning, saying that WHO officials were misinterpreting Sweden's epidemiological data. In Tegnell's view, the rise in new cases is due to a recent bump in testing. He added that Sweden is seeing a relatively low number of admissions to intensive care units, along with a decline in COVID-19 deaths. Swedish officials had said earlier this year that the city of Stockholm was on course to reach "herd immunity". A target date was set for the end of May, though last month officials acknowledged that the goal would not be met. "Herd immunity occurs when enough people of a population are immune to an infectious disease, either because they've been infected and recovered or they've been vaccinated against it," as NPR's H.J. Mai has reported. "Some researchers have put the threshold for coronavirus herd immunity at 60%." Overall, Sweden has reported more than 65,000 coronavirus cases, including more than 5,200 deaths, according to data compiled by Johns Hopkins University.
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Post by Admin on Jul 12, 2020 22:08:06 GMT
In the first longitudinal study of its kind, scientists analysed the immune response of more than 90 patients and healthcare workers at Guy’s and St Thomas’ NHS foundation trust and found levels of antibodies that can destroy the virus peaked about three weeks after the onset of symptoms then swiftly declined. Blood tests revealed that while 60% of people marshalled a “potent” antibody response at the height of their battle with the virus, only 17% retained the same potency three months later. Antibody levels fell as much as 23-fold over the period. In some cases, they became undetectable. “People are producing a reasonable antibody response to the virus, but it’s waning over a short period of time and depending on how high your peak is, that determines how long the antibodies are staying around,” said Dr Katie Doores, lead author on the study at King’s College London. The study has implications for the development of a vaccine, and for the pursuit of “herd immunity” in the community over time. The immune system has multiple ways to fight the coronavirus but if antibodies are the main line of defence, the findings suggested people could become reinfected in seasonal waves and that vaccines may not protect them for long. “Infection tends to give you the best-case scenario for an antibody response, so if your infection is giving you antibody levels that wane in two to three months, the vaccine will potentially do the same thing,” said Doores. “People may need boosting and one shot might not be sufficient.” Early results from the University of Oxford have shown that the coronavirus vaccine it is developing produces lower levels of antibodies in macaques than are seen in humans infected with the virus. While the vaccine appeared to protect the animals from serious infection, they still became infected and may have been able to pass on the virus. The King’s College study is the first to have monitored antibody levels in patients and hospital workers for three months after symptoms emerged. The scientists drew on test results from 65 patients and six healthcare workers who tested positive for the virus, and a further 31 staff who volunteered to have regular antibody tests between March and June. The study, which has been submitted to a journal but has yet to be peer-reviewed, found that antibody levels rose higher and lasted longer in patients who were severe cases. This may be because the patients have more virus and churn out more antibodies to fight the infection. There are four other types of coronavirus in widespread circulation, which cause the common cold. “One thing we know about these coronaviruses is that people can get reinfected fairly often,” said Prof Stuart Neil, a co-author on the study. “What that must mean is that the protective immunity people generate doesn’t last very long. It looks like Sars-Cov-2, the virus that causes Covid-19, might be falling into that pattern as well.” Longitudinal evaluation and decline of antibody responses in SARS-CoV-2 infection Jeffrey Seow, Carl Graham, Blair Merrick, Sam Acors, Kathryn J.A. Steel, Oliver Hemmings, Aoife O'Bryne, Neophytos Kouphou, Suzanne Pickering, Rui Galao, Gilberto Betancor, Harry D Wilson, Adrian W Signell, Helena Winstone, Claire Kerridge, Nigel Temperton, Luke Snell, Karen Bisnauthsing, Amelia Moore, Adrian Green, Lauren Martinez, Brielle Stokes, Johanna Honey, Alba Izquierdo-Barras, Gill Arbane, Amita Patel, Lorcan OConnell, Geraldine O Hara, Eithne MacMahon, Sam Douthwaite, Gaia Nebbia, Rahul Batra, Rocio Martinez-Nunez, Jonathan D. Edgeworth, Stuart J.D. Neil, Michael H. Malim, Katie Doores doi: doi.org/10.1101/2020.07.09.20148429Abstract Antibody (Ab) responses to SARS-CoV-2 can be detected in most infected individuals 10-15 days following the onset of COVID-19 symptoms. However, due to the recent emergence of this virus in the human population it is not yet known how long these Ab responses will be maintained or whether they will provide protection from re-infection. Using sequential serum samples collected up to 94 days post onset of symptoms (POS) from 65 RT-qPCR confirmed SARS-CoV-2-infected individuals, we show seroconversion in >95% of cases and neutralizing antibody (nAb) responses when sampled beyond 8 days POS. We demonstrate that the magnitude of the nAb response is dependent upon the disease severity, but this does not affect the kinetics of the nAb response. Declining nAb titres were observed during the follow up period. Whilst some individuals with high peak ID50 (>10,000) maintained titres >1,000 at >60 days POS, some with lower peak ID50 had titres approaching baseline within the follow up period. A similar decline in nAb titres was also observed in a cohort of seropositive healthcare workers from Guy′s and St Thomas′ Hospitals. We suggest that this transient nAb response is a feature shared by both a SARS-CoV-2 infection that causes low disease severity and the circulating seasonal coronaviruses that are associated with common colds. This study has important implications when considering widespread serological testing, Ab protection against re-infection with SARS-CoV-2 and the durability of vaccine protection.
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Post by Admin on Jul 18, 2020 5:03:00 GMT
Sweden kept its country open relative to its neighbors. Some citizens worked from home, and many bicycled rather than taking public transit. Schools, particularly for younger children, remained open, as did many businesses. Because of the lack of enforcement and feeling of normalcy, some citizens say they didn’t feel as stressed and anxious as they might have otherwise. “There’s a mental health aspect to lockdown,” said Nils Mattisson, founder of Minut, a home monitoring start-up based in London and Malmo, Sweden. “All the fear can have adverse effects on people’s health.” Ramping up ICU beds Back in the spring, Sweden needed far more intensive care beds to care for a potential flood of Covid-19 patients. According to Dr. Jonathan Ilicki, head of medical operations at health start-up Doktor24, the situation did improve somewhat. “In a short period of time, we did see a huge increase in ICU beds per capita, particularly in Stockholm,” he said. Swedish officials quickly ordered the construction of a field hospital in a convention complex just south of the city center in early April. That hospital closed in June as demand for care eased in the region. However, there has been some controversy about whether vulnerable elderly patients were provided with these beds when they needed them. Some health-care workers have pointed to a reluctance to admit elderly patients who came down with the virus in their nursing homes. What went OK Sweden’s top health authorities provided a daily briefing in the hardest months of the pandemic, which some residents appreciated. But that all changed in late June, when state epidemiologist Anders Tegnell shared that some of the responses were flawed. Updates are now provided just two days a week on Tuesdays and Thursdays. As Bloomberg reports, the briefings had turned into “daily grillings” where Tegnell had to justify his decisions. Still, citizens say they appreciated the information, particularly when it came from a high-ranking scientist. “It was a source of comfort, and it was helpful and relatively straightforward,” said Mattisson. An engaged and compliant public Many in Sweden say that public health officials didn’t mandate certain behaviors, in part because they didn’t need to. Restaurants, bars and salons might have remained open, but they were relatively empty compared with the months before the pandemic. Moreover, many people avoided gathering in large groups, particularly indoors. “There’s a strong trust in Sweden between the government and the people,” said Dr. Arvin Yarollahi, the head of the orthopedic department at a hospital group in the country called NU-sjukvarden. Yarollahi said people took the recommendations seriously, even if they weren’t enforced.
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Post by Admin on Aug 20, 2020 6:58:45 GMT
Sweden’s top infectious disease expert has resisted recommending face masks for the general population — arguing it’s “very dangerous” if people believe the coverings alone will stop the spread of the coronavirus. Anders Tegnell, chief epidemiologist at Sweden’s Public Health Agency, has repeatedly expressed skepticism that face masks will control virus outbreaks, the Financial Times reported. “It is very dangerous to believe face masks would change the game when it comes to COVID-19,” said Tengell, who is considered the country’s equivalent of Dr. Anthony Fauci from the White House COVID-19 task force. He noted that countries with widespread mask compliance, such as Belgium and Spain, were still seeing rising virus rates. “Face masks can be a complement to other things when other things are safely in place,” he said. “But to start with having face masks and then think you can crowd your buses or your shopping malls — that’s definitely a mistake.” He completely brushed off the prospect of wearing masks last month, saying, “With numbers diminishing very quickly in Sweden, we see no point in wearing a face mask in Sweden, not even on public transport.” Tegnell has argued that evidence about the effectiveness of face mask use was “astonishingly weak.” “I’m surprised that we don’t have more or better studies showing what effect masks actually have,” he told the UK Times.
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