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COVID-19 Коронавирусная пандемия

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Избыточные смерти и пандемия COVID-19

Оценки избыточных смертей могут предоставить информацию о бремени смертности, потенциально связанной с пандемией COVID-19, включая смерти, которые напрямую или косвенно относятся к COVID-19. Избыточные смерти обычно определяются как разница между наблюдаемым числом смертей в конкретные временные промежутки и ожидаемым числом смертей в том же временном промежутке. Визуализация предоставляет недельные оценки избыточных смертей по юрисдикции, в которой произошла смерть. Еженедельные подсчеты смертей сравниваются с историческими тенденциями, чтобы определить, есть ли значительный или незначительный рост числа смертей.

Подсчеты смертей от всех причин смерти

Подсчитываются все смерти от всех причин смерти, включая COVID-19. Поскольку некоторые смерти от COVID-19 могут быть отнесены к другим причинам смерти (например, если COVID-19 не был диагностирован или не указан в свидетельстве о смерти), отслеживание общей смертности может дать информацию о том, наблюдается ли избыток смертей, даже когда смертность от COVID-19 может быть недооценена. Кроме того, также оценивались смерти от всех причин, исключая COVID-19. Сравнение этих двух наборов оценок — избыточные смерти с и без COVID-19 — может дать представление о том, как много избыточных смертей идентифицировано как из-за COVID-19, и сколько избыточных смертей объявлено как вызванные другими причинами смерти.

Оценки избыточных смертей и методология

Оценки избыточных смертей могут быть рассчитаны различными способами и будут различаться в зависимости от методологии и предположений о том, сколько смертей ожидается. Оценки избыточных смертей, представленные на этой веб-странице, были рассчитаны с использованием алгоритмов слежения Фаррингтона. Диапазон значений числа избыточных смертей был рассчитан как разница между наблюдаемым количеством и одним из двух порогов (средним ожидаемым количеством или верхней границей 95% предиктивного интервала), неделями и юрисдикциями.

Взвешенные временные счета смертей

Предварительные счета смертей взвешены для учета неполных данных. Тем не менее, данные за последние недели все еще вероятно неполные. Веса основаны на полноте предварительных данных в предыдущие годы, но своевременность данных может измениться в 2020 году по сравнению с предыдущими годами, поэтому результативные взвешенные оценки могут быть слишком высокими в некоторых юрисдикциях и слишком низкими в других. По мере получения дополнительной информации о точности оценок по взвешиванию, могут быть внесены дальнейшие уточнения весов, которые повлияют на оценки. Любые изменения методов или алгоритма взвешивания будут отмечены в технических замечаниях при их возникновении. Подробнее о методах, взвешивании, данных и ограничениях можно найти в технических замечаниях.

Различные оценки

<strong>Выбор панели управления</strong>

Анализ чрезмерной смертности с использованием алгоритмов наблюдения Фаррингтона

Оценки избыточной смертности для всей территории США были рассчитаны как сумма числа избыточных смертей по каждому региону (с отрицательными значениями, установленными в ноль) и не были прямо оценены с использованием алгоритмов наблюдения Фаррингтона. Выбор суммирования (а не оценки) был сделан для того, чтобы учесть возможность того, что у некоторых регионов могут быть значительно неполные данные, в то время как в других регионах могут быть сообщены более высокие числа смертей, чем ожидалось, эти отрицательные и положительные значения будут сбрасывать друг друга при оценке избыточной смертности для всей территории США прямым использованием алгоритмов наблюдения Фаррингтона. До окончания окончательных данных (обычно через 12 месяцев после завершения года сбора данных), нельзя определить, являются ли наблюдаемые снижения смертности с использованием предварительных данных результатом истинного снижения или неполного доклада. Таким образом, при прямом расчете избыточных смертей для всей территории США отрицательные значения из-за неполного доклада в некоторых регионах компенсируют избыточные смерти, наблюдаемые в других регионах. Например, общее число избыточных смертей в США, рассчитанное прямым образом с использованием алгоритмов Фаррингтона, было приблизительно на 25% ниже числа, рассчитанного путем суммирования по регионам с избыточными смертями. Эта разница, вероятно, обусловлена несколькими регионами, сообщающими более низкие, чем ожидалось, числа смертей — что может быть вызвано недоотчетом, истинным снижением смертности в некоторых областях или их комбинацией. Кроме того, потенциальные расхождения между числом избыточных смертей в США при прямом расчете по сравнению с суммой оценок по конкретным регионам могут быть связаны с различными оценочными порогами для ожидаемого количества смертей в США и по регионам.

Наконец, отмечается, что отчеты об избыточной смертности, представленные здесь, могли быть вызваны не COVID-19, ни прямо, ни косвенно. Пандемия может изменить паттерны смертности для других причин смерти. Восходящие тенденции по другим причинам смерти (например, самоубийства, передозировки наркотиками, сердечные заболевания) могут способствовать избыточной смертности в некоторых регионах. Будущие анализы причинной избыточной смертности могут предоставить дополнительную информацию об этих паттернах.

По мере получения дополнительной информации о точности взвешенных оценок могут быть внесены дополнительные уточнения, и изменения в методах взвешивания повлияют на оценки. Любые изменения в методах или алгоритме взвешивания будут отмечены в технических заметках в момент их внесения.

Methods to address reporting lags (i.e., underreporting) were updated as of September 9, 2020. Generally, these updates resulted in estimates of the total number of excess deaths that were approximately 5% smaller than the previous method, as weights in some jurisdictions with improved timeliness were reduced. While these adjustments likely reduce potential overestimation for those jurisdictions with improved timeliness, estimates for the most recent weeks for the US overall are likely underestimated to a larger extent than in previous releases. Some jurisdictions have little to no provisional data available in the most recent week(s) (CT, NC, WV); together, these jurisdictions represent approximately 5% of US deaths. In previous releases, some of the underestimation or lack of provisional data from certain jurisdictions was offset by the overestimation in other jurisdictions with improved timeliness when considering trends for the US overall. Because the updated weighting methods mitigate the impact of the previous overestimation for some jurisdictions with improved timeliness but provide no additional adjustments for underestimation or a lack of recent provisional data in other jurisdictions, the excess death estimates for the US overall are expected to result in a larger degree of underestimation than in previous releases.

Unweighted estimates are shown in one of the dashboards so that readers can examine the impact of weighting on estimates of excess deaths. For some jurisdictions, improvements in timeliness in 2020 relative to prior years will lead to weighted estimates that are too large. For other jurisdictions, the weighting may be insufficient to address reporting lags, particularly for data reported with shorter lag times (e.g., within 4–6 weeks). As an additional step to guard against underreporting, the weighted counts of deaths by week and jurisdiction were compared with control counts of deaths based on available demographic information from the death certificate. Demographic data are typically available prior to the cause of death data, which can take 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. For weeks and jurisdictions where the weighted count of deaths was less than the control count based on the demographic data, the weighted values were replaced with the control count. For example, if the weighted count for a given jurisdiction and week was 400, while the control count for that same jurisdiction and week was 800, this indicates that the weights are not fully accounting for incomplete data. In this case, the value of 800 would be used, as it represents a more complete estimate of the total number of deaths occurring in that jurisdiction and week.

Data for jurisdictions where counts are between 1 and 9 are suppressed. Additionally, data for weeks where the counts are less than 50% of the expected number are also suppressed, as these provisional counts are highly incomplete and potentially misleading. This change resulted in showing estimates with a lag of 1 week for most jurisdictions and the US. For some jurisdictions (Connecticut, North Carolina, Puerto Rico), lags may be greater. Declines in the observed numbers of deaths in recent weeks should not be interpreted to mean that the numbers of deaths are decreasing, as these declines are expected when relying on provisional data that are generally less complete in recent weeks. While the weighting method is intended to mitigate the impact of underreporting, it may not be sufficient to eliminate the problem of underreporting entirely. Therefore, it is not yet possible to determine whether decreases in the number of deaths is due to underreporting or to true declines until more complete data is obtained.

Mortality Outcomes

Weekly counts of deaths from all causes were examined, including deaths due to COVID-19. As many deaths due to COVID-19 may be assigned to other causes of deaths (for example, if COVID-19 was not mentioned on the death certificate as a suspected cause of death), tracking all-cause mortality can provide information about whether an excess number of deaths is observed, even when COVID-19 mortality may be undercounted. These estimates can also provide information about deaths that may be indirectly related to COVID-19. For example, if deaths due to other causes may increase as a result of health care shortages due to COVID-19. Additionally, deaths from all causes excluding COVID-19 were also estimated. These counts excluded deaths with U07.1 as an underlying or multiple cause of death.

Comparing these two sets of estimates — excess deaths with and without COVID-19 — can provide insight about how many excess deaths are identified as due to COVID-19, and how many excess deaths are due to other causes of death. These deaths could represent misclassified COVID-19 deaths, or potentially could be indirectly related to COVID-19. Additionally, death certificates are often initially submitted without a cause of death, and then updated when cause of death information becomes available. It may be the case that some excess deaths that are not attributed directly to COVID-19 will be updated in coming weeks with cause-of-death information that includes COVID-19. These analyses will be updated periodically, and the numbers presented will change as more data are received.

Cause of Death

Estimated numbers of deaths due to these other causes of death could represent misclassified COVID-19 deaths, or potentially could be indirectly related to COVID-19 (e.g., deaths from other causes occurring in the context of health care shortages or overburdened health care systems). Deaths with an underlying cause of death of COVID-19 are not included in these estimates of deaths due to other causes, but deaths where COVID-19 appeared on the death certificate as a multiple cause of death may be included in the cause-specific estimates. For example, in some cases, COVID-19 may have contributed to the death, but the underlying cause of death was another cause, such as terminal cancer. For the majority of deaths where COVID-19 is reported on the death certificate (approximately 95%), COVID-19 is selected as the underlying cause of death.

Deaths due to all other natural causes were excluded (ICD-10 codes: A00–A39, A42–B99, D00–E07, E15–E68, E70–E90, F00, F02, F04–G26, G31–H95, K00–K93, L00–M99, N00–N16, N20–N98, O00–O99, P00–P96, Q00–Q99). External causes of death (i.e., injuries) were excluded, as the reporting lag is substantially longer for external causes of death (4). Additionally, causes of death where the underlying cause was unknown or ill-specified (i.e., R-codes) were excluded (except for R09.2, which is included under the Respiratory diseases category). Counts of deaths with unknown cause are typically substantially higher in provisional data, as many records are initially submitted without a specific cause of death and are then updated when more information becomes available (4). For deaths due to external causes of death or unknown cause, provisional data are highly unreliable and inaccurate in recent weeks, and it can take six to nine months to ensure sufficiently accurate estimates. Counts by cause provided here will not sum to the total number of deaths, given that some causes are excluded.

Estimates by cause of death and age at death are weighted, using the methods described above. The total count of deaths above average levels is shown for select causes of death. These totals are calculated by summing the number of deaths above average levels (based on weekly counts from 2015–2019) since 2/1/2020. Negative values were set to zero and therefore excluded from these sums. Because not all causes of death are shown and due to differences in how the average expected numbers of deaths are estimated, the total numbers of deaths across all the selected causes will not match the numbers of excess deaths from all causes excluding COVID-19.

Estimates by race and Hispanic origin are weighted using the methods described above. Weekly counts are shown for deaths due to all causes, all causes excluding COVID-19, and COVID-19. Because estimates are weighted to account for incomplete reporting in recent weeks, counts of death due to COVID-19 will not match other data sources. For data years 2018 – 2020, race and Hispanic-origin categories are based on the 1997 Office of Management and Budget (OMB) standards, allowing for the presentation of data by single race and Hispanic origin. These race and Hispanic-origin groups—non-Hispanic single-race white, non-Hispanic single-race black or African American, non-Hispanic single-race American Indian or Alaska Native (AIAN), and non-Hispanic single-race Asian—differ from the bridged-race categories used in previous data years when not all jurisdictions reported race and Hispanic origin using the 1997 OMB standards. Numbers may therefore differ from previous reports and other sources of data on mortality by race and Hispanic origin.

Limitations

The completeness of provisional data varies by cause of death and by age group. However, the weights applied do not account for this variability. It is unknown whether completeness varies by race and Hispanic origin. Therefore, the predicted numbers of deaths may be too low for some age groups, race/ethnicity groups, and causes of death. For example, provisional data on deaths among younger age groups is typically less complete than among older age groups. Predicted counts may therefore be too low among the younger age groups. Since the weights were based on the completeness of all-cause mortality data in past years, the weighted estimates for specific causes of death are likely too low, as reporting lags are typically larger for specific causes of death than for all-cause mortality. To minimize the degree of underreporting, cause-specific estimates are presented with a two-week lag.

Frequently Asked Questions

Why did the excess death estimates change?

Are there any changes in interpretation with the new method?

What are the limitations of this approach?

– While the imputations do account for the natural variability in weekly death counts, because they are based on the average expected numbers and variation, they may not fully account for the variability we typically see related to severe influenza seasons or other types of events that lead to much higher than expected mortality.

– This imputation approach mitigates one challenge with the older approach – having to exclude 160+ weeks of data. However, this newer approach does not provide a solution for the challenge of making accurate long-term predictions. Regardless of the statistical method used, all long-term predictions of expected mortality trends based on pre-pandemic data are subject to an unknown and growing degree of bias and uncertainty as the pandemic goes on.

Coronavirus Cases

Currently Infected Patients

in Mild Condition

Serious or Critical

Cases which had an outcome:

Recovered / Discharged

The charts above are updated after the close of the day in GMT+0. See more graphs

Reported Cases and Deaths by Country or Territory

Highlighted in green

= all cases have recovered from the infection

Highlighted in grey

= all cases have had an outcome (there are no active cases)

Daily Data

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Trending statistics

As of February 2024, the country with the most unicorns, or privately owned companies with a valuation of one billion U.S. dollars or more, was the United States. SpaceX, Stripe, and Databricks were the biggest of these American companies in terms of market valuation.

Unicorns Made in China

Although China did not have the highest number of unicorns worldwide, the country was home to some of the world’s most important ones. ByteDance, for example, was valued at around 200 billion U.S. dollars as of April 2023. This made the company not only the highest valued unicorn of China, but also the most valued unicorn of the world.

Feb 15, 2024

The ranking shows the leading FMCG companies worldwide in 2023, based on generated net sales. In that year, Unilever was ranked as the sixth largest consumer goods company worldwide with net sales of about 63.29 billion U.S. dollars.

The consumer goods industry Consumer goods are goods which are intended for everyday private consumption. They are further classified as fast moving consumer goods (FMCG) and slow moving consumer goods (SMCG). FMCG are goods with a lifespan shorter than a year. Popular categories include food and beverages, personal care and household products, clothing and apparel, tobacco, and pet food/pet care. These categories are bought quite frequently with recurring expenditure. In contrast to SMCG, the products tend to be sold high in volume, but low in cost. FMCG are also known as Consumer Packaged Goods (CPG).

FMCG companies The FMCG environment is highly competitive as FMCG companies are always on the hunt for the next great product discovery or innovation in order to meet consumer’s needs. Some of the leading key players of the FMCG environment include Nestlé, Procter & Gamble (P&G), Unilever, PepsiCo and the Coca-Cola Company. All of them operate internationally and have to try to meet country-specific requirements regarding product packaging and labeling. Their million dollar brands can be found in many household pantries. In order to keep consumers as regular buyers, CPG companies try to develop loyalty and trust towards their brands. Ariel, Gillette, Pampers and Pantene are considered to be among the most famous brands of P&G.

Feb 21, 2024

Disney in the North American movie market

The pandemic’s impact on the cinema industry posed an unparalleled challenge for the studio’s usually outstanding performance on the big screen. Disney’s box office revenue in the U.S. and Canada skyrocketed by over 160 percent in 2021. Yet the result amounted to less than one-third of the 3.8-billion-dollar revenue recorded in 2019, before the coronavirus outbreak. Despite the struggle, the distributor was the leading film studio in the U.S. and Canada by box office revenue in 2021.

The ‘Big Five’ in Hollywood

The most recent polling data from January 2024 puts the approval rating of the United States Congress at 15 percent, reflecting no significant change from the previous month. The October approval level was a record low of the 118th Congress, which began in January 2023.

Ye of little faith However, Americans tend not to have much confidence in many of the institutions in the United States. Additionally, public confidence in the ability of the Republican and Democratic parties to work together has decreased drastically between 2008 and 2022, with nearly 60 percent of Americans having no confidence the parties can govern in a bipartisan way.

This statistic depicts the average monthly salary of employees in Taiwan from 2000 to 2023. In 2023, the average monthly wage in Taiwan amounted to 58,545 New Taiwan dollars, up from 57,728 New Taiwan dollars in the previous year.

As of December, 2023, there were over 90 thousand listings for room and apartment rentals in London on the Airbnb website, the highest of any other major European city. Airbnb listings were also high in Paris, Rome and Madrid. Paris accounted for around 74 thousand listings, while Rome and Madrid had over 29 and 25 thousand, respectively.

Controversy of Airbnb in Europe

Airbnb has become an increasingly popular option for tourists looking for local accommodation. Visitors are attracted to using Airbnb properties instead of hotels and other traditional travel accommodation mainly due to cheaper prices, but also for the location, and to gain an authentic experience. However, the site is facing ongoing legal problems, with some destinations moving to ban or restrict rentals from the site due to them worsening housing problems and undermining hotel regulations. Many European cities, including Amsterdam and Paris, have placed limits on the length of rentals, and others such as Barcelona have introduced strict regulations for hosts.

The rise of Airbnb

Airbnb is one of the most successful companies in the global sharing economy. The company was founded in San Francisco, California in 2008, after being conceived by two entrepreneurs looking for a way to offset their high rental costs. Airbnb was developed as an online platform for hosts to rent out their properties on a short-term basis. It now competes with other online travel booking websites including Booking.com and Expedia.

In the fourth quarter of 2023, the quarterly gross domestic product of the United Kingdom was approximately 566.7 billion British pounds, compared with just under 568.6 billion pounds in the previous quarter.

Feb 16, 2024

In 2018, 90 percent of Australians aged 12 and over owned a smartphone, with 96 percent aged between 25 and 54 owning a device.

Market penetration of smartphones in Australia

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References

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