#] #] ********************* #] "$d_webRawe""Projects - mini/Pandemics, Kp index, sunspots/Pandemics, Kp, sunspot notes.txt" www.BillHowell.ca 21May2020 initial To view this file - use a text editor (not word processor) constant width font (eg courrier 10), tab - 3 spaces ******** 29May2020 graph of flu versus pneumonia Sub-title : 1976-2006 CDC nchs_pneumonia_flu_annual, https://www.cdc.gov/nchs/data/series/sr_24/sr24_006.pdf 1999-2008 CDC nchs_flu_annual, https://www.cdc.gov/nchs/nvss/mortality/gmwk250f.htm 2009-2015 CDC wonder_[flu, pneumonia]_monthly, https://wonder.cdc.gov/ 2015-2020 CDC fluview_[flu, pneumonia]_weekly, https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html Only the "pneumonia_deaths" (green) are read from the right axis. All data are from the USA CDC, expressed as deaths/ 1M population/ year. Weekly data : FluView. Monthly data : Wonder. Annual data : rest https://wonder.cdc.gov/controller/datarequest/D76;jsessionid=3C5F07C998E9E6A0C39CD1930B48091C CDC Wonder influenza deaths 2009-2018 >> took a while to get the hang of it!!! nchs_flu pneumonia_flu_annual +-----+ Flu-only graph : 1942-1976 Peter Doshi analysis, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2374803/ 1999-2008 CDC nchs_flu_annual, https://www.cdc.gov/nchs/nvss/mortality/gmwk250f.htm 2009-2015 CDC wonder_flu_monthly, https://wonder.cdc.gov/ 2015-2020 CDC fluview_flu_weekly, https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html 2015-2020 CDC fluview_flu_cases, https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html Only the "flu cases" (green) are read from the right axis. All data are from the USA CDC. Weekly data : FluView. Monthly data : Wonder. Annual data : rest +-----+ Both graphs of "USA flu virus [cases, deaths] 1942-2020.ods" : Influenza deaths and cases from the US [Center for Disease Control, Natioanl Institutes of Health] 1942-1976 Peter Doshi analysis, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2374803/ 1999-2008 CDC nchs_flu_annual, https://www.cdc.gov/nchs/nvss/mortality/gmwk250f.htm 2009-2015 CDC wonder_flu_monthly, https://wonder.cdc.gov/ 2015-2020 CDC fluview_flu_weekly, https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html 2015-2020 CDC fluview_flu_cases, https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html Weekly data : FluView. Monthly data : Wonder. Annual data : rest /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/USA flu virus [cases, deaths] 1942-2020.jpg /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/USA [flu virus, pneumonia condition] deaths 1942-2020.jpg /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Influenza, sunspots, Kp index, zero Kp bins 1930-2020 updated.xcf ******** 28May2020 [1976-1997, 1997-2015] influenza data Howell : it's hard to find data that separates the flu from pneumonia https://www.statista.com/statistics/184574/deaths-by-influenza-and-pneumonia-in-the-us-since-1950/ Deaths by influenza and pneumonia in the U.S. from 1950 to 2017 Influenza, or the flu, is a viral infection that is highly contagious and especially common in the winter season. Influenza is a common cause of pneumonia, although most cases of the flu do not develop into pneumonia. Pneumonia is an infection or inflammation of the lungs and is particularly deadly among young children and the elderly. search "what is the differnce between influenza and pneumonia?" flu is a viral disease, common cause of pneumonia pneumonia seems to be a chronic condition? search "is pneumonia a condition or a disease?" https://en.wikipedia.org/wiki/Pneumonia Pneumonia - Wikipedia Pneumonia is an inflammatory condition of the lung affecting primarily the small air sacs known as alveoli. Symptoms typically include some combination of productive or dry cough, chest pain, fever and difficulty breathing. The severity of the condition is variable. ... Pneumonia is usually caused by infection with viruses or bacteria and less commonly by other microorganisms, certain medications or conditions such as autoimmune diseases.[3][4] Risk factors include cystic fibrosis, chronic obstructive pulmonary disease (COPD), sickle cell disease, asthma, diabetes, heart failure, a history of smoking, a poor ability to cough (such as following a stroke), and a weak immune system.[5][7] Diagnosis is often based on symptoms and physical examination.[8] Chest X-ray, blood tests, and culture of the sputum may help confirm the diagnosis.[8] The disease may be classified by where it was acquired, such as community- or hospital-acquired or healthcare-associated pneumonia.[15] ... Pneumonia often shortens the period of suffering among those already close to death and has thus been called "the old man's friend". +-----+ 1976-1997 https://wwwn.cdc.gov/nndss/infectious-tables.html CDC Stacks Collections of Weekly Infectious Disease Tables (1951 to present) https://stacks.cdc.gov/cbrowse?parentId=cdc:49375&pid=cdc:49375 Annual reports - I want weekly if possible +--+ https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5933a1.htm Estimates of Deaths Associated with Seasonal Influenza --- United States, 1976--2007 August 27, 2010 / 59(33);1057-1062 +-----+ CDC search "1980 113 leading causes of death" https://www.cdc.gov/nchs/data/nvsr/nvsr50/nvsr50_15.pdf >> 2000 I already had this 07Jan1992 advance report 1989 influenza all 1593 scan - manual extraction 29Feb1996 Advance Report of Final Mortality Statistics, 1993 10 leading, pools flu & pneumonia https://www.cdc.gov/nchs/data/series/sr_24/sr24_006.pdf Advance reports, 1989 & 1990 pools flu & pneumonia BINGO!!! by [type of flu, age group] >> Howell : this pools [flu, pneumonia] TABLE 1. Estimated number of annual influenza-associated deaths with underlying pneumonia and influenza causes*, by age group --- United States, 1976--77 through 2006--07 influenza seasons Season Prominent influenza type/subtype† <19 yrs 19--64 yrs ≥65 yrs Overall No. (95% CI§) No. (95% CI) No. (95% CI) No. (95% CI) 1976--77 B / A(H3N2) 155 (85--488) 485 (357--958) 2,126 (1,847--3,013) 2,766 (2,289--4,459) 1977--78 A(H3N2) / A(H1N1) 234 (171--458) 771 (671--1,139) 3,889 (3,668--4,610) 4,894 (4,510--6,207) 1978--79 A(H1N1) 128 (86--343) 235 (159--530) 673 (511--1,327) 1,036 (756--2,200) 1979--80 B 100 (65--280) 336 (270--594) 1,706 (1,530--2,321) 2,142 (1,865--3,195) 1980--81 A(H3N2) / A(H1N1) 115 (78--284) 483 (411--715) 3,054 (2,878--3,650) 3,652 (3,367--4,649) 1981--82 B / A(H1N1) 41 (18--155) 173 (112--402) 903 (746--1,490) 1,117 (876--2,047) 1982--83 A(H3N2) 114 (78--222) 621 (512--859) 4,393 (4,091--5,035) 5,128 (4,681--6,116) 1983--84 A(H1N1) / B 123 (78--241) 466 (343--735) 2,548 (2,168--3,279) 3,137 (2,589--4,255) 1984--85 A(H3N2) 130 (100--217) 805 (743--1,056) 6,663 (6,459--7,363) 7,598 (7,302--8,636) 1985--86 B / A(H3N2) 88 (52--172) 487 (388--728) 3,607 (3,328--4,313) 4,182 (3,768--5,213) 1986--87 A(H1N1) 70 (47--167) 186 (127--454) 705 (510--1,478) 961 (684--2,099) 1987--88 A(H3N2) 75 (44--144) 509 (425--729) 4,375 (4,087--5,017) 4,959 (4,556--5,890) 1988--89 B / A(H1N1) 120 (71--212) 536 (391--798) 3,559 (3,095--4,331) 4,215 (3,557--5,341) 1989--90 A(H3N2) 91 (65--158) 662 (581--888) 6,158 (5,882--6,857) 6,911 (6,528--7,903) 1990--91 B 56 (35--123) 363 (284--598) 2,907 (2,624--3,659) 3,326 (2,943--4,380) 1991--92 A (H3N2) / A(H1N1) 82 (53--158) 592 (496--833) 5,494 (5,151--6,269) 6,168 (5,700--7,260) 1992--93 B / A(H3N2) 88 (57--164) 638 (533--913) 5,673 (5,290--6,587) 6,399 (5,880--7,664) 1993--94 A (H3N2) 77 (63--142) 647 (592--881) 6,705 (6,491--7,535) 7,429 (7,146--8,558) 1994--95 A(H3N2) / B 71 (47--128) 599 (512--818) 5,997 (5,692--6,752) 6,667 (6,251--7,698) 1995--96 A(H1N1) / A(H3N2) 76 (38--144) 508 (377--761) 4,357 (3,877--5,236) 4,941 (4,292--6,141) 1996--97 A(H3N2) / B 97 (71--153) 857 (764--1,103) 8,719 (8,348--9,582) 9,673 (9,183--10,838) 1997--98 A(H3N2) 78 (66--141) 787 (725--1,038) 8,528 (8,271--9,405) 9,393 (9,062--10,584) 1998--99 A(H3N2) / B 85 (65--146) 854 (749--1,102) 8,716 (8,336--9,589) 9,655 (9,150--10,837) 1999--00 A(H3N2) 85 (67--159) 911 (826--1,187) 9,598 (9,242--10,540) 10,594 (10,135--11,886) 2000--01 B / A(H1N1) 67 (43--136) 482 (340--774) 3,362 (2,824--4,350) 3,911 (3,207--5,260) 2001--02 A(H3N2) 107 (80--176) 1,218 (1,086--1,535) 11,966 (11,471--13,001) 13,291 (12,637--14,712) 2002--03 B / A(H1N1) 82 (40--148) 677 (483--990) 5,097 (4,421--6,068) 5,856 (4,944--7,206) 2003--04 A(H3N2) 103 (87--184) 1,367 (1,250--1,741) 13,245 (12,777--14,422) 14,715 (14,114--16,347) 2004--05 A(H3N2) / B 115 (83--192) 1,459 (1,269--1,781) 12,872 (12,276--13,854) 14,446 (13,628--15,827) 2005--06 A(H3N2) 101 (64--193) 1,268 (1,080--1,646) 10,415 (9,782--11,449) 11,784 (10,926--13,288) 2006--07 A(H1N1) / B / A(H3N2) 67 (20--212) 657 (355--1,147) 3,906 (2,973--5,176) 4,630 (3,348--6,535) Average 97 (65--201) 666 (555--949) 5,546 (5,182--6,373) 6,309 (5,802--7,524) Minimum 41 (18--123) 173 (112--402) 673 (510--1,327) 961 (684--2,047) Maximum 234 (171--488) 1,459 (1,269--1,781) 13,245 (12,777--14,422) 14,715 (14,114--16,347) * Deaths were categorized using the International Classification of Diseases eighth revision (ICD-8), ninth revision (ICD-9), or 10th revision (ICD-10), as appropriate. † Prominent influenza type and subtype were defined as at least 20% of all isolates that were typed or subtyped in that season. § Confidence interval. +-----+ 2008-2015 https://search.cdc.gov/search/index.html?query=Multiple+cause+mortality&sitelimit=&utf8=%E2%9C%93&affiliate=cdc-main https://wwwn.cdc.gov/nndss/data-and-statistics.html https://wwwn.cdc.gov/nndss/infectious-tables.html https://www.cdc.gov/mmwr/index2018.html WONDER Weekly Tables of Infectious Diseases (1996 to present) +-----+ NYET - not useful for me 1999-2018 only? https://wonder.cdc.gov/controller/datarequest/D76;jsessionid=37CE57970AC262C37EE11030D94E8275 Underlying Cause of Death, 1999-2018 Results CDC Wonder 15 leading causes of death >> pooled influenza and pneumonia total for all years https://wonder.cdc.gov/controller/datarequest/D76;jsessionid=37CE57970AC262C37EE11030D94E8275 Underlying Cause of Death, 1999-2018 Results CDC Wonder 15 leading causes of death 1999 Influenza (J09-J11) 1,665 279,040,168 0.60 2000 Influenza (J09-J11) 1,765 281,421,906 0.63 2001 nfluenza (J09-J11) 257 284,968,955 0.09 >> This is the same as what I already have! +-----+ NYET - not useful for me National Notifiable Infectious Diseases and Conditions: United States Data Tables 1980 Reported morbidity and mortality in the United States annual summary 1979 https://stacks.cdc.gov/view/cdc/1577 Reported Deaths by year 1969-1978 does NOT include influenza!! +-----+ NYET - not useful for me Influenza surveillance reports Example : https://stacks.cdc.gov/view/cdc/288 page 2 & 3 The expected number of deaths is based on fitting weekly mortality reports for the previous 5 years (omitting epidemic weeks) to the following equation by a least squares Fourier Regression Model: ^/gamma = u +r*t + A1*cos(2*pi*t/52) + B1*sin(2*pi*t/52) + A2*cos(4*pi*t/52) + B2*sin(4*pi*t/52) >> Howell - not so good, except for clamoring about an epidemic The equation contains terms for a linear trend over time and seasonal variation. Omission of the epidemic observations of previous years prevents an artifactual inflation of the expected level during the influenza season. The epidemic threshold is calculated by multiplying the standard error of the residual by 1.65 and adding the product to the expected number. Two successive weeks of reported deaths that exceed the threshold indicate an event of epidemiological interest. Based on the equations, graphs are prepared for publication which show the number of reported deaths, expected deaths, and the epidemic threshold by week (45-47). +-----+ NYET - not useful for me Morbidity & Mortality weekly reports - don't contain influenza! https://stacks.cdc.gov/view/cdc/291 1952 https://stacks.cdc.gov/view/cdc/1574 1980 ******** 27May2020 /media/bill/SWAPPER/Climate static/Datasets/Sunspots/sunspot daily total sidc.be-silso-datafiles 1930-2020.ods /media/bill/SWAPPER/Climate static/Datasets/Sunspots/sunspot daily total 1930-2020.png full graph : /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Influenza, sunspots, Kp index, zero Kp.jpg email to Ken Tapping CDC Wonder session - didn't work, save page : https://wonder.cdc.gov/controller/saved/D76/D85F179 ********* 27May2020 upgrade references on flu [data, chart] Sub-title : Influenza cases and deaths data from US [CDC, NIH] : 1942-1976 Peter Doshi analysis, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2374803/ 1977-1997 no data shown, possibly in https://www.cdc.gov/nchs/data/dvs/dt78icd8.pdf 1999-2008 CDC NCHS-DVS, https://www.cdc.gov/nchs/nvss/mortality/gmwk250f.htm 2009-2015 CDC Wonder, https://wonder.cdc.gov/ 2015-2020 CDC FluView, https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html maybe Multiple casues of death, https://www.cdc.gov/nchs/data/dvs/dt78icd8.pdf or https://wonder.cdc.gov/wonder/help/main.html#QueryingDataSets to use logarithmic scale, zero rate values were replaced by 0.001 /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/USA flu virus [cases, deaths].jpg ********* 26May2020 USA flu deaths +-----+ Peter Doshi paper - section "Assessing the Impact of Influenza" In this study, I have primarily considered the relative (rather than absolute) value of recorded influenza death rate statistics, which allowed me to compare 1 influenza season (or 1 month) with another. The use of the data in this way is supported by the consistent seasonality seen year after year in the monthly recorded influenza death data, which suggests that influenza-classed deaths have a nonrandom distribution. Others have also found recorded influenza-classed deaths to be a good predictor of excess all-cause mortality.25 Nevertheless, knowing the true cause of influenza-classed deaths and developing an accurate numerical assessment of the impact of influenza-related mortality remain problematic. The CDC4 and others26 have argued that recorded influenza deaths underrepresent influenza’s true impact on mortality and have offered various statistical models to calculate “influenza-associated mortality.” Although the effort to know influenza’s true impact is important and relevant from the perspective of potential public health interventions, there are several points of concern with present modeling efforts. First, current CDC estimates of seasonal influenza-associated mortality consistently dwarf recorded influenza deaths, varying from 5 to 60 times as large. Second, the CDC’s model projects that influenzaassociated mortality rose 67% from the 1980s to the 1990s; however, over this same period, recorded influenza deaths declined 38%, as shown in Figure 3 and the table available as a supplement to the online version of this article at http://www.ajph.org. Third, there are unresolved discrepancies between various published models. For the category “influenza-associated underlying influenza and pneumonia mortality” (a statistic that describes those deaths already classified as caused by influenza or pneumonia but claimed by the CDC to be associated with influenza), the CDC’s model estimated around 6000 deaths per season between 1976–1977 and 1998–1999,4 but the model of Dushoff et al. estimated over 14000 deaths per season between 1979 and 2001.26 These problems highlight the weaknesses and inconsistencies in present estimates of influenzaassociated mortality. A related problem stemming from the confusion between influenza and ILI is the use of so-called “excess mortality” or “winter mortality” in the computation of influenza’s impact. The historical monthly influenza data presented here show that for most seasons, influenza deaths were recorded for almost every month of the year, an unlikely event considering that the circulation of influenza virus is seasonal, not year-round. It is plausible that many cases and deaths from other (i.e., noninfluenza) ILIs are being misclassified as influenza, particularly when they occur during the winter season. A portion of these deaths are probably associated with other viruses such as rhinovirus and respiratory syncytial virus, which sometimes co-circulate with influenza. Moreover, cold weather itself causes upswings in mortality even without the presence of influenza.22 Further complicating the objective of gauging influenza’s true impact on mortality is the confusion between influenza and influenza-like illnesses (ILI). Influenza is but one of scores of respiratory viruses and some bacteria that cause ILI. Without laboratory testing, influenza infection is clinically indistinguishable from other ILI.27 Official annual respiratory viral surveillance data for the seasons 1976—1977 through 1998—1999 have shown that a mean of only 12% of “influenza specimens” actually tested positive for influenza virus.4 Between 1999 and 2001, there was positive confirmation of influenza virus for fewer than 10% of deaths recorded as caused by influenza. Although this proportion has increased in recent years (14% in 2002, 23% in 2003, 18% in 2004), in the absence of testing, cause of death is still only speculative. +-----+ /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/USA NCHS/Leading deaths 1900-98.ods >> capture (manually) https://www.cdc.gov/nchs/data/dvs/lead1900_98.pdf https://www.cdc.gov/nchs/data/nvsr/nvsr58/nvsr58_19.pdf https://www.cdc.gov/nchs/products/vsus/ta.htm >> This webpage is key ... https://www.cdc.gov/nchs/nvss/mortality_tables.htm https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm https://www.cdc.gov/nchs/products/vsus/ta.htm Easiest - but combines pneumonia & flu, hand data entry : https://www.cdc.gov/nchs/data/dvs/lead1900_98.pdf Pneumonia (all forms) and influenza 1900 107-109,33 1947 107-109,33 1949-57 480-493 1976 470-474,480-486 1997 (480-487) https://wonder.cdc.gov/ https://www.cdc.gov/nchs/data/dvs/Record_Layout_2010.pdf Multiple casues of death https://www.cdc.gov/nchs/data/dvs/dt78icd8.pdf ********************* 25May2020 US annual flu cases +-----+ https://www.foxnews.com/media/dr-scott-jensen-cdc-coronavirus-death-guidelines Fox News Flash Published April 28 Minnesota doctor questions coronavirus death toll, claims 'influenza deaths ... have been called COVID-19' By Yael Halon | Fox News Dr. Scott Jensen, a Minnesota family physician and Republican state senator, told "The Ingraham Angle" Tuesday that the Centers for Disease Control and Prevention (CDC) guidelines for doctors to certify whether a patient has died of coronavirus are a "mess" and predicted that some fatalities initially reported to be COVID-19-related would be reclassified. "We both know," Jensen told host Laura Ingraham, "that there have been influenza deaths, influenza cases, that have been called COVID-19 [deaths] because nobody bothered to swab their throats. If you want to find out what the data is, I don't care if they're dead or alive, swab them. We can always run a test later and then actually get real information." Earlier this month, Jensen told Ingraham that under the CDC guidelines, a patient who died after being hit by a bus and tested positive for coronavirus would be listed as having presumed to have died from the virus regardless of whatever damage was caused by the bus. Jensen also gave a hypothetical example of a patient who died while suffering from influenza. If the patient was elderly and had symptoms like fever and cough a few days before passing away, the doctor explained, he would have listed "respiratory arrest" as the primary cause of death. "There's been so much garbage going in that we are going to get garbage out," Jensen said Tuesday. "Three weeks ago, you and I talked about this and we've seen since then, [in] Pennsylvania, the coroners have pushed back and said 'These aren't COVID-19 deaths,' and Pennsylvania reduces its numbers. "New York says it's going to come out and add 3,700 [coronavirus deaths] in a day," Jensen continued. "The Illinois public health director tried to define what a COVID-19 death looks like and stumbled all over herself -- made it very clear didn't she have a clue. "We've got people ... in the southeastern part of the country saying they want accountability, we’ve got California and Minnesota saying 'We're going to count only confirmed cases' ... so it's a mess," Jensen said. New data published this week by the University of Washington's Institute for Health Metrics and Evaluation (IHME) projected more than than 74,000 coronavirus-related deaths in the United States by Aug. 4, an increase of nearly 6,000 projected deaths from its latest report. Fox News' Charles Creitz contributed to this report. +-----+ https://aspe.hhs.gov/cdc-%E2%80%94-influenza-deaths-request-correction-rfc CDC — Influenza Deaths: Request for Correction (RFC) US data on influenza deaths are false and misleading. The Centers for Disease Control and Prevention (CDC) acknowledges a difference between flu death and flu associated death yet uses the terms interchangeably. Additionally, there are significant statistical incompatibilities between official estimates and national vital statistics data. Compounding these problems is a marketing of fear—a CDC communications strategy in which medical experts "predict dire outcomes" during flu seasons. The CDC website states what has become commonly accepted and widely reported in the lay and scientific press: annually "about 36 000 [Americans] die from flu" (www.cdc.gov/flu/about/disease.htm) and "influenza/pneumonia" is the seventh leading cause of death in the United States (www.cdc.gov/nchs/fastats/lcod.htm). But why are flu and pneumonia bundled together? Is the relationship so strong or unique to warrant characterizing them as a single cause of death? David Rosenthal, director of Harvard University Health Services, said, "People don't necessarily die, per se, of the [flu] virus—the viraemia. What they die of is a secondary pneumonia. So many of these pneumonias are not viral pneumonias but secondary [pneumonias]." But Dr Rosenthal agreed that the flu/pneumonia relationship was not unique. For instance, a recent study (JAMA 2004;292: 1955-60[Abstract/Free Full Text]) found that stomach acid suppressing drugs are associated with a higher risk of community acquired pneumonia, but such drugs and pneumonia are not compiled as a single statistic. CDC states that the historic 1968-9 "Hong Kong flu" pandemic killed 34 000 Americans. At the same time, CDC claims 36 000 Americans annually die from flu. What is going on? Meanwhile, according to the CDC's National Center for Health Statistics (NCHS), "influenza and pneumonia" took 62 034 lives in 2001—61 777 of which were attributed to pneumonia and 257 to flu, and in only 18 cases was flu virus positively identified. Between 1979 and 2002, NCHS data show an average 1348 flu deaths per year (range 257 to 3006). The NCHS data would be compatible with CDC mortality estimates if about half of the deaths classed by the NCHS as pneumonia were actually flu initiated secondary pneumonias. But the NCHS criteria indicate otherwise: "Cause-of-death statistics are based solely on the underlying cause of death... defined by WHO as `the disease or injury which initiated the train of events leading directly to death.'" In a written statement, CDC media relations responded to the diverse statistics: "Typically, influenza causes death when the infection leads to severe medical complications." And as most such cases "are never tested for virus infection...CDC considers these [NCHS] figures to be a very substantial undercounting of the true number of deaths from influenza. Therefore, the CDC uses indirect modelling methods to estimate the number of deaths associated with influenza." CDC's model calculated an average annual 36 155 deaths from influenza associated underlying respiratory and circulatory causes (JAMA 2003;289: 179-86[Abstract/Free Full Text]). Less than a quarter of these (8097) were described as flu or flu associated underlying pneumonia deaths. Thus the much publicised figure of 36 000 is not an estimate of yearly flu deaths, as widely reported in both the lay and scientific press, but an estimate—generated by a model—of flu-associated death. William Thompson of the CDC's National Immunization Program (NIP), and lead author of the CDC's 2003 JAMA article, explained that "influenza-associated mortality" is "a statistical association between deaths and viral data available." He said that an association does not imply an underlying cause of death: "Based on modelling, we think it's associated. I don't know that we would say that it's the underlying cause of death." Yet this stance is incompatible with the CDC assertion that the flu kills 36 000 people a year—a misrepresentation that is yet to be publicly corrected. Before 2003 CDC said that 20 000 influenza-associated deaths occurred each year. The new figure of 36 000 reported in the January 2003 JAMA paper is an estimate of influenza-associated mortality over the 1990s. Keiji Fukuda, a flu researcher and a co-author of the paper, has been quoted as offering two possible causes for this 80% increase: "One is that the number of people older than 65 is growing larger...The second possible reason is the type of virus that predominated in the 1990s [was more virulent]." However, the 65-plus population grew just 12% between 1990 and 2000. And if flu virus was truly more virulent over the 1990s, one would expect more deaths. But flu deaths recorded by the NCHS were on average 30% lower in the 1990s than the 1980s. At the 2004 "National Influenza Vaccine Summit," co-sponsored by CDC and the American Medical Association, Glen Nowak, associate director for communications at the NIP, spoke on using the media to boost demand for the vaccine. One step of a "Seven-Step `Recipe' for Generating Interest in, and Demand for, Flu (or any other) Vaccination" occurs when "medical experts and public health authorities publicly...state concern and alarm (and predict dire outcomes)—and urge influenza vaccination" (www.ama-assn.org/ama1/pub/upload/mm/36/2004_flu_nowak.pdf). Another step entails "continued reports...that influenza is causing severe illness and/or affecting lots of people, helping foster the perception that many people are susceptible to a bad case of influenza." Preceding the summit, demand had been low early into the 2003 flu season. "At that point, the manufacturers were telling us that they weren't receiving a lot of orders for vaccine for use in November or even December," recalled Dr Nowak on National Public Radio. "It really did look like we needed to do something to encourage people to get a flu shot." If flu is in fact not a major cause of death, this public relations approach is surely exaggerated. Moreover, by arbitrarily linking flu with pneumonia, current data are statistically biased. Until corrected and until unbiased statistics are developed, the chances for sound discussion and public health policy are limited. I am a pediatrician and this propaganda affects my practice directly. Kenneth Stoller International Hyperbaric Medical Association +-----+ https://www.cdc.gov/flu/weekly/ Outpatient Illness Surveillance ILINet Nationwide during week 20, 1.1% of patient visits reported through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet) were due to influenza-like illness (ILI). This percentage is below the national baseline of 2.4%. https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html FluView - USA National, Regional, and State Level Outpatient Illness and Viral Surveillance Viral Surveillance — Data collection from both the U.S. World Health Organization (WHO) Collaborating Laboratories and National Respiratory and Enteric Virus Surveillance System (NREVSS) laboratories began during the 1997-98 season. The volume of tested specimens has greatly increased during this time due to increased participation and increased testing. During the 1997-98 season 43 state public health laboratories participated in surveillance, and by the 2004-05 season all state public health laboratories were participating in surveillance. The addition of NREVSS data during the 1997-98 season roughly doubled the amount of virologic data reported each week. The number of specimens tested and % positive rate vary by region and season based on different testing practices including triaging of specimens by the reporting labs, therefore it is not appropriate to compare the magnitude of positivity rates or the number of positive specimens between regions or seasons. The U.S. WHO and NREVSS collaborating laboratories report the total number of respiratory specimens tested and the number positive for influenza types A and B each week to CDC. Most of the U.S. WHO collaborating laboratories also report the influenza A subtype (H1 or H3) of the viruses they have isolated, but the majority of NREVSS laboratories do not report the influenza A subtype. For more information on virologic surveillance please visit:http://www.cdc.gov/flu/weekly/overview.htm#Viral Outpatient Illness Surveillance — Information on patient visits to health care providers for influenza-like illness is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). This collaborative effort between CDC, state and local health departments, and health care providers started during the 1997-98 influenza season when approximately 250 providers were enrolled. Enrollment in the system has increased over time and there were >3,000 providers enrolled during the 2010-11 season. The number and percent of patients presenting with ILI each week will vary by region and season due to many factors, including having different provider type mixes (children present with higher rates of ILI than adults, and therefore regions with a higher percentage of pediatric practices will have higher numbers of cases). Therefore it is not appropriate to compare the magnitude of the percent of visits due to ILI between regions and seasons. Baseline levels are calculated both nationally and for each region. Percentages at or above the baseline level are considered to be elevated. For more information on ILI surveillance and baselines please visit:http://www.cdc.gov/flu/weekly/overview.htm#Outpatient /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/USA flu cases, combined public & clinical labs 1997-2020.ods /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/USA flu cases, combined public & clinical labs 1997-2020.jpg ********* 24May2020 US annual flu cases +-----+ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2374803/ Peter Doshi May2008 Trends in Recorded Influenza Mortality: United States, 1900–2004 Am J Public Health. 2008 May; 98(5): 939–945. Objectives. I sought to describe trends in historical influenza mortality data in the United States since 1900 and compare pandemic with nonpandemic influenza seasons. Methods. I compiled a database of monthly influenza-classed death rates from official US mortality tables for the years 1900 to 2004 (1905–1909 excluded), from which I calculated adjusted influenza season (July 1–June 30) mortality rates. Results. An overall and substantial decline in influenza-classed mortality was observed during the 20th century, from an average seasonal rate of 10.2 deaths per 100 000 population in the 1940s to 0.56 per 100 000 by the 1990s. The 1918–1919 pandemic stands out as an exceptional outlier. The 1957–1958 and 1968–1969 influenza pandemic seasons, by contrast, displayed substantial overlap in both degree of mortality and timing compared with nonpandemic seasons. Conclusions. The considerable similarity in mortality seen in pandemic and non-pandemic influenza seasons challenges common beliefs about the severity of pandemic influenza. The historical decline in influenza-classed mortality rates suggests that public health and ecological factors may play a role in influenza mortality risk. Nevertheless, the actual number of influenza-attributable deaths remains in doubt. +--+ /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Peter Doshi May2008 Crude mortality, United States, 1900–2004 FIGURE 1— Crude mortality per 100000 population, by influenza season (July to June of the following year), for seasons 1900–1901 to 2003–2004 (a) and 1930–1931 to 2003–2004 (b), United States. Note. International Classification of Diseases (ICD) revision 1 was used from 1900 to 1909, revision 2 from 1910 to 1920, revision 3 from 1921 to 1929. Comparability ratios are unavailable for revisions 1 to 3. Beginning in 1930, influenza mortality rates have been adjusted for changes in ICD revisions to reflect conditions in the current ICD revision 10. +--+ /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Peter Doshi May2008 Trends in Recorded Influenza Mortality: United States, 1900–2004 FIGURE 2— Crude influenza-classed mortality per 100 000 population, by month, for 1900–2004 (a) and 1930–2004 (b), United States. Note. International Classification of Diseases (ICD) revision 1 was used from 1900 to 1909, revision 2 from 1910 to 1920, revision 3 from 1921 to 1929. Comparability ratios are unavailable for revisions 1 to 3. Beginning in 1930, influenza mortality rates have been adjusted for changes in ICD revisions to reflect conditions in the current ICD revision 10. Peter Doshi May2008 Trends in Recorded Influenza Mortality: United States, 1900–2004 Am J Public Health. 2008 May; 98(5): 939–945. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2374803/ TABLE 1 Comparison of Adjusted Influenza Death Rates for 12 Influenza Seasons: United States, 1941–1976 Peter Doshi May2008 Trends in Recorded Influenza Mortality: United States, 1900–2004 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2374803/ Influenza Deaths per 100 000 Population Season Type Mean Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun 1941–1942 Nonpandemic 9.9 3.4 2.8 2.9 5.2 9.7 12.1 18.9 19.5 21.5a 12.7 7.0 3.4 1942–1943 Nonpandemic 10.8 2.7 2.5 3.7 6.9 9.1 14.8 20.9a 19.7 20.8 15.0 8.8 4.4 1943–1944 Nonpandemic 22.2 3.1 3.0 3.2 6.5 9.0 78.8 92.0a 29.4 19.4 11.9 6.6 3.5 1944–1945 Nonpandemic 7.4 2.5 2.5 2.8 5.3 7.2 11.7 14.2a 14.0 12.2 7.1 5.8 3.5 1945–1946 Nonpandemic 11.2 2.2 2.1 3.0 4.3 8.4 36.9a 34.2 19.3 11.6 5.6 4.1 2.6 1946–1947 Nonpandemic 6.9 1.3 1.4 2.0 3.2 4.0 6.7 8.7 7.0 24.0a 18.2 4.8 1.8 1952–1953 Nonpandemic 5.9 0.9 0.7 0.8 1.6 2.2 3.3 19.1 27.1a 9.4 3.5 1.7 0.8 1957–1958 Pandemic 5.4 0.6 0.8 1.7 13.1 18.8a 6.2 5.6 6.9 6.2 2.8 1.2 0.6 1959–1960 Nonpandemic 4.1 0.4 0.4 0.6 0.8 1.1 1.8 10.1 21.9a 9.4 1.9 0.9 0.4 1967–1968 Nonpandemic 2.3 0.2 0.2 0.2 0.4 0.6 3.0 17.1a 4.2 1.2 0.4 0.2 0.2 1968–1969 Pandemic 4.2 0.1 0.2 0.2 0.5 1.0 16.4 23.3a 4.8 2.6 0.9 0.4 0.2 1975–1976 Nonpandemic 3.6 0.2 0.1 0.2 0.3 0.3 0.4 0.8 12.8 22.1a 4.5 0.6 0.3 a Denotes peak monthly mortality during given season. +-----+ https://www.cdc.gov/flu/about/burden/past-seasons.html >> use hospitalisations - better assurance of ID Table 1: Estimated Influenza Disease Burden, by Season — United States, 2010-11 through 2018-19 Influenza Seasons Symptomatic Illnesses Medical Visits Hospitalizations Deaths Season Estimate 95% U I Estimate 95% U I Estimate 95% U I Estimate 95% U I 2010-2011 21,000,000 (20,000,000 – 25,000,000) 10,000,000 (9,300,000 – 12,000,000) 290,000 (270,000 – 350,000) 37,000 (32,000 – 51,000) 2011-2012 9,300,000 (8,700,000 – 12,000,000) 4,300,000 (4,000,000 – 5,600,000) 140,000 (130,000 – 190,000) 12,000 (11,000 – 23,000) 2012-2013 34,000,000 (32,000,000 – 38,000,000) 16,000,000 (15,000,000 – 18,000,000) 570,000 (530,000 – 680,000) 43,000 (37,000 – 57,000) 2013-2014 30,000,000 (28,000,000 – 33,000,000) 13,000,000 (12,000,000 – 15,000,000) 350,000 (320,000 – 390,000) 38,000 (33,000 – 50,000) 2014-2015 30,000,000 (29,000,000 – 33,000,000) 14,000,000 (13,000,000 – 16,000,000) 590,000 (540,000 – 680,000) 51,000 (44,000 – 64,000) 2015-2016 24,000,000 (20,000,000 – 33,000,000) 11,000,000 (9,000,000 – 15,000,000) 280,000 (220,000 – 480,000) 23,000 (17,000 – 35,000) 2016-2017 29,000,000 (25,000,000 – 45,000,000) 14,000,000 (11,000,000 – 23,000,000) 500,000 (380,000 – 860,000) 38,000 (29,000 – 61,000) Preliminary estimates* Estimate 95% UI Estimate 95% UI Estimate 95% UI Estimate 95% UI 2017-2018* 45,000,000 (39,000,000 – 58,000,000) 21,000,000 (18,000,000 – 27,000,000) 810,000 (620,000 – 1,400,000) 61,000 (46,000 – 95,000) 2018-2019* 35,520,883 (31,323,881, 44,995,691) 16,520,350 (14,322,767, 21,203,231) 490,561 (387,283, 766,472) 34,157 (26,339, 52,664) * Estimates from the 2017-2018 and 2018-2019 seasons are preliminary and may change as data are finalized. Table 2: Estimated rates of influenza-associated disease outcomes, per 100,000, by age group — United States, 2017-2018 influenza season Illness rate Medical visit rate Hospitalization rate Mortality rate Age group Estimate 95% Cr UI Estimate 95% Cr UI Estimate 95% Cr UI Estimate 95% Cr UI 0-4 yrs 18,448.1 (12,856.5 – 36,475.0) 12,360.2 (8,501.3 -24,596.7) 128.6 (89.6 – 254.3) 0.6 (0.0, 1.8) 5-17 yrs 13,985.6 (10,983.6 – 18,987.0) 7,272.5 (5,589.3 -9,972.2) 38.3 (30.1 – 52.1) 1.0 (0.4, 2.6) 18-49 yrs 10,469.7 ( 8,895.6 – 14,075.1) 3,873.8 (3,092.9 – 5,321.7) 58.8 (49.9 – 79.0) 2.0 (1.2, 5.0) 50-64 yrs 20,881.1 (14,828.2 – 36,378.8) 8,978.9 (6,145.3 -15,818.0) 221.4 (157.2 – 385.8) 10.6 (6.7, 25.0) 65+ yrs 11,690.6 ( 7,682.1 -23,175.5) 6,546.7 (4,207.2 -13,023.8) 1,062.8 (698.4 – 2,106.9) 100.1 (70.8, 163.7) * Uncertainty interval All estimates from the 2017-2018 influenza season are preliminary and may change as data from the season are cleaned and finalized. The model to generate burden estimates uses data on influenza testing practices at FluSurv-NET hospitals to correct for known under-detection of influenza and mortality data from the National Center for Health Statistics for estimation of deaths. The most recent estimates for the burden of influenza during 2017-2018 in Tables 1-2 above use more recent data on testing practices and mortality and are lower than those previously reported. ********* 23May2020 Historical pandemics (all) /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Kp average monthly NOAA-Potsdam 1933-2020.jpg /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Kp average monthly NOAA-Potsdam 1933-2020 Title.xcf /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Kp average monthly NOAA-Potsdam 1933-2020.jpg +-----+ https://www.history.com/topics/middle-ages/pandemics-timeline +-----+ https://thefederalist.com/2020/04/18/10-deadliest-pandemics-in-history-were-much-worse-than-coronavirus-so-far/ 10 Deadliest pandemics in historyy were much worse than coronavirus so far 18Apr2020 Dan Carpenter Dan Carpenter is a proponent of preparedness, homesteads, and modern self sufficiency. He is the founder and principal of Homestead Launch and SCP Survival. Contact him at Dan@HomesteadLaunch.com. >> nice summary Antonine Plague: Rome,165-180,?measles, smallpox, or a combination of both?,5M Plague of Justinian: Eastern Roman Empire,541-750,Yersinia pestis? (bubonic plague),30-50M Black Death (bubonic plague): Europe and Asia,1347-1351,Yersinia pestis,200M New World Smallpox: Americas,1520-1600s,variola virus/rats,56M The Great Plague of Milan: Italy,1629-1631,bubonic plague,Ferrara in northern Italy did not experience a single death from the plague because it implemented strict border controls; sanitation laws; and personal hygiene practices. The Great Plague of Milan was so deadly -it is thought to be a major contributor to the decline in power of the Republic of Venice- which had risen to prominence during the Renaissance. Cholera: India/Indonesia,1817-Present,bacterium Vibrio cholerae,95k/yr 1M total,The cholera pandemic consists of a series of smaller pandemics that have been occurring on and off since 1817. We are now within the seventh cholera pandemic. Six of the seven major cholera pandemics have originated in India. However the current pandemic originated in Indonesia. Third bubonic plague: China,1885-1950s,bacteria Yersinia pestis, 12M Spanish Flu: Unknown,1918-1920,H1N1 virus (similar to swine flu)/ originated in birds,40-50M,did not fade entirely until 750 Hong Kong Flu: China,1968-1970,Influenza A virus H3N2,1-4M,The Hong Kong flu (H3N2) originated in China and was the second-worst flu pandemic of the 20th century HIV/AIDS: Sub-Saharan Africa,1981-Present,Human immunodeficiency virus (HIV)/ chimps,23-35M,HIV is not transmitted from casual contact +-----+ https://www.visualcapitalist.com/history-of-pandemics-deadliest/ Visualizing the History of Pandemics March 14, 2020 By Nicholas LePan >> Nice table Name,Time period,Type/Pre-human host,Death toll Antonine Plague,165-180,Believed to be either smallpox or measles,5M Japanese smallpox epidemic,735-737,Variola major virus,1M Plague of Justinian,541-542,Yersinia pestis bacteria/Rats fleas,30-50M Black Death,1347-1351,Yersinia pestis bacteria/Rats fleas,200M New World Smallpox Outbreak,1520 – onwards,Variola major virus,56M Great Plague of London,1665,Yersinia pestis bacteria/Rats fleas,100,000 Italian plague,1629-1631,Yersinia pestis bacteria/Rats fleas,1M Cholera Pandemics 1-6,1817-1923,V. cholerae bacteria,1M+ Third Plague,1885,Yersinia pestis bacteria/Rats fleas,12M (China and India) Yellow Fever,Late 1800s,Virus/Mosquitoes,100k-150k (U.S.) Russian Flu,1889-1890,Believed to be H2N2 (avian origin),1M Spanish Flu,1918-1919,H1N1 virus/Pigs,40-50M Asian Flu,1957-1958,H2N2 virus,1.1M Hong Kong Flu,1968-1970,H3N2 virus,1M HIV/AIDS,1981-present,Virus/Chimpanzees,25-35M Swine Flu,2009-2010,H1N1 virus/Pigs,200,000 SARS,2002-2003,Coronavirus/Bats Civets,770 Ebola,2014-2016,Ebolavirus/Wild animals,11,000 MERS,2015-Present,Coronavirus/Bats camels,850 COVID-19,2019-Present,Coronavirus/Unknown (possibly pangolins),333.5k (Johns Hopkins University estimate as of 5:32am PT 22May2020) Note: Many of the death toll numbers listed above are best estimates based on available research. Some, such as the Plague of Justinian and Swine Flu, are subject to debate based on new evidence. +-----+ https://www.forbes.com/sites/ericmack/2020/03/16/see-how-coronavirus-compares-to-other-pandemics-through-history/#152cfca37d1e Coronavirus|103,921 views|Mar 16, 2020,05:03pm EDT See How Coronavirus Compares To Other Pandemics Through History Eric Mack I cover science and innovation and products and policies they create. /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/200316 Forbes, Eric Mack - See How Coronavirus Compares To Other Pandemics Through History.png +-----+ https://www.cdc.gov/flu/pandemic-resources/basics/past-pandemics.html Influenza (Flu) 1918 H1N1 Spanish flu 1957-58 H2N2 1968 H3N3 2009 H1N1pdm09 The (H1N1)pdm09 virus was very different from H1N1 viruses that were circulating at the time of the pandemic. Few young people had any existing immunity (as detected by antibody response) to the (H1N1)pdm09 virus, but nearly one-third of people over 60 years old had antibodies against this virus, likely from exposure to an older H1N1 virus earlier in their lives. Since the (H1N1)pdm09 virus was very different from circulating H1N1 viruses, vaccination with seasonal flu vaccines offered little cross-protection against (H1N1)pdm09 virus infection. While a monovalent (H1N1)pdm09 vaccine was produced, it was not available in large quantities until late November—after the peak of illness during the second wave had come and gone in the United States. From April 12, 2009 to April 10, 2010, CDC estimated there were 60.8 million cases (range: 43.3-89.3 million), 274,304 hospitalizations (range: 195,086-402,719), and 12,469 deaths (range: 8868-18,306) in the United States due to the (H1N1)pdm09 virus. ********* 22May2020 Geomagnetic field strength geoMagField vs globaT 1860-2000, Vukcevic.gif from SuspObs : J. E. T. Channell, L. Vigliotti 29May2019 "The Role of Geomagnetic Field Intensity in Late Quaternary Evolution of Humans and Large Mammals" Reviews of Geophysics, Volume 57, Issue 3 Plain Language Summary : The strength of Earth's magnetic field in the past, recorded by rocks and sediments, provides a proxy for past flux of ultraviolet radiation (UVR) to Earth's surface due to the role of the field in modulating stratigraphic ozone. About 40,000 years ago, mammalian fossils in Australia and Eurasia record an important die‐off of large mammals that included Neanderthals in Europe. In the Americas and Europe, a large mammalian die‐off appears to have occurred ~13,000 years ago. Both die‐offs can be linked to minima in Earth's magnetic field strength implying that UVR flux variations to Earth's surface influenced mammalian evolution. For the last ~200,000 years, estimates of the timing of branching episodes in the human evolutionary tree, from modern and fossil DNA and Y chromosomes, can be linked to minima in field strength, which implies a long‐term role for UVR in human evolution. New fossil finds, improved fossil dating, knowledge of the past strength of Earth's magnetic field, and refinements in the human evolutionary tree, are sharpening the focus on a possible link between UVR arriving at the Earth's surface, magnetic field strength, and events in mammalian evolution. Suspicious Observers - Space Weather and Health video ********* 22May2020 Mar2018 overlap of NOAA & Potsdam 1. remove the month from Potsdam "music", save as : /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Kp data daily Potsdam Mar2018, music.txt 2. compare results for Mar2018 - see "Kp data check NOAA-Potsdam Mar2018.ods" >> all values are the same, so the equivalence of the data is confirmed Export image /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Kp_bins by3hr NOAA-Potsdam 1933-2020.jpg ********* 21May2020 zero-Kp days - links # loaddefs link d_Qndfs 'Kp geomag index - translate data files [NOAA, Potsdam].ndf' https://www.gfz-potsdam.de/en/kp-index/ ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETIC_DATA/INDICES/KP_AP/MONTHLY.DAT >> very different format, missing sunspots etc? >> This is Ap ONLY!! ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETIC_DATA/INDICES/KP_AP/MONTHLY.FMT ------------------------------------------------------------------------------- FORMAT FOR MONTHLY MEANS OF SELECTED GEOMAGNETIC INDICES ------------------------------------------------------------------------------- COLUMNS FMT DESCRIPTION ------------------------------------------------------------------------------- 1- 4 I4 YEAR 5- 6 I2 MONTH 7-10 I4 Monthly mean Ap PLANETARY EQUIVALENT DAILY AMPLITUDE 11-12 2X Blank 13-16 I4 Monthly mean ap or PLANETARY EQUIVALENT AMPLITUDE for 00-03 UT. 17-20 I4 Monthly mean ap or PLANETARY EQUIVALENT AMPLITUDE for 03-06 UT. 21-24 I4 Monthly mean ap or PLANETARY EQUIVALENT AMPLITUDE for 06-09 UT. 25-28 I4 Monthly mean ap or PLANETARY EQUIVALENT AMPLITUDE for 09-12 UT. 29-32 I4 Monthly mean ap or PLANETARY EQUIVALENT AMPLITUDE for 12-15 UT. 33-36 I4 Monthly mean ap or PLANETARY EQUIVALENT AMPLITUDE for 15-18 UT. 37-40 I4 Monthly mean ap or PLANETARY EQUIVALENT AMPLITUDE for 18-21 UT. 41-44 I4 Monthly mean ap or PLANETARY EQUIVALENT AMPLITUDE for 21-24 UT. 45-50 F6.2 Monthly mean Cp or PLANETARY DAILY CHARACTER FIGURE--a qualitative estimate of overall level of magnetic activity for the day determined from the sum of the eight ap amplitudes. Cp ranges, in steps of one-tenth, from 0 (quiet) to 2.5 (highly disturbed). 51-53 I3 Monthly mean C9--a conversion of the 0-to-2.5 range of the Cp index to one digit between 0 and 9. ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETIC_DATA/INDICES/KP_AP/MONTHLY.FMT save in : /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Pandemics, Kp, sunspot data 2019-20.txt After copying 1932-2018 daily tables, I need last two years! Must go to German source : https://www.gfz-potsdam.de/en/kp-index/ Definitive Kp Index and derived indices since 1932 ftp://ftp.gfz-potsdam.de/pub/home/obs/kp-ap/ ftp://ftp.gfz-potsdam.de/pub/home/obs/kp-ap/tab/ tab/: Subdirectory. Contains the monthly Kp/ap tables in a simple text format readable for both humans and computers (*.tab). The file tab_fmt.txt provides a format description. The tables distributed by regular mail (PostScript format) are also provided in this directory ftp://ftp.gfz-potsdam.de/pub/home/obs/kp-ap/tab/tab_fmt.txt The table begins with the daily lines Column Format Description ====== ====== =========== 1- 2 i2 yy, last two digits of year 3- 4 i2 mm, month (1-12) 5- 6 i2 dd, day of month (1-31) 8-19 4a3 3-hourly Kp indices, first 4 values 21-32 4a3 3-hourly Kp indices, last 4 values 35-38 a4 Daily Kp sum (supplied only for tradition, use Ap scientific purposes!) 39-42 a4 Most disturbed and quiet days; Q: most quiet days (1-10, 10th quiet day is marked Q0) D: most disturbed days (1-5) A, K: not really quiet day *: not really disturbed day 43-45 i3 Ap index 46-50 f5.2 Cp geomagnetic index. Either one line or four lines follow the daily lines 1. This line contains the monthly mean values for Ap and Cp 1- 2 i2 yy, last two digits of year 3- 4 i2 mm, month (1-12) 5- 6 i2 day is blank, may be read as zero 39-42 a4 "Mean", denotes monthly mean average of Ap and Cp 43-45 i3 Ap index (monthly mean) 46-50 f5.2 Cp geomagnetic index (monthly mean). Lines 2-4 in monthly distribution only 2. Empty line 3. This line contains the ordered most quiet days 1- 2 i2 yy, last two digits of year 3- 4 i2 mm, month (1-12) 5- 6 a2 " Q" 7-46 10a4 most quiet days 1 to 10 (day of month) A, K: not really quiet day 4. This line contains the ordered most disturbed days 1- 2 i2 yy, last two digits of year 3- 4 i2 mm, month (1-12) 5- 6 a2 " D" 7-26 5a4 most disturbed days 1 to 5 (day of month) *: not really disturbed day Accumulate data in : /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Pandemics, Kp data 2018-20 Potsdam.txt image : /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Pandemics, Kp data 2018-20 Potsdam.txt /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Pandemics, sunspots, Kp +-----+ ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETIC_DATA/INDICES/KP_AP/kp_ap.fmt describes data files ftp://ftp.swpc.noaa.gov/pub/indices/old_indices/ >> historical! >> pain in the ass to process! https://www.ngdc.noaa.gov/stp/GEOMAG/image/aastar07.jpg NOAA - AA geomagnetic index, annual number of days >=60 1868-2007.jpg NOAA - Sunspots and AA geomagnetic index, annual averages 1868-1992.gif Better data source https://www.ngdc.noaa.gov/geomag/indices/indices.html ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETIC_DATA/INDICES/KP_AP/ : save in : /media/bill/SWAPPER/Projects - mini/Pandemics, Kp index, sunspots/Pandemics, Kp, sunspot data 1932-2020 dailyy.txt >> I "melded" 2015 F5.1 data from : into : >> after that, no more F5.1??? (did Penticton shut down?) FORMAT FOR RECORDS OF SELECTED GEOMAGNETIC AND SOLAR ACTIVITY INDICES ------------------------------------------------------------------------------- COLUMNS FMT DESCRIPTION ------------------------------------------------------------------------------- 1- 2 I2 YEAR 3- 4 I2 MONTH 5- 6 I2 DAY 7-10 I4 BARTELS SOLAR ROTATION NUMBER--a sequence of 27-day intervals counted continuously from February 8, 1832. 11-12 I2 NUMBER OF DAY within the Bartels 27-day cycle. 13-14 I2 Kp or PLANETARY 3-HOUR RANGE INDEX for 0000 - 0300 UT. 15-16 I2 Kp or PLANETARY 3-HOUR RANGE INDEX for 0300 - 0600 UT. 17-18 I2 Kp or PLANETARY 3-HOUR RANGE INDEX for 0600 - 0900 UT. 19-20 I2 Kp or PLANETARY 3-HOUR RANGE INDEX for 0900 - 1200 UT. 21-22 I2 Kp or PLANETARY 3-HOUR RANGE INDEX for 1200 - 1500 UT. 23-24 I2 Kp or PLANETARY 3-HOUR RANGE INDEX for 1500 - 1800 UT. 25-26 I2 Kp or PLANETARY 3-HOUR RANGE INDEX for 1800 - 2100 UT. 27-28 I2 Kp or PLANETARY 3-HOUR RANGE INDEX for 2100 - 2400 UT. 29-31 I3 SUM of the eight Kp indices for the day expressed to the near- est third of a unit. 32-34 I3 ap or PLANETARY EQUIVALENT AMPLITUDE for 0000 - 0300 UT. 35-37 I3 ap or PLANETARY EQUIVALENT AMPLITUDE for 0300 - 0600 UT. 38-40 I3 ap or PLANETARY EQUIVALENT AMPLITUDE for 0600 - 0900 UT. 41-43 I3 ap or PLANETARY EQUIVALENT AMPLITUDE for 0900 - 1200 UT. 44-46 I3 ap or PLANETARY EQUIVALENT AMPLITUDE for 1200 - 1500 UT. 47-49 I3 ap or PLANETARY EQUIVALENT AMPLITUDE for 1500 - 1800 UT. 50-52 I3 ap or PLANETARY EQUIVALENT AMPLITUDE for 1800 - 2100 UT. 53-55 I3 ap or PLANETARY EQUIVALENT AMPLITUDE for 2100 - 2400 UT. 56-58 I3 Ap or PLANETARY EQUIVALENT DAILY AMPLITUDE--the arithmetic mean of the day's eight ap values. 59-61 F3.1 Cp or PLANETARY DAILY CHARACTER FIGURE--a qualitative estimate of overall level of magnetic activity for the day determined from the sum of the eight ap amplitudes. Cp ranges, in steps of one-tenth, from 0 (quiet) to 2.5 (highly disturbed). 62-62 I1 C9--a conversion of the 0-to-2.5 range of the Cp index to one digit between 0 and 9. 63-65 I3 INTERNATIONAL SUNSPOT NUMBER. Records contain the Zurich num- ber through December 31, 1980, and the International Brus- sels number thereafter. 66-70 F5.1 OTTAWA 10.7-CM SOLAR RADIO FLUX ADJUSTED TO 1 AU--measured at 1700 UT daily and expressed in units of 10 to the -22 Watts/ meter sq/hertz. Observations began on February 14, 1947. From that date through December 31, 1973, the fluxes given here don't reflect the revisions Ottawa made in 1966. NOTE: If a solar radio burst is in progress during the observation the pre-noon or afternoon value is used (as indicated by a flux qualifier value of 1 in column 71. 71-71 I1 FLUX QUALIFIER. "0" indicates flux required no adjustment; "1" indicates flux required adjustment for burst in progress at time of measurement; "2" indicates a flux approximated by either interpolation or extrapolation; and "3" indicates no observation. -------------------------------------------------------------------------------