{"content_id":"d2xczayimy","slug":"infection-surveillance-weekly-report-covid19-data","locale":"en","schema_type":"Article","category":"dataset_notes","category_name":"Dataset Notes","title":"COVID-19 Data in Weekly Sentinel Surveillance Reports","summary":"In week 40 of 2026, the COVID-19 detection rate at clinics fell, but hospital-reported admissions rose. Because these measures cover different groups and use different denominators, they must be interpreted with their reporting periods and surveillance systems kept distinct.","sponsorship_disclosure":null,"affiliate_disclosure":null,"commerce_disclosure":null,"author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["For week 40 of 2026, the reporting period runs from September 27 to October 3, and the official publication date is October 8.","The detection rate is the proportion of positive specimens among those tested under surveillance, not the proportion of the total population infected.","The fact that hospitalization reports and detection rates moved in different directions does not, by itself, establish a statistical error or a change in the course of the outbreak.","When comparing age groups, distinguish the number of detections, the detection rate within each age group, and each group's share of all patients.","Even within the same weekly report, different measures may cover different reporting weeks."],"content_markdown":"The detection rate in the weekly infectious disease sentinel surveillance bulletin is the proportion of tested specimens that were positive. The hospitalization indicator is the number of patients reported by participating institutions. In week 40 of 2026, the clinic-level detection rate fell while hospital-level admissions rose. The two figures cover different groups and must be interpreted together.\n\nThe figures in this article are based on the Korea Disease Control and Prevention Agency’s week 40 bulletin, published on October 8, 2026.\n\n## What does the weekly bulletin count?\n\nThe weekly bulletin shows trends in infectious diseases recorded by participating medical institutions. Patient surveillance covers information on patients identified through clinical visits or hospitalization. Pathogen surveillance covers viruses and bacteria identified in specimens. These statistics should not be treated as a complete count of infections across the entire population.\n\nThe main sections of the week 40 PDF are as follows.\n\n- Patient and pathogen surveillance for influenza, COVID-19, and acute respiratory infections\n- Patient and pathogen surveillance for intestinal infections\n- Surveillance for hand, foot, and mouth disease and enterovirus infections\n- Surveillance for epidemic keratoconjunctivitis and acute hemorrhagic conjunctivitis\n\nThe Korea Disease Control and Prevention Agency says the purpose of inpatient surveillance is to identify outbreak trends early. Results from different surveillance systems must be read within the scope of each system. [Korea Disease Control and Prevention Agency guide to acute respiratory infection surveillance](https://kdca.go.kr/kdca/3498/subview.do)\n\n## How do the reporting period and publication date differ?\n\nWeek 40 covers **September 27 to October 3, 2026**. The official publication date was October 8. A Reddit post discussing it was published on October 9. The publication date should not be mistaken for the date patients became ill.\n\n| Date category | Date | Meaning |\n| --- | --- | --- |\n| Reporting period | September 27 to October 3, 2026 | Period covered by the week 40 statistics |\n| Official publication date | October 8, 2026 | Date posted on the Korea Disease Control and Prevention Agency website |\n| Community post date | October 9, 2026 | Date of the post introducing the original bulletin |\n\nThe period covered by the official statistics and the posting date can be checked in the post and its attached PDF. [Korea Disease Control and Prevention Agency week 40 post for 2026](https://dportal.kdca.go.kr/pot/bbs/BD_selectBbs.do?q_bbsDocNo=20261008164653494\u0026q_bbsSn=1010\u0026q_clsfNo=2)\n\n## What does the COVID-19 detection rate mean?\n\nThe detection rate is the proportion of tested specimens in which the virus was found. It is calculated as “number of positive specimens ÷ number of specimens tested × 100.” The denominator is the specimens included in surveillance, not the entire population. The proportion therefore cannot be multiplied by the population to estimate the number of people infected.\n\nFor week 40, two rates were reported based on how the specimens were collected. Clinic-level outpatient surveillance and data from specialized testing medical institutions are separate. Even when they concern the same disease, the groups being tested may differ.\n\n| Indicator | Group covered | Week 39 of 2026 | Week 40 of 2026 |\n| --- | --- | --- | --- |\n| Clinic-level detection rate | Specimens from outpatients suspected of having respiratory infections | 14.2% | 9.0% |\n| Specialized testing institution detection rate | Specimens referred to 5 specialized testing medical institutions in Korea for respiratory virus genetic testing | 8.1% | 8.2% |\n| Hospital-level admissions reported | Patients hospitalized with COVID-19 at institutions participating in acute respiratory infection sentinel surveillance | 248 patients | 283 patients |\n| COVID-19 indicator in separate severe acute respiratory infection surveillance | Tertiary general hospital-level hospitalization indicator in the bulletin summary | 30 patients | 21 patients |\n\nThis comparison is based on the COVID-19 summary in the Korea Disease Control and Prevention Agency post. The original describes the change at specialized testing institutions as “similar to the previous week.” [Week 40 patient and pathogen surveillance results](https://dportal.kdca.go.kr/pot/bbs/BD_selectBbs.do?q_bbsDocNo=20261008164653494\u0026q_bbsSn=1010\u0026q_clsfNo=2)\n\nA simple average of the two detection rates is not an official combined indicator. Combining them would first require the number of tests in each dataset. Any overlap between specimens and differences in who was tested would also need to be checked.\n\n## What does the number of patients hospitalized with COVID-19 mean?\n\nThe hospitalization indicator is the number of patients reported by institutions participating in sentinel surveillance. The week 40 PDF says the acute respiratory infection surveillance system includes 223 hospital-level or higher institutions. This figure should not be read as the total number of admissions to all hospitals nationwide.\n\nSevere acute respiratory infection surveillance uses separate criteria for inclusion. The week 40 PDF lists 42 participating institutions at the general hospital level or higher. That indicator is not the total number of intensive care patients nationwide, either. Patient counts from the surveillance systems should not be added without checking for overlap.\n\nThe scope of this reporting is set out in the acute respiratory infection footnotes of the attached PDF. [Original week 40 PDF](https://dportal.kdca.go.kr/pot/component/file/ND_fileDownload.do?q_fileId=264ea2f0-5ab8-46ce-ba37-fde058e71921\u0026q_fileSn=4891845)\n\n## How to interpret indicators that move in different directions\n\nA detection rate and reported admissions moving in opposite directions is not, by itself, a contradiction. The two indicators do not come from a single dataset tracking the same people. There is also no basis for concluding that the change in one caused the change in the other.\n\n| Observed combination | What can be concluded | What else to check |\n| --- | --- | --- |\n| Detection rate falls, admissions rise | The proportion of positive specimens and reported admissions moved in different directions | Number of tests, age distribution of hospitalized patients, reporting dates |\n| Both indicators rise | Both surveillance systems show an increase | Trends over several weeks and changes in participating institutions |\n| Both indicators fall | Both surveillance systems show a decrease | Whether the decrease continues and whether any data is missing |\n| The two detection rates move in different directions | Changes differ by specimen collection route | Composition of referring institutions and who was tested |\n\nWeek 40 matches the first combination. This result alone cannot establish that infections fell nationwide or that severe illness increased. Differences in the timing of testing and hospitalization are a possibility to examine. More data is needed to determine the actual cause.\n\n## Calculation example: percentages and percentage points\n\nA difference between rates should be expressed in percentage points to make its meaning clear. The calculations below use the published figures for weeks 39 and 40 of 2026. Relative changes are rounded to two decimal places.\n\n| Calculation | Formula | Result |\n| --- | --- | --- |\n| Difference in the clinic-level detection rate | 9.0% - 14.2% | Down 5.2 percentage points |\n| Relative change in the clinic-level detection rate | (9.0 - 14.2) ÷ 14.2 × 100 | Down about 36.62% |\n| Difference in hospital-level admissions reported | 283 patients - 248 patients | Up 35 patients |\n| Relative change in hospital-level admissions reported | (283 - 248) ÷ 248 × 100 | Up about 14.11% |\n\nThe detection rate **fell 5.2 percentage points** from the previous week. Calling this a “5.2% decrease” would confuse it with the relative change. The calculated percentage decrease does not represent a decrease in the total number of people infected.\n\n## Compare the denominators before comparing figures by age\n\nThe number of detections by age and the detection rate by age are different measures. The age distribution of hospitalized patients is another separate indicator. Even if the same age group has a high figure in each, check what the denominator is.\n\n| Indicator | Denominator or unit | Question it can answer |\n| --- | --- | --- |\n| Number of detections by age | Number of positive specimens | How many detections were recorded in each age group? |\n| Detection rate by age | Number of specimens tested in that age group | What proportion of tested specimens were positive? |\n| Age distribution of hospitalized patients | Total number of hospitalized patients counted | What proportion of hospitalized patients are in that age group? |\n| Hospitalization rate by age and population | Population in that age group | How frequent is hospitalization relative to the size of the population? |\n\nThe week 40 PDF says detections through both specimen collection routes were mainly among older adults. Here, older adults means people aged 65 or older. This statement should not be treated as the infection rate among all older adults. [COVID-19 surveillance at clinics and specialized testing institutions in the week 40 PDF](https://dportal.kdca.go.kr/pot/component/file/ND_fileDownload.do?q_fileId=264ea2f0-5ab8-46ce-ba37-fde058e71921\u0026q_fileSn=4891845)\n\nWhen comparing figures, first distinguish weekly values from cumulative values. The boundaries of the age groups must also match. The age distribution for all respiratory infections should not be read as the distribution for COVID-19 alone.\n\n## Common mistake: assuming every table in an issue covers the same period\n\nThe bulletin’s issue number and the last reporting week in an individual table can differ. The enterovirus detection table in the week 40 PDF only goes through week 39. Automatically saving every item as week 40 data would therefore give some figures the wrong reporting period. This is why the dates in each table must be checked separately.\n\nWhen citing statistics or transferring them to a data file, record the following as well.\n\n- Disease and indicator names\n- Surveillance system and group covered\n- Reporting week shown in the table and the actual dates\n- Value and unit, and the denominator for any rate\n- Original publication date and date the data was checked\n- Whether the statistics are provisional and any subsequent revisions\n\nA footnote on pathogen surveillance in the Korea Disease Control and Prevention Agency’s week 40 PDF states:\n\n\u003e These are provisional statistics analyzed based on data reported at the time and may change.\n\nThis means values reported at the time may change later. If figures for the same week are revised in a later issue, the data versions must be distinguished. Avoid calculations that mix different published versions. [Reporting footnote in the week 40 PDF](https://dportal.kdca.go.kr/pot/component/file/ND_fileDownload.do?q_fileId=264ea2f0-5ab8-46ce-ba37-fde058e71921\u0026q_fileSn=4891845)\n\n## How should a winter outbreak forecast be distinguished from official statistics?\n\nA community forecast of a winter outbreak is the author’s interpretation. A Reddit post on October 9, 2026, suggested that this could lead to a winter outbreak. The forecast should not be cited as a confirmed announcement by the Korea Disease Control and Prevention Agency. [The Reddit post](https://www.reddit.com/r/Mogong/comments/1x19z6r/)\n\n| Type of information | How to present it |\n| --- | --- |\n| Official observation | Give the figure with its reporting period and surveillance system |\n| Value calculated directly | State the published figures and formula used |\n| Explanation of a cause | Distinguish a confirmed cause from a possibility to examine |\n| Forecast of a future outbreak | Identify who made the forecast and explain the uncertainty |\n\n## Frequently asked questions\n\n### Does a lower detection rate mean fewer people are infected overall?\n\nThat cannot be concluded. It means a smaller proportion of the tested specimens were positive. Other surveillance results are also needed to assess the overall scale of infections.\n\n### Which figure is more accurate, the clinic-level figure or the specialized testing institution figure?\n\nAccuracy cannot be compared based on the size of the two figures alone. The specimens are collected through different routes. The basic approach is to look at changes over time within the same surveillance system.\n\n### Do more reported admissions mean the virus has become more dangerous?\n\nAn increase in admissions alone cannot show whether the risk of severe illness has changed. The scale of infections and the age distribution of hospitalized patients may also matter. Assessing the cause requires analysis that links patient characteristics.\n\n### Where can I find the latest data?\n\nThe latest data is available in Infectious Disease News on the Korea Disease Control and Prevention Agency’s Infectious Disease Portal. (Figures checked for week 40 of 2026, September 27 to October 3: clinic-level COVID-19 detection rate 9.0%, specialized testing institution detection rate 8.2%, hospital-level hospitalized patients 283, tertiary general hospital-level hospitalized patients in the bulletin summary 21 · Source dportal.kdca.go.kr · Checked 2026-10-08) Open the weekly bulletin post under Publications and Newsletters. Check both the summary in the post and the tables and footnotes in the attached PDF.","content_html":"\u003cp\u003eThe detection rate in the weekly infectious disease sentinel surveillance bulletin is the proportion of tested specimens that were positive. The hospitalization indicator is the number of patients reported by participating institutions. In week 40 of 2026, the clinic-level detection rate fell while hospital-level admissions rose. The two figures cover different groups and must be interpreted together.\u003c/p\u003e\n\u003cp\u003eThe figures in this article are based on the Korea Disease Control and Prevention Agency’s week 40 bulletin, published on October 8, 2026.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#what-does-the-weekly-bulletin-count\" class=\"anchor\" id=\"what-does-the-weekly-bulletin-count\"\u003e\u003c/a\u003eWhat does the weekly bulletin count?\u003c/h2\u003e\n\u003cp\u003eThe weekly bulletin shows trends in infectious diseases recorded by participating medical institutions. Patient surveillance covers information on patients identified through clinical visits or hospitalization. Pathogen surveillance covers viruses and bacteria identified in specimens. These statistics should not be treated as a complete count of infections across the entire population.\u003c/p\u003e\n\u003cp\u003eThe main sections of the week 40 PDF are as follows.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePatient and pathogen surveillance for influenza, COVID-19, and acute respiratory infections\u003c/li\u003e\n\u003cli\u003ePatient and pathogen surveillance for intestinal infections\u003c/li\u003e\n\u003cli\u003eSurveillance for hand, foot, and mouth disease and enterovirus infections\u003c/li\u003e\n\u003cli\u003eSurveillance for epidemic keratoconjunctivitis and acute hemorrhagic conjunctivitis\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Korea Disease Control and Prevention Agency says the purpose of inpatient surveillance is to identify outbreak trends early. Results from different surveillance systems must be read within the scope of each system. \u003ca href=\"https://kdca.go.kr/kdca/3498/subview.do\"\u003eKorea Disease Control and Prevention Agency guide to acute respiratory infection surveillance\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-do-the-reporting-period-and-publication-date-differ\" class=\"anchor\" id=\"how-do-the-reporting-period-and-publication-date-differ\"\u003e\u003c/a\u003eHow do the reporting period and publication date differ?\u003c/h2\u003e\n\u003cp\u003eWeek 40 covers \u003cstrong\u003eSeptember 27 to October 3, 2026\u003c/strong\u003e. The official publication date was October 8. A Reddit post discussing it was published on October 9. The publication date should not be mistaken for the date patients became ill.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDate category\u003c/th\u003e\n\u003cth\u003eDate\u003c/th\u003e\n\u003cth\u003eMeaning\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Date category\"\u003eReporting period\u003c/td\u003e\n\u003ctd data-label=\"Date\"\u003eSeptember 27 to October 3, 2026\u003c/td\u003e\n\u003ctd data-label=\"Meaning\"\u003ePeriod covered by the week 40 statistics\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Date category\"\u003eOfficial publication date\u003c/td\u003e\n\u003ctd data-label=\"Date\"\u003eOctober 8, 2026\u003c/td\u003e\n\u003ctd data-label=\"Meaning\"\u003eDate posted on the Korea Disease Control and Prevention Agency website\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Date category\"\u003eCommunity post date\u003c/td\u003e\n\u003ctd data-label=\"Date\"\u003eOctober 9, 2026\u003c/td\u003e\n\u003ctd data-label=\"Meaning\"\u003eDate of the post introducing the original bulletin\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThe period covered by the official statistics and the posting date can be checked in the post and its attached PDF. \u003ca href=\"https://dportal.kdca.go.kr/pot/bbs/BD_selectBbs.do?q_bbsDocNo=20261008164653494\u0026amp;q_bbsSn=1010\u0026amp;q_clsfNo=2\"\u003eKorea Disease Control and Prevention Agency week 40 post for 2026\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#what-does-the-covid-19-detection-rate-mean\" class=\"anchor\" id=\"what-does-the-covid-19-detection-rate-mean\"\u003e\u003c/a\u003eWhat does the COVID-19 detection rate mean?\u003c/h2\u003e\n\u003cp\u003eThe detection rate is the proportion of tested specimens in which the virus was found. It is calculated as “number of positive specimens ÷ number of specimens tested × 100.” The denominator is the specimens included in surveillance, not the entire population. The proportion therefore cannot be multiplied by the population to estimate the number of people infected.\u003c/p\u003e\n\u003cp\u003eFor week 40, two rates were reported based on how the specimens were collected. Clinic-level outpatient surveillance and data from specialized testing medical institutions are separate. Even when they concern the same disease, the groups being tested may differ.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eIndicator\u003c/th\u003e\n\u003cth\u003eGroup covered\u003c/th\u003e\n\u003cth\u003eWeek 39 of 2026\u003c/th\u003e\n\u003cth\u003eWeek 40 of 2026\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Indicator\"\u003eClinic-level detection rate\u003c/td\u003e\n\u003ctd data-label=\"Group covered\"\u003eSpecimens from outpatients suspected of having respiratory infections\u003c/td\u003e\n\u003ctd data-label=\"Week 39 of 2026\"\u003e14.2%\u003c/td\u003e\n\u003ctd data-label=\"Week 40 of 2026\"\u003e9.0%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Indicator\"\u003eSpecialized testing institution detection rate\u003c/td\u003e\n\u003ctd data-label=\"Group covered\"\u003eSpecimens referred to 5 specialized testing medical institutions in Korea for respiratory virus genetic testing\u003c/td\u003e\n\u003ctd data-label=\"Week 39 of 2026\"\u003e8.1%\u003c/td\u003e\n\u003ctd data-label=\"Week 40 of 2026\"\u003e8.2%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Indicator\"\u003eHospital-level admissions reported\u003c/td\u003e\n\u003ctd data-label=\"Group covered\"\u003ePatients hospitalized with COVID-19 at institutions participating in acute respiratory infection sentinel surveillance\u003c/td\u003e\n\u003ctd data-label=\"Week 39 of 2026\"\u003e248 patients\u003c/td\u003e\n\u003ctd data-label=\"Week 40 of 2026\"\u003e283 patients\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Indicator\"\u003eCOVID-19 indicator in separate severe acute respiratory infection surveillance\u003c/td\u003e\n\u003ctd data-label=\"Group covered\"\u003eTertiary general hospital-level hospitalization indicator in the bulletin summary\u003c/td\u003e\n\u003ctd data-label=\"Week 39 of 2026\"\u003e30 patients\u003c/td\u003e\n\u003ctd data-label=\"Week 40 of 2026\"\u003e21 patients\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThis comparison is based on the COVID-19 summary in the Korea Disease Control and Prevention Agency post. The original describes the change at specialized testing institutions as “similar to the previous week.” \u003ca href=\"https://dportal.kdca.go.kr/pot/bbs/BD_selectBbs.do?q_bbsDocNo=20261008164653494\u0026amp;q_bbsSn=1010\u0026amp;q_clsfNo=2\"\u003eWeek 40 patient and pathogen surveillance results\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA simple average of the two detection rates is not an official combined indicator. Combining them would first require the number of tests in each dataset. Any overlap between specimens and differences in who was tested would also need to be checked.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#what-does-the-number-of-patients-hospitalized-with-covid-19-mean\" class=\"anchor\" id=\"what-does-the-number-of-patients-hospitalized-with-covid-19-mean\"\u003e\u003c/a\u003eWhat does the number of patients hospitalized with COVID-19 mean?\u003c/h2\u003e\n\u003cp\u003eThe hospitalization indicator is the number of patients reported by institutions participating in sentinel surveillance. The week 40 PDF says the acute respiratory infection surveillance system includes 223 hospital-level or higher institutions. This figure should not be read as the total number of admissions to all hospitals nationwide.\u003c/p\u003e\n\u003cp\u003eSevere acute respiratory infection surveillance uses separate criteria for inclusion. The week 40 PDF lists 42 participating institutions at the general hospital level or higher. That indicator is not the total number of intensive care patients nationwide, either. Patient counts from the surveillance systems should not be added without checking for overlap.\u003c/p\u003e\n\u003cp\u003eThe scope of this reporting is set out in the acute respiratory infection footnotes of the attached PDF. \u003ca href=\"https://dportal.kdca.go.kr/pot/component/file/ND_fileDownload.do?q_fileId=264ea2f0-5ab8-46ce-ba37-fde058e71921\u0026amp;q_fileSn=4891845\"\u003eOriginal week 40 PDF\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-to-interpret-indicators-that-move-in-different-directions\" class=\"anchor\" id=\"how-to-interpret-indicators-that-move-in-different-directions\"\u003e\u003c/a\u003eHow to interpret indicators that move in different directions\u003c/h2\u003e\n\u003cp\u003eA detection rate and reported admissions moving in opposite directions is not, by itself, a contradiction. The two indicators do not come from a single dataset tracking the same people. There is also no basis for concluding that the change in one caused the change in the other.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eObserved combination\u003c/th\u003e\n\u003cth\u003eWhat can be concluded\u003c/th\u003e\n\u003cth\u003eWhat else to check\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Observed combination\"\u003eDetection rate falls, admissions rise\u003c/td\u003e\n\u003ctd data-label=\"What can be concluded\"\u003eThe proportion of positive specimens and reported admissions moved in different directions\u003c/td\u003e\n\u003ctd data-label=\"What else to check\"\u003eNumber of tests, age distribution of hospitalized patients, reporting dates\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Observed combination\"\u003eBoth indicators rise\u003c/td\u003e\n\u003ctd data-label=\"What can be concluded\"\u003eBoth surveillance systems show an increase\u003c/td\u003e\n\u003ctd data-label=\"What else to check\"\u003eTrends over several weeks and changes in participating institutions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Observed combination\"\u003eBoth indicators fall\u003c/td\u003e\n\u003ctd data-label=\"What can be concluded\"\u003eBoth surveillance systems show a decrease\u003c/td\u003e\n\u003ctd data-label=\"What else to check\"\u003eWhether the decrease continues and whether any data is missing\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Observed combination\"\u003eThe two detection rates move in different directions\u003c/td\u003e\n\u003ctd data-label=\"What can be concluded\"\u003eChanges differ by specimen collection route\u003c/td\u003e\n\u003ctd data-label=\"What else to check\"\u003eComposition of referring institutions and who was tested\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eWeek 40 matches the first combination. This result alone cannot establish that infections fell nationwide or that severe illness increased. Differences in the timing of testing and hospitalization are a possibility to examine. More data is needed to determine the actual cause.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#calculation-example-percentages-and-percentage-points\" class=\"anchor\" id=\"calculation-example-percentages-and-percentage-points\"\u003e\u003c/a\u003eCalculation example: percentages and percentage points\u003c/h2\u003e\n\u003cp\u003eA difference between rates should be expressed in percentage points to make its meaning clear. The calculations below use the published figures for weeks 39 and 40 of 2026. Relative changes are rounded to two decimal places.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCalculation\u003c/th\u003e\n\u003cth\u003eFormula\u003c/th\u003e\n\u003cth\u003eResult\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Calculation\"\u003eDifference in the clinic-level detection rate\u003c/td\u003e\n\u003ctd data-label=\"Formula\"\u003e9.0% - 14.2%\u003c/td\u003e\n\u003ctd data-label=\"Result\"\u003eDown 5.2 percentage points\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Calculation\"\u003eRelative change in the clinic-level detection rate\u003c/td\u003e\n\u003ctd data-label=\"Formula\"\u003e(9.0 - 14.2) ÷ 14.2 × 100\u003c/td\u003e\n\u003ctd data-label=\"Result\"\u003eDown about 36.62%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Calculation\"\u003eDifference in hospital-level admissions reported\u003c/td\u003e\n\u003ctd data-label=\"Formula\"\u003e283 patients - 248 patients\u003c/td\u003e\n\u003ctd data-label=\"Result\"\u003eUp 35 patients\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Calculation\"\u003eRelative change in hospital-level admissions reported\u003c/td\u003e\n\u003ctd data-label=\"Formula\"\u003e(283 - 248) ÷ 248 × 100\u003c/td\u003e\n\u003ctd data-label=\"Result\"\u003eUp about 14.11%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThe detection rate \u003cstrong\u003efell 5.2 percentage points\u003c/strong\u003e from the previous week. Calling this a “5.2% decrease” would confuse it with the relative change. The calculated percentage decrease does not represent a decrease in the total number of people infected.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#compare-the-denominators-before-comparing-figures-by-age\" class=\"anchor\" id=\"compare-the-denominators-before-comparing-figures-by-age\"\u003e\u003c/a\u003eCompare the denominators before comparing figures by age\u003c/h2\u003e\n\u003cp\u003eThe number of detections by age and the detection rate by age are different measures. The age distribution of hospitalized patients is another separate indicator. Even if the same age group has a high figure in each, check what the denominator is.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eIndicator\u003c/th\u003e\n\u003cth\u003eDenominator or unit\u003c/th\u003e\n\u003cth\u003eQuestion it can answer\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Indicator\"\u003eNumber of detections by age\u003c/td\u003e\n\u003ctd data-label=\"Denominator or unit\"\u003eNumber of positive specimens\u003c/td\u003e\n\u003ctd data-label=\"Question it can answer\"\u003eHow many detections were recorded in each age group?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Indicator\"\u003eDetection rate by age\u003c/td\u003e\n\u003ctd data-label=\"Denominator or unit\"\u003eNumber of specimens tested in that age group\u003c/td\u003e\n\u003ctd data-label=\"Question it can answer\"\u003eWhat proportion of tested specimens were positive?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Indicator\"\u003eAge distribution of hospitalized patients\u003c/td\u003e\n\u003ctd data-label=\"Denominator or unit\"\u003eTotal number of hospitalized patients counted\u003c/td\u003e\n\u003ctd data-label=\"Question it can answer\"\u003eWhat proportion of hospitalized patients are in that age group?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Indicator\"\u003eHospitalization rate by age and population\u003c/td\u003e\n\u003ctd data-label=\"Denominator or unit\"\u003ePopulation in that age group\u003c/td\u003e\n\u003ctd data-label=\"Question it can answer\"\u003eHow frequent is hospitalization relative to the size of the population?\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThe week 40 PDF says detections through both specimen collection routes were mainly among older adults. Here, older adults means people aged 65 or older. This statement should not be treated as the infection rate among all older adults. \u003ca href=\"https://dportal.kdca.go.kr/pot/component/file/ND_fileDownload.do?q_fileId=264ea2f0-5ab8-46ce-ba37-fde058e71921\u0026amp;q_fileSn=4891845\"\u003eCOVID-19 surveillance at clinics and specialized testing institutions in the week 40 PDF\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWhen comparing figures, first distinguish weekly values from cumulative values. The boundaries of the age groups must also match. The age distribution for all respiratory infections should not be read as the distribution for COVID-19 alone.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#common-mistake-assuming-every-table-in-an-issue-covers-the-same-period\" class=\"anchor\" id=\"common-mistake-assuming-every-table-in-an-issue-covers-the-same-period\"\u003e\u003c/a\u003eCommon mistake: assuming every table in an issue covers the same period\u003c/h2\u003e\n\u003cp\u003eThe bulletin’s issue number and the last reporting week in an individual table can differ. The enterovirus detection table in the week 40 PDF only goes through week 39. Automatically saving every item as week 40 data would therefore give some figures the wrong reporting period. This is why the dates in each table must be checked separately.\u003c/p\u003e\n\u003cp\u003eWhen citing statistics or transferring them to a data file, record the following as well.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDisease and indicator names\u003c/li\u003e\n\u003cli\u003eSurveillance system and group covered\u003c/li\u003e\n\u003cli\u003eReporting week shown in the table and the actual dates\u003c/li\u003e\n\u003cli\u003eValue and unit, and the denominator for any rate\u003c/li\u003e\n\u003cli\u003eOriginal publication date and date the data was checked\u003c/li\u003e\n\u003cli\u003eWhether the statistics are provisional and any subsequent revisions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA footnote on pathogen surveillance in the Korea Disease Control and Prevention Agency’s week 40 PDF states:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThese are provisional statistics analyzed based on data reported at the time and may change.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis means values reported at the time may change later. If figures for the same week are revised in a later issue, the data versions must be distinguished. Avoid calculations that mix different published versions. \u003ca href=\"https://dportal.kdca.go.kr/pot/component/file/ND_fileDownload.do?q_fileId=264ea2f0-5ab8-46ce-ba37-fde058e71921\u0026amp;q_fileSn=4891845\"\u003eReporting footnote in the week 40 PDF\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-should-a-winter-outbreak-forecast-be-distinguished-from-official-statistics\" class=\"anchor\" id=\"how-should-a-winter-outbreak-forecast-be-distinguished-from-official-statistics\"\u003e\u003c/a\u003eHow should a winter outbreak forecast be distinguished from official statistics?\u003c/h2\u003e\n\u003cp\u003eA community forecast of a winter outbreak is the author’s interpretation. A Reddit post on October 9, 2026, suggested that this could lead to a winter outbreak. The forecast should not be cited as a confirmed announcement by the Korea Disease Control and Prevention Agency. \u003ca href=\"https://www.reddit.com/r/Mogong/comments/1x19z6r/\"\u003eThe Reddit post\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType of information\u003c/th\u003e\n\u003cth\u003eHow to present it\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Type of information\"\u003eOfficial observation\u003c/td\u003e\n\u003ctd data-label=\"How to present it\"\u003eGive the figure with its reporting period and surveillance system\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Type of information\"\u003eValue calculated directly\u003c/td\u003e\n\u003ctd data-label=\"How to present it\"\u003eState the published figures and formula used\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Type of information\"\u003eExplanation of a cause\u003c/td\u003e\n\u003ctd data-label=\"How to present it\"\u003eDistinguish a confirmed cause from a possibility to examine\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Type of information\"\u003eForecast of a future outbreak\u003c/td\u003e\n\u003ctd data-label=\"How to present it\"\u003eIdentify who made the forecast and explain the uncertainty\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca href=\"#frequently-asked-questions\" class=\"anchor\" id=\"frequently-asked-questions\"\u003e\u003c/a\u003eFrequently asked questions\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca href=\"#does-a-lower-detection-rate-mean-fewer-people-are-infected-overall\" class=\"anchor\" id=\"does-a-lower-detection-rate-mean-fewer-people-are-infected-overall\"\u003e\u003c/a\u003eDoes a lower detection rate mean fewer people are infected overall?\u003c/h3\u003e\n\u003cp\u003eThat cannot be concluded. It means a smaller proportion of the tested specimens were positive. Other surveillance results are also needed to assess the overall scale of infections.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#which-figure-is-more-accurate-the-clinic-level-figure-or-the-specialized-testing-institution-figure\" class=\"anchor\" id=\"which-figure-is-more-accurate-the-clinic-level-figure-or-the-specialized-testing-institution-figure\"\u003e\u003c/a\u003eWhich figure is more accurate, the clinic-level figure or the specialized testing institution figure?\u003c/h3\u003e\n\u003cp\u003eAccuracy cannot be compared based on the size of the two figures alone. The specimens are collected through different routes. The basic approach is to look at changes over time within the same surveillance system.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#do-more-reported-admissions-mean-the-virus-has-become-more-dangerous\" class=\"anchor\" id=\"do-more-reported-admissions-mean-the-virus-has-become-more-dangerous\"\u003e\u003c/a\u003eDo more reported admissions mean the virus has become more dangerous?\u003c/h3\u003e\n\u003cp\u003eAn increase in admissions alone cannot show whether the risk of severe illness has changed. The scale of infections and the age distribution of hospitalized patients may also matter. Assessing the cause requires analysis that links patient characteristics.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#where-can-i-find-the-latest-data\" class=\"anchor\" id=\"where-can-i-find-the-latest-data\"\u003e\u003c/a\u003eWhere can I find the latest data?\u003c/h3\u003e\n\u003cp\u003eThe latest data is available in Infectious Disease News on the Korea Disease Control and Prevention Agency’s Infectious Disease Portal. (Figures checked for week 40 of 2026, September 27 to October 3: clinic-level COVID-19 detection rate 9.0%, specialized testing institution detection rate 8.2%, hospital-level hospitalized patients 283, tertiary general hospital-level hospitalized patients in the bulletin summary 21 · Source dportal.kdca.go.kr · Checked 2026-10-08) Open the weekly bulletin post under Publications and Newsletters. Check both the summary in the post and the tables and footnotes in the attached PDF.\u003c/p\u003e\n","tags":["Infectious disease surveillance","COVID-19"],"faqs":[{"question":"What are the data collection period and official publication date for week 40 of 2026?","answer":"The data collection period is September 27 through October 3, 2026. The official publication date is October 8. The date of the community post must be distinguished from the period covered by the statistics."},{"question":"Is the denominator for the detection rate the entire population?","answer":"The denominator is the specimens tested under the relevant surveillance system. It is not a rate calculated for the entire population. Do not multiply the rate by the population to estimate the total number of infected people."},{"question":"Can the rates for clinics and specialized testing institutions be averaged?","answer":"A simple average is not an official combined indicator. The number of tests performed by each is needed to combine them. Duplicate specimens and differences in the groups surveyed must also be checked."},{"question":"Can reported hospitalizations rise even if the detection rate falls?","answer":"The two indicators cover different groups and measure different things, so this can happen. Just because they move in different directions does not mean there is a statistical error. Identifying the cause requires further analysis of the groups tested and the composition of hospitalized patients, among other things."},{"question":"Does the hospital-level hospitalization indicator count every hospitalized patient nationwide?","answer":"It is the number of patients reported by institutions participating in sentinel surveillance. It should not be interpreted as the total number of hospitalizations across all medical institutions nationwide. It must also be distinguished from the results of separate surveillance for severe acute respiratory infections."},{"question":"What is the difference between percent and percentage points?","answer":"A percentage point is the difference between two percentages. Relative change is calculated by dividing that difference by the earlier percentage. State which calculation was used."},{"question":"Are age-specific detection rates and the age distribution of hospitalized patients the same?","answer":"They have different denominators. The age-specific detection rate is based on specimens tested in that age group. The age distribution of hospitalized patients is based on all hospitalized patients counted."},{"question":"Are all the tables in the same bulletin based on data from the same week?","answer":"The last week covered may differ by item. Check the periods in each table's title and footnotes separately. The bulletin's issue number alone should not be used to assign dates to all values."},{"question":"Can preliminary statistics be revised later?","answer":"Because they are compiled from data available at the time of reporting, they may be revised. When comparing them, record the publication date and the date you checked the data. If values for the same week change, distinguish between the published versions."},{"question":"Can the week 40 results confirm a winter outbreak?","answer":"One week's indicators alone cannot confirm a winter outbreak. The forecast in the Reddit post is the author's interpretation. Observed values and future forecasts should be distinguished by source and wording."}],"sources":[{"url":"https://dportal.kdca.go.kr/pot/bbs/BD_selectBbs.do?q_bbsDocNo=20261008164653494\u0026q_bbsSn=1010\u0026q_clsfNo=2","title":"Korea Disease Control and Prevention Agency 2026 Infectious Disease Sentinel Surveillance Weekly Newsletter, Week 40, posted October 8, 2026","type":"data_point"},{"url":"https://dportal.kdca.go.kr/pot/component/file/ND_fileDownload.do?q_fileId=264ea2f0-5ab8-46ce-ba37-fde058e71921\u0026q_fileSn=4891845","title":"Korea Disease Control and Prevention Agency 2026 Infectious Disease Sentinel Surveillance Weekly Newsletter, Week 40 PDF","type":"source"},{"url":"https://kdca.go.kr/kdca/3498/subview.do","title":"Korea Disease Control and Prevention Agency guidance on surveillance of patients hospitalized with acute respiratory infections","type":"source"},{"url":"https://www.reddit.com/r/Mogong/comments/1x19z6r/","title":"Reddit r/Mogong weekly COVID post for Week 40 of 2026","type":"source"}],"images":[{"id":1718,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MjYyMDcsInB1ciI6ImJsb2JfaWQifX0=--14fd9363fa16e66d49cfbc3b2e3f504f3e61d54b/ai-b9faef86.webp","is_representative":true,"generation_method":"ai_photo","license":"ai_generated","mime_type":"image/webp","width":1536,"height":1024,"translations":{"ko":{"alt":"병실 침대에 앉은 노인 환자를 간호사가 살피고, 뒤로 다른 침대가 보인다.","caption":"입원 신고와 바이러스 검출률은 감시 대상이 달라 추이가 엇갈릴 수 있다.","description":null},"en":{"alt":"A nurse checks on an older man sitting up in a hospital bed, with other beds in the background.","caption":"Hospital admission reports and virus detection rates can move in different directions because they track different groups.","description":null},"ja":{"alt":"病室のベッドで上体を起こす高齢男性を看護師が見守り、奥に別のベッドが見える。","caption":"入院報告とウイルス検出率は監視対象が異なるため、増減の方向が食い違うことがある。","description":null},"es":{"alt":"Una enfermera atiende a un hombre mayor sentado en una cama de hospital; al fondo hay otras camas.","caption":"Los ingresos notificados y la tasa de detección pueden evolucionar de forma distinta porque miden grupos diferentes.","description":null},"id":{"alt":"Perawat memeriksa pria lansia yang duduk di ranjang rumah sakit, dengan ranjang lain di belakang.","caption":"Laporan rawat inap dan tingkat deteksi virus dapat bergerak berlawanan karena kelompok yang dipantau berbeda.","description":null},"pt":{"alt":"Uma enfermeira acompanha um homem idoso sentado numa cama hospitalar, com outras camas ao fundo.","caption":"Os internamentos notificados e a taxa de deteção podem seguir tendências distintas porque abrangem grupos diferentes.","description":null},"zh-hant":{"alt":"護理師查看坐在病床上的年長男子，後方還有其他病床。","caption":"住院通報與病毒檢出率的監測對象不同，變動方向可能不一致。","description":null},"de":{"alt":"Eine Pflegekraft sieht nach einem älteren Mann, der aufrecht im Krankenhausbett sitzt; hinten stehen weitere Betten.","caption":"Gemeldete Krankenhausaufnahmen und Virusnachweisraten können sich unterschiedlich entwickeln, da sie verschiedene Gruppen erfassen.","description":null}}},{"id":1719,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MjYyMTMsInB1ciI6ImJsb2JfaWQifX0=--aa83ad08d965b63be088f1a49dd6609666fac131/ai-4151984f.webp","is_representative":false,"generation_method":"ai_semi","license":"ai_generated","mime_type":"image/webp","width":1536,"height":1024,"translations":{"ko":{"alt":"호흡기 진료실에서 마스크를 쓴 의료진이 앉아 있는 여성의 코에서 검체를 채취한다.","caption":"바이러스 검출률은 감시 대상 검체 중 양성 비율이지 전체 인구의 감염 비율이 아니다.","description":null},"en":{"alt":"A masked clinician takes a nasal swab from a seated woman in a respiratory clinic.","caption":"The detection rate measures positive results among specimens tested under surveillance, not infections in the whole population.","description":null},"ja":{"alt":"呼吸器外来で、マスクを着けた医療従事者が座っている女性の鼻から検体を採取する。","caption":"検出率は監視対象の検体に占める陽性の割合であり、人口全体の感染割合ではない。","description":null},"es":{"alt":"Una profesional sanitaria con mascarilla toma una muestra nasal a una mujer sentada en una clínica respiratoria.","caption":"La tasa de detección indica la proporción de positivos entre las muestras analizadas bajo vigilancia, no la de infectados en toda la población.","description":null},"id":{"alt":"Petugas kesehatan bermasker mengambil sampel usap hidung dari seorang perempuan yang duduk di klinik pernapasan.","caption":"Tingkat deteksi menunjukkan proporsi hasil positif dari sampel yang diuji dalam surveilans, bukan proporsi penduduk yang terinfeksi.","description":null},"pt":{"alt":"Uma profissional de saúde de máscara coleta uma amostra nasal de uma mulher sentada em uma clínica respiratória.","caption":"A taxa de detecção indica a proporção de resultados positivos entre as amostras testadas na vigilância, não a de infectados na população.","description":null},"zh-hant":{"alt":"呼吸道診所內，戴口罩的醫護人員正替坐著的女子採集鼻腔檢體。","caption":"檢出率是監測檢體中的陽性比例，不代表全體人口的感染比例。","description":null},"de":{"alt":"Eine maskierte medizinische Fachkraft nimmt in einer Atemwegsambulanz bei einer sitzenden Frau einen Nasenabstrich.","caption":"Die Nachweisrate bezeichnet den Anteil positiver Ergebnisse unter den überwacht getesteten Proben, nicht den Anteil Infizierter an der Gesamtbevölkerung.","description":null}}}],"published_at":"2026-10-09T16:12:58+09:00","updated_at":"2026-10-09T16:12:58+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant","de"],"url":"https://injoys.com/en/articles/infection-surveillance-weekly-report-covid19-data"}