key: cord-0777381-w3608lnh authors: Zhang, P.-W.; Zhang, S. H.; Li, W.-F.; Keuthan, C. J.; Li, S.; Takaesu, F.; Berlinicke, C. A.; Wan, J.; Sun, J.; Zack, D. J. title: Fatality assessment and variant risk monitoring for COVID-19 using three new hospital occupancy related metrics date: 2022-02-04 journal: nan DOI: 10.1101/2022.02.03.22270417 sha: 94360358a1f931cd4299ff382d00e06a0d6f4f97 doc_id: 777381 cord_uid: w3608lnh Background Though case fatality rate (CFR) and hospital mortality rate (HMR) are used to reflect COVID-19 fatality risk, they are limited by temporal and spatial variation of CFR and availability of HMR. Alternative metrics are needed for COVID-19 fatality measurement and variant risk monitoring. Methods New metrics and their applications in fatality measurements and risk monitoring are proposed here. We also introduce a new mathematical model to estimate average hospital length of stay for death (Ldead) and discharges (Ldis). Multiple data sources were used for our analysis. Findings We propose three new metrics, hospital occupancy mortality rate (HOMR), ratio of total deaths to hospital occupancy (TDHOR) and ratio of hospital occupancy to cases (HOCR), for dynamic assessment of COVID-19 fatality risk. Estimated Ldead and Ldis for 501079 COVID-19 hospitalizations in US 34 states between Aug 7, 2020 and Mar 1, 2021 were 14.0 and 18.2 days, respectively. We found that TDHOR values of 27 countries are less spatially and temporally variable and more capable of detecting changes in COVID-19 fatality risk. The dramatic changes in COVID-19 CFR observed in 27 countries during early stages of the pandemic were mostly caused by undiagnosed cases. Compared to the first week of November, 2021, the week mean HOCRs (mimics hospitalization-to-case ratio) for Omicron variant decreased 34.08% and 65.16% in the United Kingdom and USA respectively as of Jan 16, 2022. Interpretation These new and reliable measurements for COVID-19 that could be expanded as a general index to other fatal infectious diseases for disease fatality risk and variant-associated risk monitoring. Though case fatality rate (CFR) is widely used to reflect COVID-19 fatality risk, it's use is limited by large temporal and spatial variation. Hospital mortality rate (HMR) is also used to 5 assess the severity of COVID-19, but HMR data is not directly available except 35 states of USA. Alternative metrics are needed for COVID-19 severity and fatality assessment. New metrics and their applications in fatality measurements and risk monitoring are 10 proposed here. We also introduce a new mathematical model to estimate average hospital length of stay for death (L dead ) and discharges (L dis ). Multiple data sources were used for our analysis. We propose three new metrics, hospital occupancy mortality rate (HOMR), ratio of total deaths to hospital occupancy (TDHOR) and ratio of hospital occupancy to cases (HOCR), for dynamic assessment of COVID-19 fatality risk. Estimated L dead and L dis for 501,079 COVID-19 hospitalizations in US 34 states between Aug 7, 2020 and Mar 1, 2021 were 14·0 and 18·2 days, respectively. We found that TDHOR values of 27 countries are less spatially and 20 temporally variable and more capable of detecting changes in COVID-19 fatality risk. The dramatic changes in COVID-19 CFR observed in 27 countries during early stages of the pandemic were mostly caused by undiagnosed cases. Compared to the first week of November, 2021, the week mean HOCRs (mimics hospitalization-to-case ratio) for Omicron variant decreased 34·08% and 65·16% in the United Kingdom and USA respectively as of 25 Jan 16, 2022. These new and reliable measurements for COVID-19 that could be expanded as a general index to other fatal infectious diseases for disease fatality risk and variant-associated risk 30 monitoring. Evidence before this study 35 We searched PubMed, medRxiv, and bioRxiv for peer-reviewed articles, preprints, and research reports on risk and health care evaluation for COVID-19 using the search terms "hospital occupancy mortality rate", "ratio of total deaths to hospital occupancy", "ratio of hospital occupancy to case" up to Jan 20, 2022. No similar concepts or studies were found. No similar mathematical models based on "hospital occupancy mortality rate" for the 40 estimation of hospital length of stay for deaths and discharges have been identified to date. Our new metrics, HOMR and TDHOR, mimic HMR for COVID-19 fatality risk assessment but utilize readily available data for many US states and countries around the world. HOCR 45 mimics hospitalization-to-case ratio for COVID-19. We also provide evidence that explains The case fatality rate (CFR), which represents the fraction of individuals with a particular disease who die from that disease, is one of the most commonly used metrics for assessing the severity of infectious disease outbreaks. CFR has been important in aiding county, state, and national leaders in making informed public health decisions related to the 20 ongoing COVID-19 pandemic, as well as management of many other outbreaks such as those involving influenza, SARS-CoV-1 and MERS-CoV, diseases in which the CFR has ranged from as little as 0·12% to as high as 32·7% 1-3 . CFR can have large temporal and spatial variation. For example, reported COVID-19 CFR values have varied from 0·048% (Singapore) to 10·16% (Mexico) as of Oct 20, 2020 4 . CFR can also vary highly even within 25 an individual country or region at different stages of a disease outbreak 5 . A multitude of factors could potentially contribute to regional CFR incongruities, including patient access to health care, testing capacity, age, race, sampling, vaccination status, personal compliance to government guidance, and evolving SARS-CoV-2 variants 6-9 . RNA viruses, which include SARS-CoV-2, are more rapidly mutating than DNA 30 viruses. New viral variants raise widespread concern, especially when the mutations cause substantial changes in antigenicity, transmissibility, and virulence. SARS-CoV-2 variants of concern, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and the recently emerged highly infectious Omicron (B.1.1.529) variant, which have already spread around the world 10- 15 . 35 The world's COVID-19 experience has clearly demonstrated the reliance of informed public health decision making on having available easy, accurate, and reliable methods to rapidly monitor fatality and hospitalization risk of newly emerging variants and diseases. To address this need, and improve existing methods, we here propose hospital occupancy mortality rate (HOMR), ratio of total deaths to hospital occupancy (TDHOR) and ratio of 40 hospital occupancy to cases (HOCR) as three, new, alternative and complementary measurements for COVID-19 fatality risk evaluation, early CFR variation analysis, and dynamic monitoring. Hospital mortality rate (HMR) is commonly used as an indicator of patient safety and 5 quality of care in healthcare facilities, and it is also being used to assess the severity of the 19 . Is there any way to link daily hospital occupancy to cumulative hospitalization? We found that the sum of daily hospital occupancy (HO) for a period and cumulative hospitalization can be bridged together by average length of stay for hospitalizations (L mean, for both deaths and discharges). That means "sum of daily hospital occupancy" equals L mean times cumulative hospitalizations for a specific period. 15 If HMR for COVID-19 is a ratio of hospital deaths to cumulative hospitalizations, what is a ratio of hospital deaths to the sum of daily hospital occupancy for a period? We propose this to be hospital occupancy mortality rate (HOMR), which is the daily HMR of the hospital stay period since HMR equals HOMR times L mean . HOMR is a new hospital occupancy related metric and may mimic HMR to a certain degree since it is directly related 20 to HMR through L mean . As noted above, CFR variation for COVID-19 can arise from variation in deaths, cases, or both. Some countries and USA states provide COVID-19 death data broken down into hospital deaths, long term facility deaths, and deaths at home. China and another 27 countries only release total deaths. To address this limitation in available data, we introduce a 25 concept of ratio of total deaths to the sum of daily hospital occupancy (TDHOR) for a specific period. In contrast to hospitalization-to-case ratio (HCR, a ratio of cumulative hospitalizations to cases), the ratio of hospital occupancy to cases (HOCR) is an additional hospital occupancy metric derived in this study and HOCR can mimic HCR since HOCR equals HCR times L mean . Together, HOMR, TDHOR and HOCR provide three new hospital 30 occupancy related metrics. In regional population screening in mainland China, asymptomatic cases have been reported to represent 38·23% of total cases from Apr 18, 2020 to Jan 23, 2022 (Appendix Table 1 ). Such information is generally not available since in most parts of the world COVID-19 cases were found not from population screening but rather from diagnosed cases 35 with symptoms, which equals total symptomatic cases minus undiagnosed cases. The CFR mentioned in the present study refers to symptomatic CFR if not otherwise specified 20 . The intrinsic relationships among CFR, HMR, and these three new metrics, are shown in formulas 1-2 below (Methods 1, formulas 3-13): 5 patients), discharges and deaths, respectively; 2) N dead , N dis and N hc are in-patient numbers for deaths, discharges and patients currently in hospital; 3) HO is hospital occupancy for a period (Methods 1, Formula 5); 4) The ƒ(N hc ) is the sum of period hospital stay for N hc . Usually N hc < sum of recently new admitted patients for n days, n is close to L mean and ƒ(N hc ) < N hc * L mean . 10 Average hospital length of stay for COVID-19 deaths (L dead ) and discharges (L dis ) were estimated using HOMR in US Length of stay in the hospital for deaths and discharges are useful fatality risk measurements for the COVID-19 pandemic because they reveal risk information for severe cases, which is missing in the CFR calculation. Several studies have addressed this at the 15 beginning of the COVID-19 outbreak [21] [22] . L dead and L dis can be estimated if we know HOMR and HMR for multiple days, since 1/HOMR≈ L dis * (N dis / N dead ) + L dead = L dis * (1/HMR -1) + L dead (combination of formula 1 and 4). There were 34 states within the US that we could use publicly available hospital deaths and cumulative hospitalization data for analysis, with 174,167 deaths and 501, 079 20 hospitalizations within this period (Table 1) . Interestingly, the regression plot for these states revealed different correlations at three different time periods (Fig.1A ): Jun 26, 2020 to Aug 6, 2020 (42 days), Aug 7, 2020 to Nov 15, 2020 (101 days), and Nov 16, 2020 to Mar 1, 2021 (106 days). While the latter two time periods had nearly perfect linear correlation (r 2 values of 0.97 and 0.99), for the first period and overall the correlations were limited (r 2 25 values of 0·57 and 0·75, respectively). Estimated L dead and L dis for the latter two time periods are showed in Table 1 based on their intercepts of the Y axis and slope ( Fig.1B-D) . These data indicated that L dead and L dis were constant within these two periods, and the changes between them were subtle. At the time of Feb 15, 2021, only 4·24% of population was fully vaccinated against COVID-19 in the US, so our estimations should not likely be affected by 30 vaccination rates 4 . Can these estimations can be supported by other data? L mean is equal to hospital occupancy divided by cumulative hospitalizations and these are independent results. Our estimations for lengths of stay in the hospital for deaths (L dead ) and discharges (L dis ) matched the L mean (14.9± 0.4, Table 1 ). Moreover, our estimated LOS for deaths and discharges were 35 very close to what was previously reported by Verity et al., who found the mean duration from onset of symptoms to death (T od ) and recovery (T or ) for severe cases of COVID-19 in mainland China were 24.7 days and 17.8 days, respectively 21 . Similar studies estimated the mean time from onset to death (T od ) was 20.0 days 22 , and the average time from onset to hospitalization (T oh ) was 7.0 days 23, 24 . Based on these estimations and formula L dead =T od -T oh and L dis= T or -T oh , we calculated L dead and L dis and found that the Verity's estimation L dead = 17.8-7.0=10.8 days and L dis = 24.7-7.0=17.7 days, while the Wu's estimation was L d =20.0-7.0=13 days. Our estimated L dead was 14·0 days, which close to Wu's L dead estimation (13·0 5 days, based on Chinese data before Apr, 2020) and our estimated L dis (18·2 days) was similar to Verity's L dis estimation (17·7 days). We next wanted to know whether HOMR correlated with HMR in the US. To confirm this, we calculated L mean for the combined 34 states and found that the L mean is very steady (14.9 days, 95%CI:14.8-14.9) for a period of 7 months, spanning from Aug 7, 2020 to 10 Mar 1, 2021 (Fig. 1E) . Thus, HMR, which is a fatality risk index for hospitalizations, can be mimicked by HOMR in these states within a 7-month period (HMR = HOMR*L mean ). The coefficient of determination was 0.943 (Fig. 1F) . The COVID-19 mean HMR in this period (23.78%) was 2·98-fold that of the US CDC-estimated HMR for seasonal flu for the 2019-2020 period 2 . 15 We were interested in whether TDHOR could reflect the trend of HOMR. HOMRs in 34 US states highly correlated with TDHOR, with a 0.99 coefficient of determination (Fig. 1G ). The long-term facility (LTC) CFR and LTC-to-total death ratio declined from June 26, 2020 to Mar 1, 2021 (Fig. 1H ). This indicated that the prevention in LTC improved in this period. TDHOR and CFR correlated well between 34 states and all US states with 20 coefficients of determination of 0·96 and 1·00, respectively (Appendix p1). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 5 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 4, 2022. ; https://doi.org/10.1101/2022.02.03.22270417 doi: medRxiv preprint All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 4, 2022. ; https://doi.org/10.1101/2022.02.03.22270417 doi: medRxiv preprint In view of the fact that CFR has large temporal and spatial variations, we were interested in how much variation exists for TDHOR and HOCR. Since CFR = TDHOR* HOCR =TDHR*HCR (formula 10 in Methods 1), there are two contributing factor pairs causing major fluctuations in CFR (TDHOR* HOCR or TDHR*HCR). CFRs for most of 5 these 28 countries can be divided into two stages, a more volatile, or dramatic change stage, followed by a stagnant, or relative flat stage. COVID-19 CFRs in these countries fluctuated considerably over time as the pandemic progressed ( Fig. 2A) . TDHORs did not exhibit dramatic changes with outbreak stage (Fig 2B) . Here, we first introduced another new metric, named the "range1-to-mean2 ratio", to 10 measure the variation of CFR and TDHOR for two stages (dramatic change stage and flat stage, Appendix p2). The average range1-to-mean2 for CFRs of 27 countries was 3·57±1·78 (Methods 4, 5). The average range1-to-mean2 ratio for 27 countries TDHORs was 0·80±0·22, which was smaller than the average range1-to-mean2 of CFR, indicating TDHOR are spatially comparable between countries (Appendix Table 2 ). HOCR showed a similar 15 pattern to CFR ( Fig 2C) . The more consistent TDHOR index, compared to the highly volatile CFR and HOCR metrics, suggest that CFR variations are majorly derived from HOCR. Therefore, we sought to quantitatively analyze the contribution to the dramatic change of CFRs. We examined CFR, HOCR and TDHOR in 27 countries and US states ( Fig. 2D -F). The approximate 20 78·66% decrease in CFR was from HCR (hospitalization-to-case ratio) and 21·34% from TDHR (ratio of total death to hospitalization, Fig. 2G ) in USA 31 states. There are two possibilities for HCR to contribute majorly to the CFR dramatic decrease because HCR equals hospitalization/ (total symptomatic cases-undiagnosed symptomatic case). We hypothesized undiagnosed symptomatic cases maybe the major 25 reason for CFR dramatic decrease, creating an artificially high CFR since they all developed into a low and flat CFR stage after eight to nine months. If the HCR decrease was majorly caused by a decrease in the ratio of hospitalizations within the total symptomatic cases (also means the severe cases rate dramatic decrease), then the LTC CFR would dramatic decrease accordingly, which was not consistent with the LTC CFR (Fig. 1H ). There was no new 30 COVID-19 variant nor a dramatic shift in cases to a different age group reported in this period, which could cause the severe cases rate to change dramatically. The change in undiagnosed, symptomatic cases should be the major reason for the dramatic change stage for CFR, confirming our hypothesis. Similarly, HOCR contributed 89·37% and TDHOR contributed 10·63% to the CFR 35 changes in 27 countries (Fig. 2H , Appendix Table 3 ). It is reasonable to assume that the major contributing factor for the 27 countries with highest peak CFRs came from undiagnosed, symptomatic case numbers because the TDHORs did not have the same dramatic change during this time. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. H, Quantification of the CFR decrease due to HCR and TDHR variables for 31 states (including a combined average) between May 2020 and Dec 2020 and 27 countries (between May 2020 and Nov 2020). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The COVID-19 pandemic has progressed as a series of waves, making healthcare facilities overwhelmed and staffing shortages inevitable in certain regions and time periods over the last 20 months. Therefore, fatality risk monitoring and surveillance for short-term 5 and longer-term COVID-19 trends is critical. To explore the possibility of using TDHOR for COVID-19 fatality risk monitoring, we set a criterion for elevation as values of three continuous days above 30% of the previous three consecutive days from Apr 1, 2020 to Nov 15, 2021 . For the TDHOR in the 17 countries that met this elevation criterion, which match the time when Alpha variant was 10 reported (samples taken in September, 2020 in the United Kingdom) 25 , 11 showed no elevations (Fig. 3A) . 13 countries had CFR elevations, and 15 showed no CFR elevations (Fig. 3B) . Nine countries had HOCR elevations, while 19 countries showed no HOCR elevations (Fig. 3C , Appendix p3,). HOCR and TDHOR provided additional information because they mimic HCR and HMR (Appendix Table 4 ). 15 The TDHOR was able to detect elevations for 16 states in the US between Sep 1, 2020 and Mar 1, 2021, while CFR only detected elevations in five states. We could confirm this data for 14 states by HMR and TDHR among 16 states detected TDHOR elevations. The remaining two states (Missouri and Vermont) were unconfirmed since these states did not have cumulative hospitalization data to include in our analysis (Appendix p4). Interestingly, 20 TDHOR and CFR decreases were showed in some US states between Aug 1, 2021 and Nov 15, 2021(all less than 20%) while there was no obvious decrease in HOCR within this period ( Fig. 2D-F) . All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Genomic surveillance has shown that Omicron variant has spread to the many countries after Omicron was first discovered on Nov 24, 2021 based on samples collected on 10 Nov 11, 2021 in South Africa 15, 26 . It raises serious concerns due to a much higher transmission rate and potential immune escape compared to the Delta variant 27 . The omicron variant accounted for 58·6% of US cases as of Dec 25, 2021, as estimated by the US CDC 28 . One of the most important questions is whether the variant increases severe rate or hospitalization-to-case ratio (HCR). The average length of stay in 15 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 4, 2022. ; https://doi.org/10.1101/2022.02.03.22270417 doi: medRxiv preprint hospital of US (L mean ) was constant within 7 months (Fig 1E) . The L mean of the United Kingdom was also relative steady within 18 months (Appendix p5). These indicate hospitalization-to-case ratio (HCR) can be monitored using HOCR for UK, US and other countries since HOCR equals HCR times L mean . We measured the TDHORs and HOCRs of the most recent three months for 28 5 countries from Oct 16, 2021 to Jan 16, 2022 and compared them to CFRs (Fig.3D, Appendix p6) . As of Jan 16, 2022, 12 among 28 countries were found to have more than 50% decrease of HOCR compared with the week mean HOCRs of the first week November, 2021(as of Nov 7, 2021). In particular, the UK and the US had decreases in mean week HOCR by 34·08% and 65·16% respectively (Fig.3E -F, Appendix Table 5 ), which mimics the 10 hospitalization-to-cases ratio in these countris. It is of the utmost importance to accurately assess and understand the risk for novel diseases like COVID-19, which continues to be a prominent threat to global health. As a 15 novel alternative for COVID-19 risk assessment, we have introduced HOMR, TDHOR and HOCR as three new indexes for COVID-19 fatality risk assessment. TDHOR is valuable as a risk measurement in early stages of COVID-19 outbreaks and has the potential to monitor SARS-CoV-2 mutations that affect the death rate. Here, in addition to describing the concept and the relationships of HOMR, TDHOR, 20 HOCR, CFR, and HMR, we have applied them to estimate the length of hospital stay in 34 states. The period between Jun 26, 2020 and Aug 6, 2020 did not show a good linear correlation between 1/HOMR and N dis / N dead . This could reflect either that f (N hc )/N dead and N hc /N dead need to be included in the calculation of HOMR or that missing hospitalization information in this data set have a big effect on estimation of L dis and L dead during this time 25 period. Together, this is important information for decision makers to properly allocate healthcare resources for COVID-19, as well as for other future infectious diseases. HOMR showed a high correlation with TDHOR, and imitated HMR well in 34 US states within the first 7 months of the COVID-19 outbreak. This allows us to use TDHOR as another assessment of COVID-19 fatality risk. Based on our analysis, more than two-thirds 30 of the dramatic changes in CFR were caused by undiagnosed cases in USA, while less than one-third were attributed to case-independent data. Notably, TDHORs are less spatially and temporally variable than CFRs on a global level (among 27 countries analyzed), supporting our hypothesis and providing additional value in fatality risk dynamic monitoring. This is additionally supported by CFR data from USA long term care facilities, where COVID-19 35 cases are far less likely to be undetected, and therefore had no dramatic change stage. Numerous variants have been identified for the SARS-CoV-2 virus, including the highly transmissible Delta variant 14 and the newly-detected Omicron variant 15 . We observed TDHOR elevations in 17 of 28 countries analyzed, along with 16 of the 50 US states in 20 months monitoring. Although there is no direct evidence to link the increase in TDHOR 40 values to the origin of new Alpha variants, this is a possibility that warrants further investigation. The HOCRs of the US and other 11 countries decreased more than 50%, All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 4, 2022. ; https://doi.org/10.1101/2022.02.03.22270417 doi: medRxiv preprint suggesting that Omicron does decrease the hospitalization-to-case ratio as of Jan 16, 2022. This is consistent with other reports about omicron risk 29 . The criteria for COVID-19 hospitalization in different countries may affect HOMR and TDHOR. The overwhelming healthcare conditions brought about by COVID-19 also restricted available hospitalization capacity at surge times for COVID-19. HOMR, TDHOR 5 and HOCR provide additional information for disease fatality risk assessment and monitoring that are complementary to CFR. It would be interesting to compare HOMR, TDHOR and HOCR for other viruses like seasonal influenza, SARS-CoV-1, MERS-CoV, or Ebola, as well as future, unknown, and potentially fatal diseases. Overall, these new indexes of hospital occupancy related metrics provide the public health sector with additional, effective 10 indicators for monitoring COVID-19 fatality risk, possibly encouraging more countries to release hospital occupancy data in the future since these calculations require data that is relatively easy to collect. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. CAB collected data and did analysis, JS and JW contributed to statistic and epidemiological modelling choices. DJZ and PWZ supervised the whole process of data analysis, results interpretation and manuscript writing. The authors declare no competing interests. The authors declare that all data generated or analyzed during this study are 15 included in this published article and its supplementary information files. Keywords: COVID-19; SARS-CoV-2; hospital occupancy mortality rate (HOMR); ratio of total deaths to hospital occupancy (TDHOR); ratio of hospital occupancy to cases (HOCR); hospital mortality rate (HMR); case fatality rate (CFR); hospitalization-to-case ratio (HCR); evaluation; 20 fatality risk; mortality risk; severity; measurement; assessment; Range1-to-mean2 ratio (R1/M2); Range-to-mean ratio(R/M); mid-range1-to-mean2 ratio (MR1/M2) All data are updated as of Jan 20, 2021, unless specifically mentioned in the text. These data included cases, deaths, current hospital bed occupancy, and cumulative hospitalizations. Symptomatic and asymptomatic cases in mainland China. 30 Global data, which includes the US but excludes China, was collected from "Our World in Data" (https://ourworldindata.org/coronavirus), which was derived from Johns Hopkins Coronavirus Resource Center and European Centre for Disease Prevention and Control based on its descriptions. Cases, deaths, and hospitalizations for 28 countries analysis were confirmed for COVID- 19. 35 Chinese data was manually collected from the Chinese government by looking at authorized daily announcements for COVID-19 in Chinese areas (National Health Commission of China, http://en.nhc.gov.cn/news.html). Cases, deaths, and hospitalizations for 28 countries analysis were confirmed for COVID- 19 . US COVID-19 information was gathered from the "COVID-19 tracking project" 40 before Mar 2, 2021(directly from the websites of US state/territory public health authorities, https://covidtracking.com). After Mar 2, 2021, cases and death data were collected from the All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 4, 2022. ; https://doi.org/10.1101/2022.02.03.22270417 doi: medRxiv preprint US Centers for Disease Control and Prevention, and hospital data were taken from the US Department of Health and Human Services (HHS) (https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh). Hospital occupancy mortality rate (HOMR) = hospital deaths / hospitalization occupancy Ratio of total deaths to hospital occupancy (TDHOR) = total deaths / hospital occupancy 20 Where: All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 4, 2022. ; https://doi.org/10.1101/2022.02.03.22270417 doi: medRxiv preprint Note: 1) deaths, case and hospitalizations are all cumulative and cases are symptomatic cases if not specified. 2) Long-term care (LTC) facilities includes nursing homes, assisted living, and other long-term care facilities. N LTCd is the deaths in LTC facilities and N hd is the deaths at home with COVID-19. These formulas demonstrate that: 1) HMR is determined majorly by the N dis /N dead ratio (formula 4) and HOMR depends on L dead , L dis , and the N dis /N dead ratio and 2) The 5 difference between HOMR (hospital deaths / hospital occupancy) and TDHOR ((hospital deaths + LTC deaths)/ hospital occupancy) is the LTC deaths. HOMR = TDHOR if LTC deaths equals zero. CFR can be segregated by TDHOR * HOCR and TDHR * HCR based on mathematical derivation (formula 10). Under the condition of N hc is negligible or N dead >> N hc, 1/HOMR = Formulas 1, 10 and 11 were used for estimation of L dead and L dis . We assume deaths at home (N hd ) for COVID-19 was very low compared to total cases and let total deaths (N td ) equal the sum of N dead , N LTCd , and N td ≈ N dead + N LTCd for deaths calculation in our estimation of average length of stay. 15 Cumulative hospitalization data are needed to calculate HOMR. Only UK and 35 states in the US provide data on cumulative hospitalizations. New Jersey was excluded from our analysis because it had a dramatic change in hospitalizations compared to the other states (New Jersey first released cumulative hospitalization data on May 26, 2020 at 16,373, which jumped from 24517 to 37222 on Oct 22, 2020 First, HO was calculated. Next, N dead were obtained by subtracting LTC deaths (N LTCd ) from total deaths (N td ) and calculated HODR using HO and N dead (where N dead equals N td -N LTCd ). HMRs were then calculated using cumulative hospitalizations and hospital deaths to get N dis /N dead u s i n g N dis /N dead ≈ 1/HMR-1(formula 4) and N dis = cumulative hospitalizations -30 N dead . Linear regression was performed from Jun 26, 2020 to Mar 1, 2021 using 1/HOMR and N dis /N dead . The plotted trendline was used to determine the average length of stay in hospitals for deaths (L dead ) at the intersection of the Y axes. The length of stay in hospitals for discharges (L dis ) was determined by the trendline slope. Three different time periods were divided according to these plotting results and their respective r 2 values. 35 A new metric, "range1-to-mean2 ratio" (R1/M2), was used to measure the dispersion of CFR and TDHOR for two stages (phases). For this, the range was calculated from the first stage, and the mean was derived from the second stage. Here, we use R1/M2, rather than All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 4, 2022. ; https://doi.org/10.1101/2022.02.03.22270417 doi: medRxiv preprint MR1/M2 (mid-range1 to mean 2 ratio), or Q3R/M2 (Q3 range1 to mean2 ratio) because these calculations are largely similar in this situation. For this calculation, the range 1 is derived from the first, dramatic change stage (Mar 9, 2020 to October 31, 2020), while the mean2 is calculated from the month immediately following the dramatic change (November, 2020), the second, flat stage (Appendix p2). 5 The first 14 days of each dataset were omitted if the data was not zero for all analyses due to variations in disease onset, unless indicated in the text. The onset effects of TDHOR were caused by the time gap of when the first daily hospital occupancy data was released and 10 the first death. All CFR, HCR, HOMR, TDHOR, and HMR calculations were cumulative unless specified. HOMR is not available for this analysis due to the lack of LTC data in other countries, except in the US. Globally, a total of 35 countries have released daily hospital occupancy and deaths data. Eight countries were excluded for analysis. Of these, six European countries, including 15 Finland, Iceland, Lithuania, Norway, Spain, and Malta, had some data missing for either weekends or certain days. Australia does not have a flat stage followed by dramatic change stage in CFR and only had one death in November. It was excluded from analysis. In the case of China, a country that has had a unique COVID-19 progression and time window. It only had four COVID-19 deaths after Apr, 18, 2020. 20 In total, 27 countries were used for TDHOR, CFR, and HOCR comparison as a single group. These metrics for all US states and one district showed a similar pattern as the 28 countries previously analyzed, except with some TDHOR onset effects in some states. Based on CFR= HCR*TDHR (formula 10), we first quantitatively analyzed COVID-19 HCR and TDHR contributions in the US. We examined CFRs changes between two months (May, 30 2020 and December of 2020) in US 35 states for two reasons: 1) only 35 states had cumulative hospitalization data for HR and TDHR calculations and 2) May 2020 was the peak month for CFR and December 2020 was the month after the dramatic change stage for most of the US states. Four states (Indiana, Nebraska, New Jersey, and Washington) did not have complete 35 cumulative hospitalization data in May 2020. CFR, TDHR, and HCR were calculated for the remaining 31 states for two months (May 2020 and December 2020). Fold decrease for TDHR and HCR between May 2020 and December 2020 were used to calculate the contributions to the CFR changes. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 4, 2022. ; https://doi.org/10.1101/2022.02.03.22270417 doi: medRxiv preprint From Mar 9, 2020 to Nov 1, 2020, dramatic changes for CFR in 27 countries occurred within an eight-month period, followed by a stagnant stage after Nov 1, 2020. Australia was excluded from analysis because of the same reason mentioned in Methods 5. Based on CFR= TDHOR*HOCR (Formula 2), we quantitatively analyzed COVID-19 TDHOR and HOCR contributions in these 27 countries and examined CFR changes between 5 two months (May 2020 and November 2020). CFR, TDHR, and HCR were calculated for 31 states for May 2020 and November 2020. Fold decrease for TDHR and HCR between May 2020 and November 2020 were used to calculate the contribution to the CFR changes. To explore the possibility of using TDHOR and HOCR for COVID-19 fatality risk 10 monitoring, we set the criterion for elevation as values of three continuous days above 30% of the previous three consecutive days among 28 countries between Mar 24, 2020 and Nov.15, 2021. Representative A, CFR and B, TDHOR plots for the COVID-19 outbreak in Hungary. To calculate the R1/M2 ratio, the range was obtained from the initial, volatile outbreak period (stage 1), while the mean was calculated from the second, stagnant phase (stage 2). The start and end points for these phases are denoted by the black lines. 30 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Excel file containing mainland Chinese data for COVID-19(Jan 9, 2020-Jan 23, 2021). A website with mainland Chinese COVID-19 data (including Wuhan and Hubei) is ready to activate. 10 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 4, 2022. ; https://doi.org/10.1101/2022.02.03.22270417 doi: medRxiv preprint Early detection of SARS-CoV-2 P.1 variant in Southern Brazil and reinfection of the same patient by P.2 Public health actions to control new SARS-CoV-2 variants Safety and immunogenicity of SARS-CoV-2 variant mRNA vaccine boosters in healthy adults: an interim analysis Variation in COVID-19 mortality across 117 US hospitals in high and how-burden settings Hospital admission and mortality rates for non-covid diseases in Denmark during covid-19 15 pandemic: Nationwide population-based cohort study The COVID tracking project Analysis of the 20 time course of COVID-19 cases and deaths from countries with extensive testing allows accurate early estimates of the age specific symptomatic CFR values Estimates of the severity of coronavirus disease 2019: a model-based analysis Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China Clinical Characteristics of 138 Hospitalized 30 Patients with 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China Clinical characteristics and outcomes of 952 hospitalized COVID-19 patients in The Netherlands: A retrospective cohort study Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding WHO, Classification of Omicron (B.1.1.529): SARS-CoV-2 Variant of Concern Countries with no elevations for COVID- 19 (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.