# American Institute of Mathematical Sciences

September  2021, 3(3): 479-541. doi: 10.3934/fods.2021022

## A study of disproportionately affected populations by race/ethnicity during the SARS-CoV-2 pandemic using multi-population SEIR modeling and ensemble data assimilation

 1 Florida Atlantic University, 777 Glades Rd., Boca Raton, FL 33431, USA 2 University of North Carolina, 130 Mason Farm Road Chapel Hill, NC 27599, USA 3 University of Michigan, 530 Church St, Ann Arbor, MI 48109, USA 4 Arizona State University, 1151 S Forest Ave, Tempe, AZ 85281, USA 5 Smith College, Northampton, MA 01063, USA 6 NORCE Norwegian Research Centre AS, Nygårdsporten 112, 5008 Bergen, Norway

Received  January 2021 Revised  August 2021 Published  September 2021 Early access  September 2021

Fund Project: The second author (CSS) is supported by the US Office of Naval Research under grant N00014-18-1-2204. The third author (DPM) is supported by the NSF GRFP DGE 1256260. The seventh author (GE) was supported by internal funding from NORCE

The disparity in the impact of COVID-19 on minority populations in the United States has been well established in the available data on deaths, case counts, and adverse outcomes. However, critical metrics used by public health officials and epidemiologists, such as a time dependent viral reproductive number ($R_t$), can be hard to calculate from this data especially for individual populations. Furthermore, disparities in the availability of testing, record keeping infrastructure, or government funding in disadvantaged populations can produce incomplete data sets. In this work, we apply ensemble data assimilation techniques which optimally combine model and data to produce a more complete data set providing better estimates of the critical metrics used by public health officials and epidemiologists. We employ a multi-population SEIR (Susceptible, Exposed, Infected and Recovered) model with a time dependent reproductive number and age stratified contact rate matrix for each population. We assimilate the daily death data for populations separated by ethnic/racial groupings using a technique called Ensemble Smoothing with Multiple Data Assimilation (ESMDA) to estimate model parameters and produce an $R_t(n)$ for the $n^{th}$ population. We do this with three distinct approaches, (1) using the same contact matrices and prior $R_t(n)$ for each population, (2) assigning contact matrices with increased contact rates for working age and older adults to populations experiencing disparity and (3) as in (2) but with a time-continuous update to $R_t(n)$. We make a study of 9 U.S. states and the District of Columbia providing a complete time series of the pandemic in each and, in some cases, identifying disparities not otherwise evident in the aggregate statistics.

Citation: Emmanuel Fleurantin, Christian Sampson, Daniel Paul Maes, Justin Bennett, Tayler Fernandes-Nunez, Sophia Marx, Geir Evensen. A study of disproportionately affected populations by race/ethnicity during the SARS-CoV-2 pandemic using multi-population SEIR modeling and ensemble data assimilation. Foundations of Data Science, 2021, 3 (3) : 479-541. doi: 10.3934/fods.2021022
##### References:
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Gerardin, Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation, Infectious Disease Modelling. [12] M. Asch, M. Bocquet and M. Nodet, Data Assimilation: Methods, Algorithms, and Applications, SIAM, Society for Industrial and Applied Mathematics, 2016. doi: 10.1137/1.9781611974546.pt1. [13] L. M. A. Bettencourt, R. M. Ribeiro, G. Chowell, T. Lant and C. Castillo-Chavez, Towards real time epidemiology: Data assimilation, modeling and anomaly detection of health surveillance data streams, Lecture Notes in Computer Science Intelligence and Security Informatics: Biosurveillance, 79–90. doi: 10.1007/978-3-540-72608-1_8. [14] M. Bocquet and P. Sakov, An iterative ensemble Kalman smoother, Quarterly Journal of the Royal Meteorological Society, 140 (2013), 1521-1535.  doi: 10.1002/qj.2236. [15] A. Carrassi, M. Bocquet, L. Bertino and G. Evensen, Data assimilation in the geosciences: An overview on methods issues and perspectives, WCC, 9 2018. doi: 10.1002/wcc.535. [16] A. A. Emerick and A. C. Reynolds, Ensemble smoother with multiple data assimilation, Computers & Geosciences, 55 (2013), 3-15.  doi: 10.1016/j.cageo.2012.03.011. [17] G. Evensen, Analysis of iterative ensemble smoothers for solving inverse problems, Computational Geosciences, 22 (2018), 885-908.  doi: 10.1007/s10596-018-9731-y. [18] G. Evensen, J. Amezcua, M. Bocquet, A. Carrassi, A. Farchi, A. Fowler, P. L. Houtekamer, C. K. Jones, R. J. de Moraes, M. Pulido, C. Sampson and F. C. Vossepoel, An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation, Foundations of Data Science, (2020). doi: 10.3934/fods.2021001. [19] J. R. Eyre, S. J. English and M. Forsythe, Assimilation of satellite data in numerical weather prediction. part i: The early years, Quarterly Journal of the Royal Meteorological Society, 146 (2019), 49-68.  doi: 10.1002/qj.3654. [20] A. L. Garcia-Basteiro, G. Moncunill, M. Tortajada, M. Vidal, C. Guinovart, A. Jiménez, R. Santano, S. Sanz, S. Méndez, A. Llupià, R. Aguilar, S. Alonso, D. Barrios, C. Carolis, P. Cisteró, E. Chóliz, A. Cruz, S. Fochs, C. Jairoce, J. Hecht, M. Lamoglia, M. J. Martínez, R. A. Mitchell, N. Ortega, N. Pey, L. Puyol, M. Ribes, N. Rosell, P. Sotomayor, S. Torres, S. Williams, S. Barroso, A. Vilella, J. Muñoz, A. Trilla, P. Varela, A. Mayor and C. Dobaño, Seroprevalence of antibodies against SARS-CoV-2 among health care workers in a large spanish reference hospital, Nature Communications, 11 (2020), Article number: 3500. doi: 10.1038/s41467-020-17318-x. [21] C. G. Grijalva, M. A. Rolfes, Y. Zhu, H. Q. McLean, K. E. Hanson, E. A. Belongia, N. B. Halasa, A. Kim, C. Reed, A. M. Fry and H. K. Talbot, Transmission of SARS-COV-2 infections in households - Tennessee and Wisconsin, April-September 2020, MMWR. Morbidity and Mortality Weekly Report, 69 (2020), 1631–1634. doi: 10.15585/mmwr.mm6944e1. [22] P. L. Houtekamer and F. Zhang, Review of the ensemble kalman filter for atmospheric data assimilation, Monthly Weather Review, 144 (2016), 4489-4532.  doi: 10.1175/MWR-D-15-0440.1. [23] J. P. A. Ioannidis, Infection fatality rate of COVID-19 inferred from seroprevalence data, Bulletin of the World Health Organization, 99 (2020), 19–33F. doi: 10.2471/blt.20.265892. [24] J. Jeppesen, Fact sheet: Reanalysis, URL https://www.ecmwf.int/en/about/media-centre/focus/2020/fact-sheet-reanalysis, 2020, Last accessed 2021-07-31. [25] E. J. Kostelich, Y. Kuang, J. M. Mcdaniel, N. Z. Moore, N. L. Martirosyan and M. C. Preul, Accurate state estimation from uncertain data and models: An application of data assimilation to mathematical models of human brain tumors, Biology Direct, 6 (2011), 64.  doi: 10.1186/1745-6150-6-64. [26] W. Lieberman-Cribbin, S. Tuminello, R. M. Flores and E. Taioli, Disparities in COVID-19 testing and positivity in new york city, American Journal of Preventive Medicine, 59 (2020), 326-332.  doi: 10.1016/j.amepre.2020.06.005. [27] N. Narea, Immigrants have helped keep essential services running. But those without legal status have no financial safety net, URL https://www.vox.com/2020/5/5/21244630/undocumented-immigrants-coronavirus-relief-cares-act, 2020, Last accessed 2021-07-31. [28] I. Pathak, Y. Choi, D. Jiao, D. Yeung and L. Liu, Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in state covid-19 data, MedRxiv, (2020). doi: 10.1101/2020.10.01.20205377. [29] J. Skjervheim, G. Evensen, J. Hove and J. G. Vabø, An ensemble smoother for assisted history matching, SPE, (2011), 141929. doi: 10.2118/141929-MS. [30] A. S. Stordal and A. H. Elsheikh, Iterative ensemble smoothers in the annealed importance sampling framework, Advances in Water Resources, 86 (2015), 231-239.  doi: 10.1016/j.advwatres.2015.09.030. [31] G. Vernieres, A. Anis, R. N. Miller and L. L. Ehret, Generalized inversion of thermistor-chain data and a layer model of lake kinneret, Ocean Modelling, 12 (2006), 112-139.  doi: 10.1016/j.ocemod.2005.04.004. [32] Z. Wu, T. Phan, J. Baez, Y. Kuang and E. J. Kostelich, Predictability and identifiability assessment of models for prostate cancer under androgen suppression therapy, Mathematical Biosciences and Engineering, 16 (2019), 3512-3536.  doi: 10.3934/mbe.2019176.

show all references

##### References:
 [1] Cyberstates 2020: The definitive guide to the U.S. tech industry and tech wrokforce, URL https://www.cyberstates.org, Last accessed 2021-04-13. [2] Disparities in Wealth by Race and Ethnicity in the 2019 Survey of Consumer Finances, URL https://www.federalreserve.gov/econres/notes/feds-notes/disparities-in-wealth-by-race-and-ethnicity-in-the-2019-survey-of-consumer-finances-20200928.htm, Last accessed 2021-04-13. [3] Diversity in high tech, URL https://www.eeoc.gov/special-report/diversity-high-tech, Last accessed 2021-04-13. [4] Economy at a Glance: California, URL https://data.bls.gov/timeseries/LASST060000000000006?, Last accessed 2021-04-13. [5] IHME COVID-19 estimates, URL http://www.healthdata.org/covid/data-downloads, Last accessed 2021-04-13. [6] Options to Reduce Quarantine for Contacts of Persons with SARS-CoV-2 Infection Using Symptom Monitoring and Diagnostic Testing, URL https://www.cdc.gov/coronavirus/2019-ncov/more/scientific-brief-options-to-reduce-quarantine.html, Last accessed 2021-04-13. [7] Racial Data Dashboard, 2021, URL https://covidtracking.com/race/dashboard, Last accessed 2021-04-13. [8] Risk for covid-19 infection, hospitalization, and death by race/ethnicity, URL https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-race-ethnicity.html, Last accessed 2021-08-03. [9] Rt COVID-19, URL https://rt.live/, Last accessed 2021-04-13. [10] Statistics and Church Facts | Total Church Membership, URL http://newsroom.churchofjesuschrist.org/facts-and-statistics/state/utah, Last accessed 2021-07-31. [11] E. Armstrong, M. Runge and J. Gerardin, Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation, Infectious Disease Modelling. [12] M. Asch, M. Bocquet and M. Nodet, Data Assimilation: Methods, Algorithms, and Applications, SIAM, Society for Industrial and Applied Mathematics, 2016. doi: 10.1137/1.9781611974546.pt1. [13] L. M. A. Bettencourt, R. M. Ribeiro, G. Chowell, T. Lant and C. Castillo-Chavez, Towards real time epidemiology: Data assimilation, modeling and anomaly detection of health surveillance data streams, Lecture Notes in Computer Science Intelligence and Security Informatics: Biosurveillance, 79–90. doi: 10.1007/978-3-540-72608-1_8. [14] M. Bocquet and P. Sakov, An iterative ensemble Kalman smoother, Quarterly Journal of the Royal Meteorological Society, 140 (2013), 1521-1535.  doi: 10.1002/qj.2236. [15] A. Carrassi, M. Bocquet, L. Bertino and G. Evensen, Data assimilation in the geosciences: An overview on methods issues and perspectives, WCC, 9 2018. doi: 10.1002/wcc.535. [16] A. A. Emerick and A. C. Reynolds, Ensemble smoother with multiple data assimilation, Computers & Geosciences, 55 (2013), 3-15.  doi: 10.1016/j.cageo.2012.03.011. [17] G. Evensen, Analysis of iterative ensemble smoothers for solving inverse problems, Computational Geosciences, 22 (2018), 885-908.  doi: 10.1007/s10596-018-9731-y. [18] G. Evensen, J. Amezcua, M. Bocquet, A. Carrassi, A. Farchi, A. Fowler, P. L. Houtekamer, C. K. Jones, R. J. de Moraes, M. Pulido, C. Sampson and F. C. Vossepoel, An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation, Foundations of Data Science, (2020). doi: 10.3934/fods.2021001. [19] J. R. Eyre, S. J. English and M. Forsythe, Assimilation of satellite data in numerical weather prediction. part i: The early years, Quarterly Journal of the Royal Meteorological Society, 146 (2019), 49-68.  doi: 10.1002/qj.3654. [20] A. L. Garcia-Basteiro, G. Moncunill, M. Tortajada, M. Vidal, C. Guinovart, A. Jiménez, R. Santano, S. Sanz, S. Méndez, A. Llupià, R. Aguilar, S. Alonso, D. Barrios, C. Carolis, P. Cisteró, E. Chóliz, A. Cruz, S. Fochs, C. Jairoce, J. Hecht, M. Lamoglia, M. J. Martínez, R. A. Mitchell, N. Ortega, N. Pey, L. Puyol, M. Ribes, N. Rosell, P. Sotomayor, S. Torres, S. Williams, S. Barroso, A. Vilella, J. Muñoz, A. Trilla, P. Varela, A. Mayor and C. Dobaño, Seroprevalence of antibodies against SARS-CoV-2 among health care workers in a large spanish reference hospital, Nature Communications, 11 (2020), Article number: 3500. doi: 10.1038/s41467-020-17318-x. [21] C. G. Grijalva, M. A. Rolfes, Y. Zhu, H. Q. McLean, K. E. Hanson, E. A. Belongia, N. B. Halasa, A. Kim, C. Reed, A. M. Fry and H. K. Talbot, Transmission of SARS-COV-2 infections in households - Tennessee and Wisconsin, April-September 2020, MMWR. Morbidity and Mortality Weekly Report, 69 (2020), 1631–1634. doi: 10.15585/mmwr.mm6944e1. [22] P. L. Houtekamer and F. Zhang, Review of the ensemble kalman filter for atmospheric data assimilation, Monthly Weather Review, 144 (2016), 4489-4532.  doi: 10.1175/MWR-D-15-0440.1. [23] J. P. A. Ioannidis, Infection fatality rate of COVID-19 inferred from seroprevalence data, Bulletin of the World Health Organization, 99 (2020), 19–33F. doi: 10.2471/blt.20.265892. [24] J. Jeppesen, Fact sheet: Reanalysis, URL https://www.ecmwf.int/en/about/media-centre/focus/2020/fact-sheet-reanalysis, 2020, Last accessed 2021-07-31. [25] E. J. Kostelich, Y. Kuang, J. M. Mcdaniel, N. Z. Moore, N. L. Martirosyan and M. C. Preul, Accurate state estimation from uncertain data and models: An application of data assimilation to mathematical models of human brain tumors, Biology Direct, 6 (2011), 64.  doi: 10.1186/1745-6150-6-64. [26] W. Lieberman-Cribbin, S. Tuminello, R. M. Flores and E. Taioli, Disparities in COVID-19 testing and positivity in new york city, American Journal of Preventive Medicine, 59 (2020), 326-332.  doi: 10.1016/j.amepre.2020.06.005. [27] N. Narea, Immigrants have helped keep essential services running. But those without legal status have no financial safety net, URL https://www.vox.com/2020/5/5/21244630/undocumented-immigrants-coronavirus-relief-cares-act, 2020, Last accessed 2021-07-31. [28] I. Pathak, Y. Choi, D. Jiao, D. Yeung and L. Liu, Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in state covid-19 data, MedRxiv, (2020). doi: 10.1101/2020.10.01.20205377. [29] J. Skjervheim, G. Evensen, J. Hove and J. G. Vabø, An ensemble smoother for assisted history matching, SPE, (2011), 141929. doi: 10.2118/141929-MS. [30] A. S. Stordal and A. H. Elsheikh, Iterative ensemble smoothers in the annealed importance sampling framework, Advances in Water Resources, 86 (2015), 231-239.  doi: 10.1016/j.advwatres.2015.09.030. [31] G. Vernieres, A. Anis, R. N. Miller and L. L. Ehret, Generalized inversion of thermistor-chain data and a layer model of lake kinneret, Ocean Modelling, 12 (2006), 112-139.  doi: 10.1016/j.ocemod.2005.04.004. [32] Z. Wu, T. Phan, J. Baez, Y. Kuang and E. J. Kostelich, Predictability and identifiability assessment of models for prostate cancer under androgen suppression therapy, Mathematical Biosciences and Engineering, 16 (2019), 3512-3536.  doi: 10.3934/mbe.2019176.
The general form of the $R_{ij}^A(n)$ contact matrix elements for contact rates between age groups $i$ and $j$ in a given sub-population. The contact matrix is then subdivided into different blocks where parameters $\alpha, \beta, \eta, \gamma, \varepsilon, \zeta, \delta_1, \delta_2, \delta_3, \xi_1, \xi_2, \xi_3$ and $\xi_4$ control the contact rates between different age groups which generate similar patterns for spreading the disease. In particular, we define $\gamma, \delta_2, \xi_2$ and $\xi_3$ to be the parameters for the contact rates of the working class age groups
$\mathbf{R}^{\rm{A}}$ for the NDAP $(i)$ and DAP $(ii)$ in our runs without DA for each intervention period along with the $\mathbf{R}^{\rm{C}}$ matrix $(iii)$ used in these simulations. We also used the $\mathbf{R}^{\rm{A}}$'s in $(i)$ and $(ii)$ for the DA runs
Results for non-DA runs with DAP and NDAP at about the same population
Results for non-DA runs with NDAP at $70\%$ of the total population
Examples of analysis runs for various values of $R^C_{nm}$ from the District of Columbia (DC). Top: left $R^C_{nm} = 0$, right $R^C_{nm} = 10^{-3}$. Bottom: left $R^C_{nm} = 10^{-1}$, right $R^C_{nm} = 6\times10^{-1}$
Analysis results for the District of Columbia (DC) with $R^A_{ij}=1$ and piece-wise updates to $R_t(n)$
Analysis results for the District of Columbia (DC) with age stratified $R^A$ and piece-wise updates to $R_t(n)$
Analysis results for the continuous update case for the state of AK
Analysis results for the continuous update case for the state of CA
Analysis results for the continuous update case for the state of CT
Analysis results for the continuous update case for the state of DE
Analysis results for the continuous update case for the District of Columbia
Analysis results for the continuous update case for the state of HI
Analysis results for the continuous update case for the state of MD
Analysis results for the continuous update case for the state of MI
Analysis results for the continuous update case for the state of UT
Analysis results for the continuous update case for the state of WA
Analysis results when using piece-wise updates to $R(t)$ for AK. Top row: $R^A$ with entries of all ones. Bottom Row: $R^A$ for DAPs and NDAPs
Analysis results when using piecewise updates to $R(t)$ for CA. Top row: $R^A$ with entries of all ones. Bottom Row: $R^A$ for DAPs and NDAPs
Analysis results when using piecewise updates to $R(t)$ for CT. Top row: $R^A$ with entries of all ones. Bottom Row: $R^A$ for DAPs and NDAPs
Analysis results when using piecewise updates to $R(t)$ for DE. Top row: $R^A$ with entries of all ones. Bottom Row: $R^A$ for DAPs and NDAPs
Analysis results when using piecewise updates to $R(t)$ for DC. Top row: $R^A$ with entries of all ones. Bottom Row: $R^A$ for DAPs and NDAPs
Analysis results when using piecewise updates to $R(t)$ for HI. Top row: $R^A$ with entries of all ones. Bottom Row: $R^A$ for DAPs and NDAPs
Analysis results when using piecewise updates to $R(t)$ for MD. Top row: $R^A$ with entries of all ones. Bottom Row: $R^A$ for DAPs and NDAPs
Analysis results when using piecewise updates to $R(t)$ for MI. Top row: $R^A$ with entries of all ones. Bottom Row: $R^A$ for DAPs and NDAPs
Analysis results when using piecewise updates to $R(t)$ for UT. Top row: $R^A$ with entries of all ones. Bottom Row: $R^A$ for DAPs and NDAPs
Analysis results when using piecewise updates to $R(t)$ for WA. Top row: $R^A$ with entries of all ones. Bottom Row: $R^A$ for DAPs and NDAPs
Analysis results with a CFR prior of $0.009$ ($\sigma_{CFR} = 0.05$) and the same $R_t(n)$ ($\sigma_{R(t)} = 1.5$) prior for all populations. This figure corresponds to columns 2 and 3 in Table 5
Analysis results with a CFR prior of $0.020$ ($\sigma_{CFR} = 0.05$) and the same $R_t(n)$ ($\sigma_{R(t)} = 1.5$) prior for all populations. This figure corresponds to columns 4 and 5 in Table 5
Analysis results with a CFR prior of $0.001$ ($\sigma_{CFR} = 0.03$) and the same $R_t(n)$ ($\sigma_{R(t)} = 1.5$) prior for all populations. This figure corresponds to columns 6 and 7 in Table 5
Analysis results with a CFR prior of $0.020$ ($\sigma_{CFR} = 0.05$) and prior $R_t(n)$ ($\sigma_{R(t)} = 0.5$) curves coming from initial piece-wise assimilation also assuming a $0.020$ CFR prior. This figure corresponds to columns 2 and 3 in Table 6
Analysis results with a CFR prior of $0.020$ ($\sigma_{CFR} = 0.002$) and prior $R_t(n)$ ($\sigma_{R(t)} = 0.5$) curves coming from initial piece-wise assimilation also assuming a $0.020$ CFR prior. This figure corresponds to columns 4 and 5 in Table 6
Analysis results with a CFR prior of $0.020$ ($\sigma_{CFR} = 0.05$) for the Black population only and a CFR Prior of $0.009$ ($\sigma_{CFR} = 0.05$) for all other populations. The prior $R_t(n)$ ($\sigma_{R(t)} = 1.5$) curves are the same for all populations. This figure corresponds to columns 2 and 3 in Table 7
Analysis results with a CFR prior of $0.020$ ($\sigma_{CFR} = 0.002$) for the Black population only and a CFR Prior of $0.009$ ($\sigma_{CFR} = 0.0009$) for all other populations. The prior $R_t(n)$ ($\sigma_{R(t)} = 0.5$) curves coming from initial piece-wise assimilation also assuming a $0.020$ CFR prior. This figure corresponds to columns 4 and 5 in Table 7 and is the only case where Black infections can be lower than that of the LatinX population and still have more deaths
Analysis results with a CFR prior of $0.020$ ($\sigma_{CFR} = 0.05$) for the Black population only and a CFR Prior of $0.009$ ($\sigma_{CFR} = 0.05$) for all other populations. The prior $R_t(n)$ ($\sigma_{R(t)} = 0.5$) curves coming from initial piece-wise assimilation also assuming a $0.020$ CFR prior. This figure corresponds to columns 6 and 7 in Table 7
Analysis results for the continuous update case for the state of AK
Analysis results for the continuous update case for the state of CA
Analysis results for the continuous update case for the state of CT
Analysis results for the continuous update case for the state of DC
Analysis results for the continuous update case for the state of DE
Analysis results for the continuous update case for the state of HI
Analysis results for the continuous update case for the state of MD
Analysis results for the continuous update case for the state of MI
Analysis results for the continuous update case for the state of UT
Analysis results for the continuous update case for the state of WA
Matrix scaling parameters for DAP and NDAP workers in lockdown and post-lockdown time periods
 Scalings Matrix Parameters $\alpha$ $\beta$ $\gamma$ $\eta$ $\epsilon$ $\zeta$ $\delta_1$ $\delta_2$ $\delta_3$ $\xi_1$ $\xi_2$ $\xi_3$ $\xi_4$ DAP lock 0.5 0.7 0.55 0.25 0.25 0.35 0.3 0.7 0.7 0.4 0.65 0.55 0.6 DAP post-lock 0.7 0.8 0.7 0.3 0.3 0.4 0.6 0.85 0.7 0.85 0.7 0.65 0.65 NDAP lock 0.5 0.7 0.5 0.2 0.2 0.3 0.3 0.6 0.7 0.4 0.6 0.5 0.6 NDAP post-lock 0.7 0.8 0.7 0.25 0.25 0.35 0.6 0.7 0.75 0.7 0.7 0.65 0.65
 Scalings Matrix Parameters $\alpha$ $\beta$ $\gamma$ $\eta$ $\epsilon$ $\zeta$ $\delta_1$ $\delta_2$ $\delta_3$ $\xi_1$ $\xi_2$ $\xi_3$ $\xi_4$ DAP lock 0.5 0.7 0.55 0.25 0.25 0.35 0.3 0.7 0.7 0.4 0.65 0.55 0.6 DAP post-lock 0.7 0.8 0.7 0.3 0.3 0.4 0.6 0.85 0.7 0.85 0.7 0.65 0.65 NDAP lock 0.5 0.7 0.5 0.2 0.2 0.3 0.3 0.6 0.7 0.4 0.6 0.5 0.6 NDAP post-lock 0.7 0.8 0.7 0.25 0.25 0.35 0.6 0.7 0.75 0.7 0.7 0.65 0.65
Date breakdown by intervention periods for all states and the District of Columbia
 Interventions Information on Intervention Periods by State and the District of Columbia AK CA CT DC DE HI MD MI UT WA Start date 3/8/20 2/25/20 2/27/20 2/27/20 3/1/20 2/28/20 2/25/20 2/21/20 2/29/20 1/9/20 1st Phase 3/19/20 3/19/20 3/19/20 3/17/20 3/17/20 3/17/20 3/17/20 3/17/20 3/17/20 3/17/20 2nd Phase 5/27/20 5/27/20 5/27/20 5/29/20 5/31/20 5/15/20 5/15/20 5/19/20 5/5/20 5/28/20
 Interventions Information on Intervention Periods by State and the District of Columbia AK CA CT DC DE HI MD MI UT WA Start date 3/8/20 2/25/20 2/27/20 2/27/20 3/1/20 2/28/20 2/25/20 2/21/20 2/29/20 1/9/20 1st Phase 3/19/20 3/19/20 3/19/20 3/17/20 3/17/20 3/17/20 3/17/20 3/17/20 3/17/20 3/17/20 2nd Phase 5/27/20 5/27/20 5/27/20 5/29/20 5/31/20 5/15/20 5/15/20 5/19/20 5/5/20 5/28/20
Demographic breakdown by race/ethnicity (where data is available) for all states and the District of Columbia. Groups that meet the criteria to be a DAP are in bold. AIAN = American Indian and Alaska Native, NHPI = Native Hawaiian and Pacific Islander
 Race/Ethnicity Percentage of Population per State and the District of Columbia AK CA CT DC DE HI MD MI UT WA AIAN 0.15 0.076 X X X X X 0.005 0.023 0.01 Asian 0.06 0.14 0.04 0.0435 0.045 0.38 0.06 0.035 0.038 0.08 Black 0.03 0.06 0.1 0.4453 0.22 0.02 0.29 0.14 0.021 0.04 LatinX X 0.39 0.16 0.113 0.09 X 0.1 X 0.142 0.13 Multi X X 0.02 X 0.02 X X X X 0.05 NHPI 0.01 0.039 X X X 0.1 X X 0.016 0.008 Other 0.08 X 0.01 0.01 X 0.24 X 0.03 0.01 0.005 White 0.65 0.37 0.67 0.4196 0.62 0.25 0.51 0.78 0.78 0.69
 Race/Ethnicity Percentage of Population per State and the District of Columbia AK CA CT DC DE HI MD MI UT WA AIAN 0.15 0.076 X X X X X 0.005 0.023 0.01 Asian 0.06 0.14 0.04 0.0435 0.045 0.38 0.06 0.035 0.038 0.08 Black 0.03 0.06 0.1 0.4453 0.22 0.02 0.29 0.14 0.021 0.04 LatinX X 0.39 0.16 0.113 0.09 X 0.1 X 0.142 0.13 Multi X X 0.02 X 0.02 X X X X 0.05 NHPI 0.01 0.039 X X X 0.1 X X 0.016 0.008 Other 0.08 X 0.01 0.01 X 0.24 X 0.03 0.01 0.005 White 0.65 0.37 0.67 0.4196 0.62 0.25 0.51 0.78 0.78 0.69
Results for the CFR using the same $R_t(n)$ prior for all populations with $\sigma_{R(t)} = 1.5$
 Population Prior, $\sigma=0.05$ Post, $\chi^2=112$ Prior, $\sigma=0.05$ Post, $\chi^2=92$ Prior, $\sigma=0.03$ Post, $\chi^2=87$ Asian 0.009 0.0074 0.020 0.0093 0.001 0.0070 Black 0.009 0.0126 0.020 0.0157 0.001 0.0084 LatinX 0.009 0.0213 0.020 0.0246 0.001 0.0147 Multi 0.009 0.0103 0.020 0.0104 0.001 0.0086 Other 0.009 0.0065 0.020 0.0042 0.001 0.0065 White 0.009 0.0229 0.020 0.0271 0.001 0.0134
 Population Prior, $\sigma=0.05$ Post, $\chi^2=112$ Prior, $\sigma=0.05$ Post, $\chi^2=92$ Prior, $\sigma=0.03$ Post, $\chi^2=87$ Asian 0.009 0.0074 0.020 0.0093 0.001 0.0070 Black 0.009 0.0126 0.020 0.0157 0.001 0.0084 LatinX 0.009 0.0213 0.020 0.0246 0.001 0.0147 Multi 0.009 0.0103 0.020 0.0104 0.001 0.0086 Other 0.009 0.0065 0.020 0.0042 0.001 0.0065 White 0.009 0.0229 0.020 0.0271 0.001 0.0134
Results for the CFR using $R_t(n)$ priors taken from piece-wise assimilation first assuming a CFR of 2% for all populations
 Population Prior, $\sigma=0.05$ Post, $\chi^2=89$ Prior, $\sigma=0.002$ Post, $\chi^2=41$ Asian 0.020 0.0282 0.020 0.0199 Black 0.020 0.0337 0.020 0.0199 LatinX 0.020 0.0317 0.020 0.0200 Multi 0.020 0.0316 0.020 0.0202 Other 0.020 0.0299 0.020 0.0204 White 0.020 0.0259 0.020 0.0195
 Population Prior, $\sigma=0.05$ Post, $\chi^2=89$ Prior, $\sigma=0.002$ Post, $\chi^2=41$ Asian 0.020 0.0282 0.020 0.0199 Black 0.020 0.0337 0.020 0.0199 LatinX 0.020 0.0317 0.020 0.0200 Multi 0.020 0.0316 0.020 0.0202 Other 0.020 0.0299 0.020 0.0204 White 0.020 0.0259 0.020 0.0195
Results for the CFR assuming a CFR of 2% for only the Black Group
 Population Prior, $\sigma=0.05$ Post, $\chi^2=302$ Prior, $\sigma=.1x$ Post, $\chi^2=38$ Prior, $\sigma=0.05$ $\chi^2=83$ Asian 0.0090 0.0101 0.0090 0.0091 0.0090 0.0249 Black 0.0200 0.0226 0.0200 0.0201 0.0200 0.0371 LatinX 0.0090 0.0211 0.0090 0.0092 0.0090 0.0299 Multi 0.0090 0.0097 0.0090 0.0092 0.0090 0.0285 Other 0.0090 0.0053 0.0090 0.0093 0.0090 0.0234 White 0.0090 0.0248 0.0090 0.0090 0.0090 0.0256
 Population Prior, $\sigma=0.05$ Post, $\chi^2=302$ Prior, $\sigma=.1x$ Post, $\chi^2=38$ Prior, $\sigma=0.05$ $\chi^2=83$ Asian 0.0090 0.0101 0.0090 0.0091 0.0090 0.0249 Black 0.0200 0.0226 0.0200 0.0201 0.0200 0.0371 LatinX 0.0090 0.0211 0.0090 0.0092 0.0090 0.0299 Multi 0.0090 0.0097 0.0090 0.0092 0.0090 0.0285 Other 0.0090 0.0053 0.0090 0.0093 0.0090 0.0234 White 0.0090 0.0248 0.0090 0.0090 0.0090 0.0256
CFR estimates coming from the run presented in Section 4.2.6
 Population Prior, $\sigma=0.009$ Post, $\chi^2=31$ Asian 0.0900 0.0089 Black 0.0900 0.0100 LatinX 0.0900 0.0090 Multi 0.0900 0.0093 Other 0.0900 0.0094 White 0.0900 0.0094
 Population Prior, $\sigma=0.009$ Post, $\chi^2=31$ Asian 0.0900 0.0089 Black 0.0900 0.0100 LatinX 0.0900 0.0090 Multi 0.0900 0.0093 Other 0.0900 0.0094 White 0.0900 0.0094
The table gives a set of first-guess model parameters. As we could not find scientific estimates of these parameters, we set their values based on available information from the internet and initial model-tuning experiments. We leave it to the data assimilation system to fine-tune the parameter values
 Parameter First guess Description $\tau_ \rm{inc}$ 5.5 Incubation period $\tau_ \rm{inf}$ 3.8 Infection time $\tau_ \rm{recm}$ 14.0 Recovery time mild cases $\tau_ \rm{recs}$ 5.0 Recovery time severe cases $\tau_ \rm{hosp}$ 6.0 Time until hospitalization $\tau_ \rm{death}$ 16.0 Time until death $p_ \mathrm{f}$ 0.009 Case fatality rate $p_ \mathrm{s}$ 0.039 Hospitalization rate (severe cases) $p_ \mathrm{h}$ 0.4 Fraction of fatally ill going to hospital
 Parameter First guess Description $\tau_ \rm{inc}$ 5.5 Incubation period $\tau_ \rm{inf}$ 3.8 Infection time $\tau_ \rm{recm}$ 14.0 Recovery time mild cases $\tau_ \rm{recs}$ 5.0 Recovery time severe cases $\tau_ \rm{hosp}$ 6.0 Time until hospitalization $\tau_ \rm{death}$ 16.0 Time until death $p_ \mathrm{f}$ 0.009 Case fatality rate $p_ \mathrm{s}$ 0.039 Hospitalization rate (severe cases) $p_ \mathrm{h}$ 0.4 Fraction of fatally ill going to hospital
The $p$-numbers indicate the fraction of sick people in an age group ending up with mild symptoms, severe symptoms (hospitalized), and fatal infection
 Age group 1 2 3 4 5 6 7 8 9 10 11 Age range 0–5 6–12 13–19 20–29 30–39 40–49 50–59 60–69 70–79 80–89 90–105 Population 351159 451246 446344 711752 730547 723663 703830 582495 435834 185480 45230 p–mild 1.0000 1.0000 0.9998 0.9913 0.9759 0.9686 0.9369 0.9008 0.8465 0.8183 0.8183 p–severe 0.0000 0.0000 0.0002 0.0078 0.0232 0.0295 0.0570 0.0823 0.1160 0.1160 0.1160 p–fatal 0.0000 0.0000 0.0000 0.0009 0.0009 0.0019 0.0061 0.0169 0.0375 0.0656 0.0656
 Age group 1 2 3 4 5 6 7 8 9 10 11 Age range 0–5 6–12 13–19 20–29 30–39 40–49 50–59 60–69 70–79 80–89 90–105 Population 351159 451246 446344 711752 730547 723663 703830 582495 435834 185480 45230 p–mild 1.0000 1.0000 0.9998 0.9913 0.9759 0.9686 0.9369 0.9008 0.8465 0.8183 0.8183 p–severe 0.0000 0.0000 0.0002 0.0078 0.0232 0.0295 0.0570 0.0823 0.1160 0.1160 0.1160 p–fatal 0.0000 0.0000 0.0000 0.0009 0.0009 0.0019 0.0061 0.0169 0.0375 0.0656 0.0656
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