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

Emmanuel Fleurantin; Christian Sampson; Daniel Paul Maes; Justin Bennett; Tayler Fernandes-Nunez; Sophia Marx; Geir Evensen

doi:10.3934/fods.2021022

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An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation

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

Emmanuel Fleurantin ^1, , Christian Sampson ^2, , Daniel Paul Maes ^3, , Justin Bennett ^4, , Tayler Fernandes-Nunez ^5, , Sophia Marx ^5, and Geir Evensen ^6,

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.

Keywords: SARS-CoV-2, COVID-19, effective reproduction number, ensemble smoothers, data assimilation, multi-population SEIR model, age stratification, ESMDA, parameter estimation, model calibration.

Mathematics Subject Classification: 91F99, 65K10, 65P99, 65K99.

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:

[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.

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.

Figure 1. 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

Scalings	Matrix Parameters
Scalings	$\alpha$	$\beta$	$\gamma$	$\eta$	$\epsilon$	$\zeta$	$\delta_1$	$\delta_2$	$\delta_3$	$\xi_1$	$\xi_2$	$\xi_3$	$\xi_4$
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

Interventions	Information on Intervention Periods by State and the District of Columbia
Interventions	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

Race/Ethnicity	Percentage of Population per State and the District of Columbia
Race/Ethnicity	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

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=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=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=0.009 $	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

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

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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American Institute of Mathematical Sciences

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

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