Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model
Authors: Yuan Chen
Wenbo Fei
Qinxia Wang
Donglin Zeng
Yuanjia Wang
Venue: NeurIPS 2021 Poster (2021)
Keywords: Spatial temporal modeling
COVID-19 pandemic
Health disparity
Selection bias
Gaussian process
Disease transmission
Latent process

Abstract: COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual- and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients.

Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model
Yuan Chen
Wenbo Fei Department of Biostatistics Columbia University
Department of Biostatistics
Memorial Sloan Kettering Cancer Center
New York, NY 10017
New York, NY 10032
cheny19@mskcc.org
wf wf22700cumc. columbiii umbia. edu
Qinxia Wang
Donglin Zeng
Department of Biostatistics Columbia University
Department of Biostatistics University of North Carolina at Chapel Hill
New York, NY 10032
Chapel Hill, NC 27516 dzeng@email unc edu
qu22238cumc wow.tmmc...oobiii... columbia. edu
Yuanjia Wang
Department of Biostatistics Columbia University
New York, NY 10032
yw2016 www.e/cccccccccccaaa...... cowwmbia....o.mmmmmm...mo.. edu
Abstract
COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-tempores model is included in the individual risk assessment so to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where fameing effects of community risk factors and significant interactions between individual- and community-leved risk factors are detected. By examining the sociocconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decisiio-makinng and facilitate better clinical management of COVID patients.
Introduction
The coronavirus disease 2019 (COVID-19) has created several surges of pandemic globally since early 2020 and continues to be major public health threat. COVID-19 related hospitalizations and
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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deaths have caused immense burdens to the health systems 21]. Therefore, it is crucial to study the community- level risk factors affecting the transmission of the disease and the individual-level risk factors of severe COVID-19 outcomes. We propose a spatial-temporal model for COVID-19 transmissions and weighted semiparametric time-varying coefficient model for hospitalizations that can integrate multiple levels of information and perform causal inference.
Various models have been proposed forecast the trend of COVID-19 transmissions [32 18
27
14 based on
(SEIR) model, Gaussian process, or agent- based modeling. Their main goals are on the prediction or forecast accuracy. On the other hand, socioeconomic disparities and spatial variations have been observed in COVID-19 transmissions and individual outcomes [12] [34]. Spatial correlations have been detected for infectious disease transmissions where the disease incidence tends to occur in spatial clusters especially during the initial stages of an outbreak 25 However, some of these previous forecast models analyze the individual-an temporal trends separately, and do not account for socioeconomic disparity effects.
Our first goal is propose a joint model for the temporal and spatial patterns across all areas, which allows explaining what accounts for the spatial variability and the spread of infectious disease, and would be more efficient than modeling two trends and for different areas separately. Specifically, the temporal dependence is modeled by taking a convolution of past infection numbers during a transmissible time interval following the framework in 27 The latent pre-symptomatic disease transmission phase is captured. To allow the spatial dependence, we construct a spatial conditional autoregressive (CAR) model under the Gaussian process account for the correlation in the infection rates among neighborhood areas and improve the estimation for areas with fewer cases. Area-specific socioeconomic factors are included to account for the heterogeneity in infection rates across regions. For efficient estimation, the proposed temporal and spatial dependence structures are united under a single objective function linked by the infection rates. By examining the spatial disparities and community-level risk factors, our model can facilitate policy decisions and better allocation of healthcare resources to control the disease transmission.
To assess an individual's 's risk of severe COVID outcomes after diagnosis, risk factors such as demographies and pre-existing medical conditions haven been identified partially account for the heterogeneity in patients' risks [22 29 16, (3). These existing risk assessment models are solely based on individual-level data. However, community-levs
risk factors such as neighborhood poverty have been shown to be associated with hospitalization rate (26). Another important community risk factor is the background risk of virus exposure in the neighborhood measured as the total number of diagnosed and pre-symptomatic infected individuals. This latent virus exposure can be estimated from the proposed spatial-temport disease transmission model. To fully capture individual risks, we need to consider all these community-leved factors.
Furthermore, there are multiple challenges to construct a valid risk assessment model. Firstly, sample selection bias from multiple sources can be present. For example, it is shown that several neighborhoods in New York City (NYC) with high case rates also had high testing rates (26). As a result, residents from areas with high case rate are more likely to be tested positive and thus more likely to be included in the study sample consisting of only diagnosed patients. A serious challenge that such selection bias may induce spurious association between risk factor and COVID outcomes and thus misleads interpretation and decision [15. Secondly, considering the dynamic nature of the pandemic, the effect of the risk factors may not be the same over time. For instance, the number of infected subjects varies substantially throughout the outbreak (26 and the drivers of the variation (e.g.. racial disparity) may be different early on in the pandemic versus later period. Hospital capacity is another important time-varying factor that influences a patient's 'ss chance of being admitted, especially during the initial outbreak (II). However, most existing studies do not consider the selection bias inherited in their study samples (15) and ignore the time-varying effects of the risk factors due to the evolving dynamics of pandemic. Lastly, most of existing risk assessment models lack rigorous calibration and validation procedures 33
Our second goal is to assess individual risk of severe COVID-19 outcomes accounting for community virus exposure burden and selection bias by a weighted spatial-temporal model. Specifically, we integrate individual-level risk factor data and neighborhood environmental risk factors including the latent time-varying neighborhood virus exposure to better account for patients' heterogeneous risks. Two sets of subject-specific weights are constructed to mitigate the selection biases so that after weighting the study sample could reflect the target population of interest and are not overly selected
2

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from neighborhoods with high infection rates. Furthermore, we build semiparametric models with time-varying coefficients to allow dime-dependent effects of hospital capacity and community factors (e.g., number of infectious COVID-19 patients predicted from the spatial-temporal transmission model). The method is applied to model COVID-19 transmission in NYC and assess individual risks using electronic health records (EHRs) of PCR confirmed COVID-19 patients diagnosed at New York Presbyterian Hospital (NYPH). Validation and calibration are conducted to evaluate the proposed method. Our model assists medical decision making and clinical management by integrating risk factors at multiple levels to test which factors significantly influence individual's 8 severe health outcomes after COVID-19 19 infection.
2 Method
2.1
Community-level transmission model accounting for spatial and temporal correlation
Model structure We propose a generative model that accounts for both temporal and spatial correlation of disease transmission while allowing heterogeneous infection rates across different areas accounted for by area-specific characteristics (e.g., distribution of minority population and social distancing measures in an area). We follow the framework of the survival convolution model proposed in [27] to account for the disease transmission during pre-symptomatic phase. We let at(t) denote the effective infection rate for the ith area (i = 1,... n) on day t defined as
(t)
ax(()
M,(1)*
where M,(1) is the number of infected subjects who remain in the transmission chain and can transmit virus to others (including those who are pre-symptomatic or asymptomatic) for the ith area on day t, and (t) is the number of newly infected subjects for the ith area on day t. Note that Mt(t) and (t) are both latent processes, and they are more accurate measures of the underlying virus exposure than the daily reported number of diagnosed cases. Let S(k) denote the proportion of individuals remaining infectious and who can transmit disease after is days of being infected, then (t) and (t) are related as
C. M.(1) = Σ N1(1- - k)S(k 1);
(2)
1=1
where C min(t (1 La, C) 4o is the unknown day when the first subject is infected in area i, and C' is the maximum incubation period (i.e., 14-21 days for COVID-19 [9].. We let two be C days prior to the first reported diagnosis of COVID-19 case. We assume that subjects under quarantine will not in the transmission chain, and thus o, (1) reflects the effective transmission rate either due to quarantine out of infectious period of SARS-COV-2 virus. It follows that the expected number of diagnosed subjects out of transmission chain in area i on day denoted as (I),,, can be calculated based on the latent processes
Yi(1) = Σ No(t - ()((()) S(k 1)]-
Note the number of diagnosed patients is the only quantity that is observe. To obtain other quantities, given la and Ne(tan) = 1, the expected number of newly infected cases N, (f) and diagnosed cases (t()) can be updated sequentially based on (2). I and (3) if the infection rates a, (t) and the survival function S--) are known. Here we model S(.) as the normalized survival function of the exponential
distribution, i(k) 5kk) (e-k/6 - e 0//8) - e e8/)) and we set its mean 5 = 5.2 following 199
More importantly, we will model the time- varying infection rate (t) borrowing strength from the neighborhood areas and area-specific lime-invarian and time-varying characteristics, c.g., demo- graphics, social vulnerability index, and mobility. Let (t denote the relative rate of infection in region i compared to a "baseline" expected rate at day t - averaged across regions denoted by Σn a, 1). and (0) 1. We
a(t 1), i.e., (t) (tt( 1), where a(t 1) denote the baseline average expected number of infections as (t = M, (t) 1), and assume the actual number of new infections has mean of (1)))a,()), or equivalently mean of (.())((().
We model the logarithm of the relative infection rate, (1) log (A)())), by a spatial con- ditional autoregressive (CAR) model to account for correlation between regions. Let Z(t)
3

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(Z) .Z)(t)) for time points t = 0.... .T. Similar to the small area estimation problem [TO] where the disease rates for areas with smaller at-risk populations are estimated less accurately, here when estimating (t))) in ( areas with smaller infectious population (!) are subject to more
estimation variability. Therefore, to borrow strength from areas with more infectious subjects, we assume (t) follows Gaussian process that exhibits spatial dependence as
(4) where X, is matrix of covariates (e.g., demographics, social vulnerability index, mobility, and include a column of constants of one). The covariance matrix Σ, should reflect heterogeneous variances of estimated infection rates different locations due differential baseline number of infectious subjects (t). In addition, the infection rates estimated from smaller populations are more variable and should use more pooling than areas with larger populations. To accommodate these factors, following the spatial rate model used for disease mapping [9] [0] and a spatial CAR
(tt) (V 3,, _x),
model 10, under the condition that H is positive definite, we specify
Σ, = 7 p: H) -1 A where A diag (t)
(5)
(t,
Furthermore, H = (hiz) has zeros as the diagonal terms, and the off-diagonal terms are specified as hag (() if E G(i), and O elsewhere, where G(i) indicates the set of areas that share borders with the ith area (i.e., the neighborhood of area i). In other words, we only borrow information from the neighborhood areas to improve the estimation, without pooling over irrelevant areas. With the covariance matrix specified in (5). we account for the larger variability of small areas with lower expected infection numbers E, (t) (or equivalent Mt)... Under this parametrization, can be shown that Pt represents the spatial partial correlation at time between neighborhood counties (i, given other regions, i.e., corr 2, (t), Z, (t), * i,j) = Pt. This correlation is invariant to the neighborhood structure of counties (i.e., does not depend on H), which desirable. We provide more explanation of the model structure in the Supplementary material. We will estimate a separate model at each time point We present an illustrate diagram for the model architecture in Supplementary Figure A.
Estimation
For estimation, we will combine the loss function for the reported daily new cases (t) to the expected number based on 3 and the likelihood of the log relative infection rates (t)
T [V(ORO)
"
T
Σ (Z(): + t=
Σ IIs. - E--1
(6)
=1
2=7+1
where ((Ze; E2) is the likelihood under the multivariate Gaussian distribution in with &, denoting the parameters of (Bt, Pt: T?), and ||-II1 is the L' norm. Note in (6) denotes the first day of diagnosed case across all areas. In other words, we align the areas according to the relative stage of disease transmission in modeling the spatial dependence. Here the first term in (6) is to evaluate the prediction performance from the latent disease transmission process where the square root transformation is a variance stabilization transformation for count data; the second term is to ensure the spatial smoothness between the areas and account for the heterogeneous infection rates by area- specific factors such as socioeconomics and mobility; and the last term is the fused lasso penalty to ensure the smoothness of the estimated parameters over time. Note that the fused lasso penalty will consequently encourage smoothness in the daily infection rates over time, which reduces variability and is consistent with observed data.
Furthermore, to avoid the high computational burden involved in maximizing the likelihood of the high-dimensional multivariate Gaussian process Z,, we consider an alternative approach based on optimizing conditional pseudo-likelihood often used to learn Gaussian graphical models. For given X, and all Zje² 's with j o 66 and 24 showed that the multivariate Gaussian model 44 with covariance matrix of the form (5) implies the conditional normal distributions with
(1))) E G(1) N(On, -2), One = Pi +
Σ JEC())
Pehin (1) p3))),
where uie = E(),(()) = X and - = -// Then the objective function T
6
becomes
T ( - -v + Σ
n
T
logra +
+ X
Σ
Is, -5-E.m E,111,,,
t
7+1
4

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so that the joint likelihood reduces to products of area-wise conditional likelihood and optimization much easier. To obtain solution, we treat (t) and _ as parameters and optimize the objective function by gradient descent implemented by Py Torch 23 on local and cluster CPUs.
Under this model, we estimate the time-varying between-area correlations of the infection rate and the effects from time-dependent community area-level covariates on the infection rates. With available parameters, the log-relative infection rates 02r can be estimated from 7 and forecast can be provided based on assumptions of the future epidemic trend (e.g., assuming similar trend over next d weeks as previous weeks).
2.2 Individual risk assessment accounting for selection bias, time-varying effects and community risks
Weights for controlling selection biases Presence of selection bias inherited in the study sample threatens validity of risk prediction models. Many existing models for predicting hospitalization/death once a subject is diagnosed of COVID-19 utilized study samples that only consisted of diagnosed patients, Also the study samples were usually collected from several selected hospitals. In this circumstance, one source of selection bias is that individuals who live closer to a specific hospital are more likely to be included in the study sample. Another source of selection bias is due to the heterogeneous COVID-19 19 case rates across different neighborhoods, so that individuals from areas with high case rate may be more likely to get tested and diagnosed 226 and thus over-represented in a study sample.
More importantly, since we are interested in in- vestigating whether the prevalence of neighbor- hood virus exposure (i.e., the underlying num- ber of infectious subjects (t)) as a measure of community COVID-19 19 disease burden has
Neighborhood infectious number
Discase severity
Sample selection
an effect on individual's COVID-19 19 outcomes, the second source of selection bias may induce spurious association. This phenomenon is also known collider bias [5 Sl which we illus- trate in Figure D Note that in this case the risk factor of interest (i.e., measure of community risk) and the outcome of interest (individual COVID outcome severity) are both associated with sample selection. This is because when the study population only consists of diagnosed patients, individuals from neighborhood with high infectious rate more likely to be diagnosed and thus included in the sample. On the other hand, individuals with more severe symptoms are also more likely to seek testing and get diagnosed, thus prone to be included in the sample. This is particularly the case during the early COVID-19 outbreak when the healthcare facilities were limited and testing was restricted to patients with severe symptoms (2). Taking these two associations into consideration, conditioning on the selected sample can induce association between the neighborhood infection prevalence and the disease severity even if they were marginally not correlated. Similar collider bias present for other risk factors of interest (e.g.. pre-existing medical conditions).
Illustration of the collider bias through a directed acyclic graph. Directed arrows indicate causal effects and dotted lines indicate induced associations.
Figure 1:
To address these two selection biases, we propose iiverse-rrobability weighting (IPW) adjustment with two sets of subject-specifie weights. We denote w1, as the first weight for the jith subject G L,..., m) constructed W1,- = -E area (e.g.. postal area) which subject comes from, p. denotes the true population density for area i (i.e., number of residents in area divided by the total population), and qi denotes the density of subjects coming from area i (i = 1,... n) in the selected sample. Therefore, after weighting, our sample resembles the true spatial population composition. The bias due association between other time-invariant risk factors and sample selection can be mitigated after addressing the sample selection bias with wij.
pi/q; [(S, = i) where Sy denotes the neighborhood
The second subject-specific weight aims to remove the association between the vareinarying com- munity infection rate and sample selection in order to mitigate the collider bias in Figure] Let W2 denote the second weight for subject j defined as
a
P1) I(s, = =0, D((1- - MCO Σ M.(), a(1) = (())))(()) Σ R.()). 70
W2j-
1=1
i=1
5

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Note T3 is the date when patient j is diagnosed of COVID-19, 19. M,(t) as defined in section 2.1 is the number of infectious subjects in area at day (including those who are pre-symptomatic and have not tested positive), and (t) is the reported number of diagnosed subjects in area i on day I. In other words, if there no causal effect between neighborhood infection prevalence and sample selection, the number of reported diagnosed patients in the sample should be proportional to the number of underlying infectious subjects from each neighborhood on each day. The weights w2 further balance the sample selection from each neighborhood on each day as pandemic continues and remove the bias due to unbalanced numbers of individuals seeking testing.
High-dimensional feature extraction from EHR data EHRs have been increasingly used to assist real world medical decision making. EHRs provide abundant information about patients' medical history, diagnoses, medications, treatment plans, medical procedures, and laboratory test results, etc. To better account for patients' heterogeneity in their existing medical conditions, in addition the variables derived based on diagnoses and prescribed medications recorded in the EHRS (e.g., diabetes, chronic kidney disease, anticoagulant use), we extract features from the high- dimensional EHRs containing CPT procedure codes and medication prescription codes. Specifically, we filter the top 50 most common procedures and 100 most common medications, and for each of them we record the number of occurrences for each subject. Next, factor model is fitted account for the shared variability in the 150 procedures and medications. Specifically. for subject j. we let y, My Az, + Ej. where yj is a vector of length p (p 150) representing the number of each procedure or prescribed medication, My is the population mean of y 1, ...: m). Z the lower- dimensiimas latent factors of length K. A is a x K matrix of factor loadings, and Ej is the residuals with E(e,) = 0 and diagonal covariance matrix. 2j and E, are assumed to independent.
These latent factors z; serve as efficient dimension reduction technique while capturing the most important common variations in patient's procedure and medication history. Thus, after fitting the factor model, we use the estimated 2; as extracted features for each subject. These latent factors are shown to be very strong predictors of the COVID-19 hospital admission later experiments. At the same time, we can interpret each latent factor by examining the loading matrix A. The factor loadings from the fitted model in the later experiment are explored in Figure D.7 in the Supplementary material and explained in section 3
Semiparametrie model for time-varying effects As discussed section 1 hospital capacity is a time-varying factor that could influence patient's COVID-19 related outcomes [1 28 Additionally,
the effect of neighborhood factors (e.g.. number or prevalence of infectious COVID-19 patients) vary at different stages of the outbreak. To accommodate these effects, we construct time-varying coefficients building the risk prediction models using splines. Mathematically, the time-varying
coefficient (x(t) can expressed as (t = Ex= Φx (1)a; where (bx(t) are the basis functions (e.g.. cubic splines), and we allow the effect of the covariates to vary by day. Then the postulated model for the probability of hospitalization can be written as
((a(TT (oo(T)) + on (T)))xx
,(x,, =
Σ nx 1=d+1
where (I)) 1/(1+ and we assume the first d variables have time- vire-varying effects. A roughness penalty for each time-varying coefficient is included in the objective function prevent overfitting and to promote smoothness, which leads to the following objective function
and X, (X,1, f1...... fe) are the feature variables for the jth subject and
m En f(C, TT)) +
m
d Σ
L(y,
(t)dt - f(, TT)) A
ou (t)
where L(y, (X,, is the cross-entropy loss for the binary outcome yj (c.g., hospital admission) and its fitted probability T(; for the jth subject. af" is the second derivative of the Ith time-varying coefficient. Generalized cross validation used to choose the tuning parameter Asymptotics for penalized splines have been previously established 7 31 Therefore, we can construct confidence intervals to evaluate the uncertainty of the model estimates and the predicted individual risks.

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3 Experiments
3.1 Transmission model accounting for spatial dependence
New York City (NYC) was the epicenter of COVID- in the United States during spring 2020 26 We applied the spatial-temporal disease transmission model described in section[2.]Jee the COVID- 19 data in NYC from early March to end of July 2020, which correspond to the first wave of the outbreak in NYC. ZIP code-level daily reported new cases, social vulnerability index (SVI, e.g., minority percentage) and mobility data from opted-in users (percentage of individuals shelter-in-place captured by their mobile devices) were used to model disease transmission process. We plot the covariates in Supplementary Figure D.1 where heterogeneity across ZIP areas in NYC are observed. The data sources and preprocessing were described in the Supplementary material.
We identified significant spatial correlation of COVID-19 transmission in NYC from the spatial- temporal model (p= 0.109, 95% CI: (0.102, 0.117)). To visualize the COVID transmission in NYC, in Figure 2 we show the estimated infection rate in each area at few representative time points. The first surge of the pandemic in NYC was characterized by the highest infection rates during mid to late March and a much lower rate during May with a slight uptick from June, corresponding to the NYC stay-at-home order which was in full effect from March 22 to June 8, 2020. Change points of infection rates (e.g., peak around March 19 and rehound around June 11) were observed in Supplementary Figure D.2, which shows the infection rates in each neighborhood from late February to the end of July. Spatial disparity and correlation at the ZIP code level were also observed in the figure. Furthermore, we detected significant racial disparity where neighborhoods with denser minority population suffer a higher risk of COVID-19 19 infection. The effect coefficient 3, has a decreasing trend and was estimated to be 0.046 (95% CI: (0.026, 0.066)) on March 10, 0.030 (95% CI: (0.012, 0.048)) on March 19, 0.020 (95% CI: (0.017, 0.023)) on June 11, and 0.017 (95% CI: (0.015, 0.018)) on July 11. The confidence intervals were constructed based on permuting residuals, and we describe the procedure in the Supplementary material. The shelter-in-place percentage was not significantly associated with infection rates. Since this mobility variable was collected from only opted-in individuals, more accurate and representative measure is required in order to improve the estimation. Additionally, we show the observed and estimated daily new COVID-19 cases for each ZIP code area in Supplementary Figure D.5. The fitted curves captured the central trend with a smoother fit. For a few zip-code areas, the number of cases was under-estimated, especially around the peak period due to multiple reasons. First, the data variability was high in some zip areas where there were abnormal/exttreme spikes. Those spikes were primarily due data backlog and sudden "data dumps" instead of true case rises []] Since our model encourages smoother fit by incorporating penalty terms for consecutive days, those abnormal spikes cannot be (and perhaps should not be) captured. Second, since we pool information from neighborhood areas to infer infection rates, when the local spatial dependence is not very strong, pooling information may affect the area-specific case estimation. However, for the majority of zip areas, the proposed spatial model is beneficial. Future studies may extend the current model to allow more flexible spatial dependence structure, e.g.. community-specific dependence parameters.
Experiments on simulated data We conducted additional numerical studies using simulated data to evaluate the performance of our proposed method in recovering the true time-varying coefficients accounting for the spatial and temporal correlation in COVID-19 19 transmission. Date were simulated based on observed NYC ZIP level data where minority percentage was included as the area specific feature variable. Area specific time-varying infection rates were simulated based on the Gaussian process model under the specified true parameters, where the true {)(() ranged from - -0.05 to 0.05, (1()) ranged from to 0.06, Ttt) ranged from 0.01 to 0.08, and was set to 0.012 to mimic the parameters estimated from the real data. The number of infections and reported cases were then simulated based on the proposed disease transmission model. The simulation code are provided in the Supplementary materials. We considered two scenarios, one for the 44 ZIP-code defined areas in Manhattan only and one for the 176 ZIP areas in all boroughs of NYC.
We replicated the simulation experiments 100 times. The rooted mean squared errors (RMSEs) in estimating the time-varying parameters presented Supplementary Figure D.3, where the RMSES were calculated across all time points and the figures show variability from the experiment replications. For the time-invariant parameter p. we obtained a mean estimate of 0. 107 (RMSE of 0.015 from 100 replications) under 44 areas and mean estimate of 0.116 (RMSE of 0.010) under
7

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2020-03-05 2020-03-26 2020-05-28
2020-06-18
Infection
|=.00--a..11
(2:2 (.0023
(0.231 0.30)
(0.232 0.202)
(026204222
Figure 2: Estimated infection rates for neighborhood areas in NYC selected dates.
176 areas. Therefore, the parameters of interest (i.e.. (t), 31 (t), and p) are accurately estimated, and the accuracy increases with the increase of number of areas (lower RMSEs under 176 areas compared to 44 areas). Furthermore, in Supplementary Figure D.4 we present the RMSEs in estimating the reported diagnosed cases based on the estimated parameters from 100 replications. Small RMSEs were obtained compared to the large daily reported case numbers (an average of 31 cases across time and areas with an average maximum of 296 cases across replications in the scenario of 176 areas). These simulation results demonstrate that the proposed method and learning algorithm can recover the true parameters accounting for area heterogeneity and spatial correlations as well as the underlying transmission process of COVID-19.
3.2 Individual risk assessment accounting for selection biases and time-varying effects
We applied the proposed bias- corrected semiparametric risk assessment model introduced in section 2.2 to 6911 subjects who were diagnosed of COVID-19 from two New York-Presbyterian hospitals from March 9 to July 6, 2020. Among these patients, 51.8% were female, the median age was 60, and there were a total of 3676 hospital admissions and 717 deaths. The EHR study protocol (with study end date of July 6) was approved by the IRB.
Table I: Fitted coefficients of hospitalization risk among patients
diagnosed with COVID-19.
Weighted
Unweighted
Age (standardized)
0.18 (0.1. 0.25)
0.4 (0.33. 0.46)
Fomale
-0.29 (40.43, -0.15
-0.33 (-0:45, -0,
Asian White Black vs. White
+0.82 -0.233)
-1.18 (-L.7, -0.67y
0008 (-0.38, 0.22)
0.02 ( (-0.22, 0.18)
Minority vs. White Hispanic: Yes vs. No Diabetes
-0.42 (-1.62, 0.78)
-1.23 (-2.1, -0.36y*
0,75 (0.49, 1.01)a**
1.06 (0.89, 1.24)a**
0.47 (0.03, 0,92y*
0.11 (-0.56, 0,34)
Chronic kidney disease
1.44 (0.2, 2.68y*
0.76 (-0.39, 1.91)
Respiratory discuse
2.46 (1.34, 3,58
1.69 (0.9. 2.48)
Cancer
1.31 (-0.03, 2.64)
(-0.69,
Mental illness Anticoagulant
0.86 (.0.52, 2.23)
0,87 (-0.17, 1911)
0.35 (-0.1 0.81)
(-0.37, 0.54)
Procedure & medication F1
0.1 (0.03, 0.18)**
-0.04 (-0.111 0.08) 1.11 (0.92. 1229)
Procedure & medication F2
11.09, 1.5
In building the risk assessment model, we integrated the individual EHR data with the neighborhood risk factors on two levels, spatially and temporally. Specifically, for each subject, to evalu- ate the risk due to the background risk in-
White Minority (%)
0.8. (.0.33, 2.44)
2.75 (1.75, 3.74)asa
Asian x Minority (%)
(-1.91
-0.86 1.41)
Black Minority (%)
(0.71, 2.99
0,91 (-1.91...) 0.1)
Minority x Minority (%)
-4.44 (-9.61, 0.73)
.3.03 (-7.73, 1.6)
Hispanic Yes x Minority (%)
1.77 (0.86, 2,68y
0.21 (-0.67, 1.I)
White Multi-unit (%)
1.19 3,08)
-0,95 (-2.56, 0.66)
Asian x Multi-unit (%)
-1.07 (-3.82, 1.67)
0.5 (-2.36, 3.36)
Black x Multi unit (%)
(-1.1, 1.73)
5.64 (4.12, 7.16)
of virus exposure infection, corporated the predicted number of in- fectious subjects (i.e., (t) acquired from the transmission model in sec- tion 2.1) in his/her neighborhood 7 days prior to diagnosis (on average 5 days of incubation [9 and we consider 2 days of time lapse before diagnosis) as a covariate in the risk assessment model. This measure is the total prevalence of COVID-19 infections including both symptomatic and pre- symptomatic subjects who have not yet tested positive, and thus more accurately assess community COVID-19 risk. Additionally, we matched each individual with the time-invariant neighborhood information (e.g., the neighborhood social vulnerability index) based on their billing 7IP codes. Interactions between individual-level race and ethnicity and neighborhood social vulnerability were examined. Time-varying intereept and time-varying coefficients for neighborhood infection preva- lence, minority percentage, multi-unit living percentage were constructed.
Minority x Multi-unit (9)
-0.59 (-7. 16, 5.97)
0.05 (1, 8.21)
Hlsspnicc Yes Multi unit (8)
2.46 (1.02, 3.9)
(3.58, 6.52)a4
for p-value < 0.05, s* for p-value < 0.01, **8 for p-value < 0.001.
To visualize the selection biases in the study sample, we plot in Supplementary Figure D.6 the sample frequency and heterogeneous infection patterns across ZIP code areas in NYC. To assess the utility of the weights derived in section 2.2 accounting for the selection biases. we fit both weighted and unweighted models assess hospitalization risk among diagnosed COVID-19 patients.
For the weighted model, we multiplied the two proposed weights, performed standardization, and then truncated at 5th and 95th percentiles to climinate extreme values. Model discrimination and
8

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calibration were evaluated on independent subjects through 4-fold cross vooss-alliation.. Experiments
were conducted using R package "mgev" 1.8.34 (license: GPL-2 GPL-3) [3] on local CPUs.
Results in Table I Suggests that accounting for the selection biases led to more meaningful model results than the unweighted model. Age, sex, race, medical conditions such diabetes, chronic kidney disease, respiratory disease are shown to be strong predictors for hospitalization in the weighted model, which consistent with the literature and CDC guidance [16 3. If we ignore the selection biases, some of these coefficients were not statistically significant and some were in the unexpected direction. For example, having diabetes was found to be associated with lower risk of hospitalization in the unweighted model. Additionally, we detected interactions between individual- characteristics and neighborhood-leve factors. Among African Americans and Hispanics. higher neighborhood minority percentage is associated with a higher risk of COVID-19 hospitalization. Hispanics from higher level multi-unit living environment in NYC were more likely to be hospitalized after being diagnosed of COVID-19.
The two factors we learned from the high-dimensional CPT codes and medication codes in the EHRs were shown to be strongly associated with severe COVID-19 outcomes. From the factor loadings in Figure D.7 in the Supplementary material, the first factor loads on almost all medications and procedures and represents the general medical burden. The second factor is more sparse and more predictive of hospitalization. It is mainly characterized by pain relievers, ancillary drugs for surgery, drugs for controlling side effects of cancer treatments, and glucose for hypoglycemia. Hence, the result indicates that history of surgery, cancer, and hypoglycemia are strongly related to an individual's higher risk of adverse COVID-19 outcomes, which is consistent with the literature [13 16.
We show the fitted time-varying coefficients in Figure 3 After accounting for individual risk factors, diagnosed patients had higher proba- bility of hospital admission at later time periods in the first surge of COVID-19 outbreak in NYC (Figure 3p). This may be explained by the lim- ited hospital capacity during the initial outbreak [17]. and more health care resources were avail- able towards May 2020. However, the death rate decreased consistently, indicating mortality higher when hospital resource is scarce [28]
Effecto of irfecious rumber
0.01
Interceet
0.00
0.8-
-0.4
-0.03
0
60
91
90
135
Days since 03-09
Days since 03-09
(a) Hospitalization
(b) Hospitalization
of vitctouus number
8.50-
Interoext
2.05-
(Figure 1)) On the other hand, higher neigh- borhood virus exposure (indicated by number of infectious subjects) is associated with higher risk for both hospitalization and mortality es- pecially during the post-peak period of the out- break (Figure (3). It suggests controlling neighborhood community infection prevalence may have an effect on preventing an individual's 's severe COVID-19 outcomes above and beyond an individual's own risk factors.
6:00-
-0.35
133
30
60
30
120
Days since G3-09
Days since 03-09
(c) Death
(d) Death
Figure 3: Time-varying coefficient of neighborhood infection prevalence (including pre-symptomatic infec- tioms) and intercept (with 95% confidence hands) on the risk of hospitalization and death respectively for subjects diagnosed with COVID
To examine the marginal effect of the background virus exposure at infection, we further show in Figure predicted time-narying risk for hospitalization under different prevalence of neighborhood infections. When the neighborhood COVID-19 prevalence is low, we observe decreasing risk of hospitalization which gradually tends stable after the peak of outbreak. However, the risk for hospitalization is consistently increasing if the neighborhood infection prevalence is persistently high.
We calibrated and validated the fitted model through a 4-fold cross validation, and results were averaged from all validation sets and presented in Figure 4 We evaluated the two fitted models on the validation sets after correcting selection biases via the two sets of weights why and W2y. A higher area under the receiver operating characteristic (ROC) curve is observed for the proposed method that addressed the selection biases. We assessed model calibration by comparing weighted mean predicted risk with mean observed risks grouped by deciles of the predicted risk on the validation sets. Much higher calibration is observed for the weighted model where the fitted regression line is very close to the diagonal line while the unweighted model fails to calibrate on the validation sets. These results demonstrate the generalizability of the proposed weighting strategy.
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2.00
reeecept 0.02 Slope: 0.94
Intercept: 0.04 Slope: 0.76
1,004
Obsonod
3.2-
Unangrtdd
100
Prodicted
Prodicted
(a) ROC curve
(b) Calibration, weighted model (c) Calibration, unweighted model
Figure 4: ROC and calibrations of the weighted and unweighted individual risk model on the validations sets. In (b) and (c), regression line (the solid line) is plotted for the observed vs. predicted risks (the closer to the diagonal dash line more desirable), the fitted intercept and slope are reported the figure.
4 Discussion
In this work, propose spatial-temporal model for COVID-19 transmission and bias-corrected semiparametric time-varying coefficient model for assessing individual risk for severe COVID-19 outcomes integrate multiple levels of information and draw causal inference. The spatial and temporal dependence for disease transmission and heterogeneous time-varying infection rates across areas are accommodated. The relative strength of community-leve and individual-level risk factors on explaining severe COVID outcomes are assessed accounting for evolving dynamics of the pandemic.
Infectious Number
A limitation of this work is that the individual-level EHR data were collected from two hospitals in NYC. Larger and more general populations can be considered to further test our method. COVID-19 19 vaccines were not available during the time period of our EHR data. Future studies can consider including vaccination information in transmission and disease risk modeling.
Predicted risk for hosptallizaion
0.8
25% quantie
quantil
0.64
Here we discuss the potential negative societal impacts. In this work, we account for the potential biases in the sample selection using carefully designed weights so that after weighting
0.2
60
90
120
Days since 03-09
sample could represent the general population of interest and avoid spurious association. Even though have made best attempts design the weights to balance the sample selections at the ZIP code level, there could be residual imbalances in specific socioeconomic factors because the weights were not explicitly defined by them. Additionally, although we have incorporated many risk factors to account for the individual health outcomes, individual-level behavioural measures such as masking were not included due to data availability. Therefore, when interpreting the results, potential unmeasured confounding shall be noted.
Figure 5: Estimated risk of hospitaliza- tion over under different prevalence of neighborhood infections (including pre- symptomatic infections as oppose to only re- ported infections). Other covariates fixed the mean age of 59, male, white, non- hispanic, no diabetes or other comorbidities, median value of the factor scores for proce- dure and medication. median value of the neighborhood minority level and multi-unit living level.
We detected a significant spatial correlation of COVID- 19 transmission in NYC and significant racial disparity where neighborhoods with a denser minority population suffer higher risk of COVID-19 infection. We also identified significant interaction between individual's is race and community- level SVIs for COVID-19 hospitalization beyond an individual's other risk factors (e.g., age, co- morbidities). These findings suggest an intricate interplay between individual-level and community- level risks for COVID-19, and community-leve risk factors are non-ignorable even after accounting for the individual-level characteristies. The significant interactions and time-varying effects can facilitate precision public health decision-making at both community- and individual- i.e., to inform when, which population, and in what communities should we target the intervention to better reduce the hospitalization burden. For example, our results suggest that it can be beneficial to target intervention to Hispanic and black communities living in areas with dense minority populations and target the multi-unit living buildings specifically for the Hispanic population. To prepare for future pandemics, a comprehensive approach targeting both individual health and community risks highly desirable.
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Acknowledgments and Disclosure of Funding
This research supported by U.S. NIH grants CA008748, NS073671, GM124104, and MH123487.
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14


Reviews:
[{"review_id": "FzWdXfq8lA", "reviewer": "Reviewer_KY3s", "summary": "Authors propose a spatio-temporal model for modelling evolution of SARS-CoV2 and then they use that model with the a semi-parametric model to analyse risks of COVID-19 at individual levels. ", "questions": "", "limitations": "", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Authors propose a spatio-temporal model for modelling infections, hospitalisations and deaths related to COVID19. They then further extend their analysis by using a semi-parametric model to asses individual level risks by using EHR data from two hospitals in NYC. The  paper is written in a very elegant and simple way that makes it easier for reader to parse through the ideas and get to the main goals. The experiments and methodology is sound. \n\nI think, authors are overplaying their contributions in terms of coming up with a spatio-temporal models. Temporal models are back bone of infectious diseases, they are not just limited to citation 26 in the paper. The history of models using delay distributions could go way back to use of renewqal equations in Willy Feller. “On the Integral Equation of Renewal Theory”. In:The An-nals of Mathematical Statistics(1941).issn: 0003-4851.doi:10.1214/aoms/1177731708 or Richard Bellman and Theodore Harris. “On Age-Dependent Binary Branch-ing Processes”. In:The Annals of Mathematics(1952).issn: 0003486X.doi:10.2307/1969779. Practical use of these equations could be seen in Fraser, C. Estimating Individual and  Household Reproduction  Numbers in an Emerging \nEpidemic. PLOS ONE 2, e758 (2007)., E. Goldstein et al.: Proc. Natl. Acad. Sci. U.S.A. 106, 21825 (2009) and A. Cori et al.: Am. J. Epidemiol. 178, 1505 (2013). In fact one of the very first model for modelling covd19 in Europe, S. Flaxman et al.: Nature 584, 257 (2020),  is also based on approach the authors claim to be novel. I would urge them to downplay this claim and even the claim that infectious disease models generally are not temporal, at best it is actually exactly the other way around. Similarly various spatial models have also been around there for modeling SARS-COV2 for long like J. S. Jia et al.: Nature 582, 389 (2020), Giuliani, D., Dickson, M.M., Espa, G. et al. Modelling and predicting the spatio-temporal spread of COVID-19 in Italy. BMC Infect Dis 20, 700 (2020). https://doi.org/10.1186/s12879-020-05415-7, F. Bartolucci et al.: Spatial Stat. 100504 (2021), https://localcovid.info/data-methods.html and many others. So again I willask authors to downplay their claims. \n\nSimilarly like modelling of spread the model to account for individual level risk  is standard and something used in the public health for a long-long time. SO again these contributions are not novel, hence request is to downplay this part. \n\nHowever, the results and analysis is quite detailed and looks for things of interest. I will urge authors to talk about them more and push that as their main contribution. Lastly, I will like authors to release their code at least. I can see why data they can't but see no reason behind not releasing the code. ", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes they have", "review_text": "", "extra_scores": {"ethical_concerns": "none they have permision from IRB", "needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "5"}, "extra_text": {}}, {"review_id": "E5cIokDuYdV", "reviewer": "Reviewer_e3RN", "summary": "This work proposes (a) a community-transmission model, and (b) an individual risk-assessment model for COVID-19. The model is general enough to be applicable to any infectious disease. The model for (b) is enriched by inferred variables from (a) by (i) using them as covariates and (ii) addressing the selection bias in the data used for (b). \n\n### community-transmission model\n\nFirst, this work links the temporal model for forecasting the number of infectious cases with a spatial model to account for disease propagation between regions. \nThe connection is made via modelling the relative infection rate as a Gaussian Process parameterized by a covariance matrix that depends on the expected number of infectious cases in the neighbouring regions. \nThe simulation results in the Appendix demonstrate that the model is able to uncover the true underlying parameters. \nThis spatiotemporal model is then fitted to the reported number of daily COVID-19 cases in NYC from March to July, and the results show a substantial spatial correlation in disease propagation. \n\n### individual risk-assessment model\n\nFinally, an individual risk-assessment model is constructed using the inferred variables from the above fitted spatiotemporal model.\nThe data used to build this model is from two hospitals in New York. \nThe authors addressed the issue of selection bias in the data via subject-specific weights by making the sample representative of true underlying prevalence and spatial population.\nThe results seem to suggest a superior fit to the data compared to the unweighted model. \n\n", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "### Writing\n\nThe paper could have been written or structured better. It was hard to follow through the description at times, for example, it was difficult to follow the description in Methods at places. Some equations use variables that are explained after quite a while, for example, $\\rho_t$ in eq. 5. Some variables are not explained at all, for example, $\\tau_t$ in eq.5.\n\nIt was difficult to connect the two models. See my last comment in Experiments. \n\n### Novelty\n\nThere have been several discussions in the community about growing inequalities due to the COVID-19 pandemic.\nAs pointed out in the introduction, and to the best of my knowledge, there aren’t studies that forecast infection or assess individual risks by incorporating socio-economic and spatial factors even though these factors are deemed crucial in the transmission of viruses.\nThis research attempts to mathematically model and assess the correlation between various socioeconomic variables to individual disease severity and spatial propagation. \nI think this work could be useful both practically and to make further progress in the field of modelling infectious diseases with socioeconomic factors. \n\n### Methods\n\n1. Line 113: Survival function seems similar to assuming characteristics of an infectious individual’s likelihood to infect susceptible individuals. Following this line of thought, can the survival function follow the same distribution as viral load curve, i.e., a gamma distribution with the same mean (akin to the modelling of infectiousness in most published studies)? Do the authors think that will result in better estimations? \n2. Eq. 5: Is the choice $\\Sigma_t$ motivated from prior work? Can the authors briefly describe the intuition behind such a choice in Appendix? It is hard to find this in the resources referenced.\n3. Line 135: Can the denominator be 0? If so, what should be $h_{ij}$ in that case? Will this not happen because the summation in Eq. 6 is from the day of observing a min of 1 case across all areas?\n4. Line 142-143: I don’t get how $\\rho_t$ is a scalar given that it depends on i,j,k. Can the authors provide proof of this statement or refer to the relevant text in the referenced papers? Even an intuitive explanation might suffice for the readers. \n5. Line 173: Can the authors suggest a way to modify Eq. 7 to account for the reversal of trend (e.g., waves of infections after some decline in cases)? \n6. Line 233 - 245: Can the authors comment whether PCA could be as useful for dimensionality reduction as the proposed factor model? Did they try PCA? Are there results on PCA as well? \n7. Line 246-263: What is the relation between $\\tilde{\\mathbf{X}}_j$ and $\\hat{\\mathbf{z}}_j$?\n8. Eq.6: Is the first term any different from $(R_i(t) - Y_i(t))^2$? It doesn't seem like, but if so, can the authors explain so that the reader is aware?\n\n### Experiments\n\n1. Line 282: Can it be shown visually? For example, show a spatial map of the density of the minority population. This will help the reader understand this sentence better. \n2. Line 283: Is it $\\rho_t$ instead of $beta_t$?\n3. Line 287: The procedure to construct confidence bounds is not explained in the supplementary material.\n4. It is not entirely clear how exactly the spatio-temporal model in section 2.1 is related to the individual-risk model in section 2.2. The connection is through the correction for sampling bias ($w_{2j}$) and using $M(t-7)$ as a covariate. For a complete understanding of this connection, can the authors compare their regression results with a risk model that includes only $w_{1j}$ and doesn’t use $M(t-7)$ as a covariate?\n\n### Minor comments\n\n1. Line 244: Figure C.4 (not C.3) corresponds to the factor loadings\n2. Line 291: Figures C.1 and C.2 are exactly the same. I think you meant Figure C.3. \n3. Line 308: IRB could mean different things to people from different countries. Can the authors please provide a link to this organization?\n\n### Broader comments\n\nCan the authors comment on what would be the pitfalls or advantages of jointly training the two models? Does it sound reasonable to do so? \n\nCan the authors comment on whether their model would be appropriate to forecast waves of infection? Will it require additional variables in modelling?\n\n\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The paper has discussed the potential pitfalls of their modelling approach. ", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": ["I don’t know"], "time_spent_reviewing": "10"}, "extra_text": {}}, {"review_id": "83wYSDNB3S", "reviewer": "Reviewer_L2tY", "summary": "This study aims to estimate the daily infection rate and individual risk of hospitalization through offering a modeling of COVID-19 pandemic. They find correlations between the individual characteristics (e.g., age and race) with the community neighborhood. They have use real-world data of NYC cases during the 2020 pandemic.", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "- My main concern with this paper is its relevance to the NeurIPS community. The paper is more suited for submission to data analytics and knowledge discovery venues. \n\n- The topic of this paper is among one of the most studied during the past year. Modeling the Covid-19 and estimating the new cases are well-studied and there are plethora of models to benchmark the results of this study against. However, authors do not perform a thorough research on the related studies and offer no comparison with any benchmarks. It is hard to judge the merit of the proposed model in the absence of any baseline.\n\n- The study offers marginal novelty over the existing methods, as evident from the amount of methods borrowed in the paper from the previous studies. More specifically, the impact of communities on a viral spread (and especially on COVID-19) is well-studied. So is the relationship between the individual features and the risk of severe outcomes upon diagnosis. \n\n- Although the study claims to perform casual inference, the results only find correlation between the selected features. Moreover, the authors do not discuss any of the discovered correlations in detail (for example, what might be underlying cause of increased hospitalization among minority groups). The study does not offer much more than a simple analytic study.\n\n- In figure C.3 in appendix where the difference between observed and predicted daily cases are given, for some zip codes the prediction is much weaker than the observed value (e.g., 11375, 11368, 11229). These cases are the interesting ones that show an underlying factor(s) change the pattern of infection unexpectedly. Deeper analysis to find the difference between these zipcodes and the rest help to understand the possible shortcomings of the proposed model (and addressing them).\n\n\nMinor comments:\n    - Line 24: \"health system\" --> \"healthcare system\" ", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "See above.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "3"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents novel ML approaches to examining the socioeconomic disparity of infection risks and interaction among the risk factors, to assist public health decision-making and facilitate better clinical management of COVID patients. This study is highly relevant to the NeurIPS community. As one reviewer states, this work pushes the boundary of merging the epidemiological analysis with socio-economic factors and is quite ground breaking in this respect, with the potential for huge societal impact. The paper is methodologically solid and the authors have presented a strong rebuttal and discussion with the reviewers
Decision: Accept (Poster)