Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

Vahedi, Behzad; Karimzadeh, Morteza; Zoraghein, Hamidreza

doi:10.1038/s41467-021-26742-6

Cited by 26 publications

(20 citation statements)

References 55 publications

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“…Together, these features help capture the heterogeneous spatial spread of COVID-19, both intra- and inter-county, for a given week [ 93 ]. In order for our model to learn the spatiotemporal spread, we create temporal series of the weekly averages of each of these variables.…”

Section: Datamentioning

confidence: 99%

“…SCI is a static index produced annually; however, as SPC is weighted by the weekly COVID-19 incidence rate, SPC is a dynamic measure of proximity to cases. Together, these features help capture the heterogeneous spatial spread of COVID-19, both intra-and inter-county, for a given week [93]. In order for our model to learn the spatiotemporal spread, we create temporal series of the weekly averages of each of these variables.…”

Section: Facebook-derived Spatial Featuresmentioning

confidence: 99%

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A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

Lucas

Vahedi

Karimzadeh

2022

Int J Data Sci Anal

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With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental models, are based on the assumption of spatiotemporal homogeneity of the spread of the virus, which may cause forecasting to underperform, especially at high spatial resolutions. In this paper, we approach the forecasting task with an alternative technique—spatiotemporal machine learning. We present COVID-LSTM, a data-driven model based on a long short-term memory deep learning architecture for forecasting COVID-19 incidence at the county level in the USA. We use the weekly number of new positive cases as temporal input, and hand-engineered spatial features from Facebook movement and connectedness datasets to capture the spread of the disease in time and space. COVID-LSTM outperforms the COVID-19 Forecast Hub’s Ensemble model (COVIDhub-ensemble) on our 17-week evaluation period, making it the first model to be more accurate than the COVIDhub-ensemble over one or more forecast periods. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate than the COVIDhub-ensemble. We highlight that the underutilization of data-driven forecasting of disease spread prior to COVID-19 is likely due to the lack of sufficient data available for previous diseases, in addition to the recent advances in machine learning methods for spatiotemporal forecasting. We discuss the impediments to the wider uptake of data-driven forecasting, and whether it is likely that more deep learning-based models will be used in the future.

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Section: Datamentioning

confidence: 99%

Section: Facebook-derived Spatial Featuresmentioning

confidence: 99%

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

Lucas

Vahedi

Karimzadeh

2022

Int J Data Sci Anal

Self Cite

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“…95% "zero" 90% "zero" 80% "zero" 70% "zero" 60% "zero" 50% "zero" accuracy, especially for counties with very small/zero sample sizes. Some studies (Khan et al, 2018;Gibbs et al, 2020;Oleson et al, 2008;Vahedi et al, 2021) suggested incorporating the spatial random effect by borrowing strength from space can improve model fit and estimate accuracy. The topic is slightly out of the scope of this study, we may explore the spatial random effect in the BBZ and other BHR models in the future study.…”

Section: Discussionmentioning

confidence: 99%

Modeling County-Level Rare Disease Prevalence Using Bayesian Hierarchical Sampling Weighted Zero-Inflated Regression

Xie¹,

Rolka²,

Barker³

2022

Journal of Data Science

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Estimates of county-level disease prevalence have a variety of applications. Such estimation is often done via model-based small-area estimation using survey data. However, for conditions with low prevalence (i.e., rare diseases or newly diagnosed diseases), counties with a high fraction of zero counts in surveys are common. They are often more common than the model used would lead one to expect; such zeros are called ‘excess zeros’. The excess zeros can be structural (there are no cases to find) or sampling (there are cases, but none were selected for sampling). These issues are often addressed by combining multiple years of data. However, this approach can obscure trends in annual estimates and prevent estimates from being timely. Using single-year survey data, we proposed a Bayesian weighted Binomial Zero-inflated (BBZ) model to estimate county-level rare diseases prevalence. The BBZ model accounts for excess zero counts, the sampling weights and uses a power prior. We evaluated BBZ with American Community Survey results and simulated data. We showed that BBZ yielded less bias and smaller variance than estimates based on the binomial distribution, a common approach to this problem. Since BBZ uses only a single year of survey data, BBZ produces more timely county-level incidence estimates. These timely estimates help pinpoint the special areas of county-level needs and help medical researchers and public health practitioners promptly evaluate rare diseases trends and associations with other health conditions.

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“…Space–time models also need to incorporate the spatial diffusion or spillover related to behaviours such as commuting [ 18 , 19 ]. It is also especially useful in policy terms to be able to extrapolate the infectious disease evolution beyond the observation span, as illustrated in some studies of the COVID epidemic [ 20 , 21 , 22 , 23 ].…”

Section: Relevant Literaturementioning

confidence: 99%

A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic

Congdon

2022

IJERPH

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Spatio-temporal models need to address specific features of spatio-temporal infection data, such as periods of stable infection levels (endemicity), followed by epidemic phases, as well as infection spread from neighbouring areas. In this paper, we consider a mixture-link model for infection counts that allows alternation between epidemic phases (possibly multiple) and stable endemicity, with higher AR1 coefficients in epidemic phases. This is a form of regime-switching, allowing for non-stationarity in infection levels. We adopt a generalised Poisson model appropriate to the infection count data and avoid transformations (e.g., differencing) to alternative metrics, which have been adopted in many studies. We allow for neighbourhood spillover in infection, which is also governed by adaptive regime-switching. Compared to existing models, the observational (in-sample) model is expected to better reflect the balance between epidemic and endemic tendencies, and short-term extrapolations are likely to be improved. Two case study applications involve COVID area-time data, one for 32 London boroughs (and 96 weeks) since the start of the COVID epidemic, the other for a shorter time span focusing on the epidemic phase in 144 areas of Southeast England associated with the Alpha variant. In both applications, the proposed methods produce a better in-sample fit and out-of-sample short term predictions. The spatial dynamic implications are highlighted in the case studies.

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Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

Cited by 26 publications

References 55 publications

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

Modeling County-Level Rare Disease Prevalence Using Bayesian Hierarchical Sampling Weighted Zero-Inflated Regression

A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic

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