Globally local: Hyper-local modeling for accurate forecast of COVID-19

Gopalakrishnan, Vishrawas; Pethe, Sayali; Kefayati, Sarah; Srinivasan, Raman; Hake, Paul; Deshpande, Ajay; Li, Xuan; Hoang, Etter; Davila, Marbelly; Bianco, Simone; Kaufman, James H.

doi:10.1016/j.epidem.2021.100510

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Their approach incorporated a Lagrangian movement model that captures population movements within and among regions. Gopalakrishnan et al [ 53 ] emphasized the impact of spatial granularity on COVID-19 forecasting results using different unit areas of state, county cluster and county; however, they did not take into account the spatial interactions between these areal units in their compartmental forecasting model. Liu and Li [ 54 ] proposed a multi-group SEIR model taking into account the spatial heterogeneity through incorporating spatial diffusion and heterogeneity in the model parameters.…”

Section: Discussionmentioning

confidence: 99%

Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model

Hatami

Chen

Paul

et al. 2022

IJERPH

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The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte–Concord–Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model’s predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.

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Section: Discussionmentioning

confidence: 99%

Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model

Hatami

Chen

Paul

et al. 2022

IJERPH

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“…Brizzi et al [ 2 ] pointed out that there is a tendency to overestimate the parameter R 0 and the basic effective reproduction R t during the onset of an epidemic. Gopalakrishnan et al pointed out that data otherwise reliable obtained for a specific geographical region, no matter how large, does not necessarily apply in other locations [ 3 ]. Moreover, they stress that state-level data are defective, when applied to policy decisions for smaller areas, since they lead to errors which can be as large as 200 to 300%.…”

Section: Introductionmentioning

confidence: 99%

Model based on COVID-19 evidence to predict and improve pandemic control

et al. 2023

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Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.

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