Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis

Huang, Rui; Liu, Miao; Ding, Yongmei

doi:10.3855/jidc.12585

Cited by 140 publications

(129 citation statements)

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“…The studies by Chen et al [21], Yang and Wang [20], Anastassopoulou et al [19], Tang et al [18], Zhao and Chen [17], Wu et al [16], Huang et al [22], and Wan et al [23] were used as references for the SEIR model settings in the present study. The model settings were as follows: respectively, then the rate of change in the size of the susceptible population was defined as follows:…”

Section: Construction Of Seir Modelmentioning

confidence: 99%

“…This study was distinctive from the studies by Wu et al [16], Zhao and Chen [17], Tang et al [18], Anastassopoulou et al [19], Yang and Wang [20], Chen et al [21] and Huang et al [22] in the following ways: First, population migration was embedded in the SEIR model to simulate and analyze the effects of the amount of population inflow on the number of confirmed cases. Second, compared with existing studies based on numerical simulations, this study used statistical data for the empirical validation of its theoretical deductions.…”

Section: Introductionmentioning

confidence: 98%

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Population migration, spread of COVID-19, and epidemic prevention and control: empirical evidence from China

Yang

et al. 2020

Preprint

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Background: This study applied the SEIR model to analyze and simulate the transmission mechanisms of the coronavirus disease 2019 (COVID-19) in China. Methods: The population migration was embedded in the SEIR model to simulate and analyze the effects of the amount of population inflow on the number of confirmed cases. Based on numerical simulations, this study used statistical data for the empirical validation of its theoretical deductions and discussed how to improve the effectiveness of epidemic prevention and control considering population migration variables. Statistics regarding the numbers of infected people in various provinces were obtained from the epidemic-related data reported by China’s National Health Commission.Results: This study explored how the epidemic should be prevented and controlled from the perspective of population migration variables. It found that a combination of a susceptible population, an infected population, and transmission media was an important route affecting the number of infections and that the migration of a Hubei-related infected population played a key role in promoting epidemic spread. Epidemic prevention and control should focus on regions with better economic conditions than the epidemic region. Prevention and control efforts should focus on the more populated neighboring provinces having convenient transportation links with the epidemic region. To prevent and control epidemic spread, priority should be given to elucidating the destinations and directions of population migration from the domestic origin of infections, and then stemming population migration or human-to-human contact after such migration. Conclusions: This study enriched and expanded on simulations of the effects of population migration on the COVID-19 epidemic and China-based empirical studies while offering an epidemic evaluation and warning mechanism to prevent and control similar public health emergencies in the future.

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Section: Construction Of Seir Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Population migration, spread of COVID-19, and epidemic prevention and control: empirical evidence from China

Yang

et al. 2020

Preprint

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“…23 Here, we discussed a detailed analysis of the spatial diffusion of COVID-19 in São 24 Paulo State, Brazil, with the objective of providing real-time responses to support 25 public health strategies. This approach can be done in other states of Brazil as well as 26 in other developing countries [8]. 27…”

Section: Introductionmentioning

confidence: 99%

“…In the second step, data about each municipalities such as infrastructure, facilities, 57 land use, jobs, and urban mobility were used to identify the fundamental entities of the 58 spatial structure that triggers coronavirus dispersion in São Paulo territory [11]. In the 59 visualization step, we attempted to produce a map that could be understandable by 60 health authorities and community [8,9]. The proportional symbol maps scale was used 61 to size the circles proportionally to the number of confirmed cases in each municipality.…”

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confidence: 99%

Elementary spatial structures and dispersion of COVID-19: health geography directing responses to public health emergency in São Paulo State, Brazil

Fortaleza

2020

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Public health policies to contain the spread of COVID-19 rely mainly on non-pharmacological measures. Those measures, especially social distancing, are a challenge for developing countries, such as Brazil. In São Paulo, the most populous state in Brazil (45 million inhabitants), most COVID-19 cases up to April 18th were reported in the Capital and metropolitan area. However, the inner municipalities, where 20 million people live, are also at risk. As governmental authorities discuss the loosening of measures for restricting population mobility, it is urgent to analyze the routes of dispersion of COVID-19 in those municipalities. In this ecological study, we use geographical models of population mobility as patterns for spread of SARS-Cov-2 infection. Based on surveillance data, we identify two patterns: one by contiguous diffusion from the capital metropolitan area and other that is hierarchical, with long-distance spread through major highways to cities of regional relevance. We also modelled the impact of social distancing strategies in the most relevant cities, and estimated a beneficial effect in each and every setting studied. This acknowledgement can provide real-time responses to support public health strategies.

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“…68 Statistical models often eschew deterministic population dynamics and fit the 69 observed data as a function of time and possibly other covariates in a regression (or 70 equivalent) framework. Log-linear [74], generalized Richards [75], ARIMA [76,77], 71 exponential [78], Gaussian CDF [79], and logistic [80][81][82] models, which all 72 accommodate the generally sigmoidal shape of the cumulative infection count that is 73 often observed in epidemics, as well as various other models [83][84][85][86] including machine 74 learning algorithms [87][88][89] have been proposed for COVID-19. Murray et al and 75 Woody et al take similar approaches for modeling COVID-19 deaths using the error 76 function (ERF) [90,91].…”

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confidence: 99%

Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

Watson

Xiong

Zhang

et al. 2020

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Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian nonlinear mixed model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course. Author summaryCOVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian nonlinear mixed model for case velocity and a machine learning algorithm (random : medRxiv preprint forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We also identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

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Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis

Cited by 140 publications

References 12 publications

Population migration, spread of COVID-19, and epidemic prevention and control: empirical evidence from China

Population migration, spread of COVID-19, and epidemic prevention and control: empirical evidence from China

Elementary spatial structures and dispersion of COVID-19: health geography directing responses to public health emergency in São Paulo State, Brazil

Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

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