Dataset Information

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei.

ABSTRACT: At the moment of writing, the future evolution of the COVID-19 epidemic is unclear. Predictions of the further course of the epidemic are decisive to deploy targeted disease control measures. We consider a network-based model to describe the COVID-19 epidemic in the Hubei province. The network is composed of the cities in Hubei and their interactions (e.g., traffic flow). However, the precise interactions between cities is unknown and must be inferred from observing the epidemic. We propose the Network-Inference-Based Prediction Algorithm (NIPA) to forecast the future prevalence of the COVID-19 epidemic in every city. Our results indicate that NIPA is beneficial for an accurate forecast of the epidemic outbreak.

SUBMITTER: Prasse B

PROVIDER: S-EPMC7341469 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei.

Prasse Bastian B Achterberg Massimo A MA Ma Long L Van Mieghem Piet P

Applied network science 20200708 1

At the moment of writing, the future evolution of the COVID-19 epidemic is unclear. Predictions of the further course of the epidemic are decisive to deploy targeted disease control measures. We consider a network-based model to describe the COVID-19 epidemic in the Hubei province. The network is composed of the cities in Hubei and their interactions (e.g., traffic flow). However, the precise interactions between cities is unknown and must be inferred from observing the epidemic. We propose the ...[more]

PMID: 32835088

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Project description:Introduction: The current outbreak of the novel coronavirus disease 2019 (COVID-19), originating from Wuhan (Hubei, China), has rapidly spread across China and several other countries. During the outbreak of COVID-19, mental health of the general population in Hubei province may be affected. This study aimed to assess the psychological status and associated risk factors of the general population in Hubei province during the COVID-19 outbreak. Methods: A cross-sectional online survey was used to evaluate the symptoms of posttraumatic stress disorder (PTSD), depression, and anxiety, which were assessed by the Chinese version of the Impact of Event Scale-Revised, the Patient Health Questionnaire 9, and the seven-item Generalized Anxiety Disorder Scale, respectively. Coping style was assessed by the Simplified Coping Style Questionnaire. Multivariate logistic regression analysis was carried out to detect factors associated with mental health outcomes. Results: Among 9,225 participants, 44.5% rated symptoms of PTSD, and 17.9 and 12.7% suffered from moderate and severe symptoms of depression and anxiety, respectively. Individuals who were geographically located in Wuhan and familiar with someone who has COVID-19 had more severe symptoms of PTSD, depression, and anxiety, as well as a higher score in passive coping style (P < 0.05). Multivariate logistic regression analysis showed that people who were geographically located in Wuhan [odds ratio (OR) = 1.25, 95% confidence interval (CI) = 1.14-1.36, P < 0.001] were associated with severe symptoms of PTSD. Besides, individuals who were familiar with someone who had COVID-19 (OR = 2.33, 95% CI = 2.07-2.63, P < 0.001; OR = 1.90, 95% CI = 1.66-2.17, P < 0.001; OR = 2.06, 95% CI = 1.78-2.39, P < 0.001) and had a higher score in passive coping style (OR = 1.16, 95% CI = 1.14-1.17, P < 0.001; OR = 1.17, 95% CI = 1.15-1.19, P < 0.001; OR = 1.17, 95% CI = 1.15-1.19, P < 0.001) were associated with severe symptoms of PTSD, depression, and anxiety. Moreover, a higher score in active coping style (OR = 0.96, 95% CI = 0.95-0.97, P < 0.001; OR = 0.94, 95% CI = 0.93-0.94, P < 0.001; OR = 0.95, 95% CI = 0.94-0.96, P < 0.001) was associated with a lower risk of symptoms of PTSD, depression, and anxiety. Conclusions: During the midphase of COVID-19 outbreak, quite a few people have mental health problems; nearly half of the respondents rated symptoms of PTSD, and approximately one-fifth reported moderate to severe symptoms of anxiety and depression. Our findings may lead to better comprehend the psychological status of the general public and alleviate the public mental health crisis during the COVID-19 outbreak.

Project description:The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people's lives and socio-economic development throughout China and across the globe since December 2019. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman's rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspot and cluster/outlier area was observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation (p < 0.01) with the registered population (RGP), resident population (RSP), Baidu migration index (BMI), regional gross domestic production (GDP), and total retail sales of consumer goods (TRS), respectively, from 29 January to 18 February 2020 and had a negative and significant correlation (p < 0.05) with minimum elevation (MINE) from 2 February to 18 February 2020, but no association with the land area (LA), population density (PD), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) from 23 January to 18 February 2020. (4) At the county level, the number of CCC in Hubei province had a positive and extremely significant correlation (p < 0.01) with PD, RGP, RSP, GDP, and TRS, respectively, from 27 January to 18 February 2020, and was negatively associated with MINE, MAXE, MNE, and RAE, respectively, from 26 January to 18 February 2020, and negatively associated with LA from 30 January to 18 February 2020. It suggested that (1) the COVID-19 epidemic at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks, and had an extremely significant association with social and economic factors. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers.

Dataset Information

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei.

Publications

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei.

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