Dataset Information

Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak.

ABSTRACT:

Background

Various models have been applied to predict the trend of the epidemic since the outbreak of COVID-19.

Methods

In this study, we designed a dynamic graph model, not for precisely predicting the number of infected cases, but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors.

Results

We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to halt virus spread within a city. We further illustrated that sudden emergence of a large number of cases could overwhelm the city medical system, and external medical aids are critical to not only containing the further spread of the virus but also reducing fatality.

Conclusion

Our model simulation showed that highly populated modern cities are particularly vulnerable and lessons learned in China could facilitate other countries to plan the proactive and decisive actions. We shall pay close attention to the asymptomatic transmission being suggested by rapidly accumulating evidence as dramatic changes in quarantine protocol are required to contain SARS-CoV-2 from spreading globally.

Supplementary materials

The supplementary materials can be found online with this article at 10.1007/s40484-020-0215-4.

SUBMITTER: Yang X

PROVIDER: S-EPMC7479740 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak.

Yang Xiaofei X Xu Tun T Jia Peng P Xia Han H Guo Li L Zhang Lei L Ye Kai K

Quantitative biology (Beijing, China) 20200909 3

<h4>Background</h4>Various models have been applied to predict the trend of the epidemic since the outbreak of COVID-19.<h4>Methods</h4>In this study, we designed a dynamic graph model, not for precisely predicting the number of infected cases, but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors.<h4>Results</h4>We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to ha ...[more]

PMID: 32923014

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Project description:BackgroundThe emergence of COVID-19 as a global pandemic presents a serious health threat to African countries and the livelihoods of its people. To mitigate the impact of this disease, intervention measures including self-isolation, schools and border closures were implemented to varying degrees of success. Moreover, there are a limited number of empirical studies on the effectiveness of non-pharmaceutical interventions (NPIs) to control COVID-19. In this study, we considered two models to inform policy decisions about pandemic planning and the implementation of NPIs based on case-death-recovery counts.MethodsWe applied an extended susceptible-infected-removed (eSIR) model, incorporating quarantine, antibody and vaccination compartments, to time series data in order to assess the transmission dynamics of COVID-19. Additionally, we adopted the susceptible-exposed-infectious-recovered (SEIR) model to investigate the robustness of the eSIR model based on case-death-recovery counts and the reproductive number (R0). The prediction accuracy was assessed using the root mean square error and mean absolute error. Moreover, parameter sensitivity analysis was performed by fixing initial parameters in the SEIR model and then estimating R0, β and γ.ResultsWe observed an exponential trend of the number of active cases of COVID-19 since March 02 2020, with the pandemic peak occurring around August 2021. The estimated mean R0 values ranged from 1.32 (95% CI, 1.17-1.49) in Rwanda to 8.52 (95% CI: 3.73-14.10) in Kenya. The predicted case counts by January 16/2022 in Burundi, Ethiopia, Kenya, Rwanda, South Sudan, Tanzania and Uganda were 115,505; 7,072,584; 18,248,566; 410,599; 386,020; 107,265, and 3,145,602 respectively. We show that the low apparent morbidity and mortality observed in EACs, is likely biased by underestimation of the infected and mortality cases.ConclusionThe current NPIs can delay the pandemic pea and effectively reduce further spread of COVID-19 and should therefore be strengthened. The observed reduction in R0 is consistent with the interventions implemented in EACs, in particular, lockdowns and roll-out of vaccination programmes. Future work should account for the negative impact of the interventions on the economy and food systems.

Project description:BackgroundIn April 2022, an outbreak of the SARS-CoV-2 virus Omicron variant in Shanghai precipitated an extensive lockdown. We assessed changes in healthcare utilization during this outbreak and investigated the relationship between the stringency of mitigation strategies and disruptions in healthcare utilization.MethodsUsing provincial-level data from routine health information systems covering all hospitals across Mainland China, we conducted an interrupted time series analysis to examine changes in healthcare utilization during the Shanghai outbreak. Linear regression was used to evaluate the direction and magnitude of the association between the relative changes in the move-out movement index, a proxy for the stringency in population-level mitigation strategies, and the estimated relative changes in healthcare utilization.ResultsOverall, there were 22.9 billion outpatient visits and 1.2 billion discharged inpatients during the study period from January 2016 to May 2022, including 9.1 billion (39.7%) and 0.46 billion (38.2%) in the post-COVID-19 period (January 2020-May 2022), respectively. From March through May 2022, the outbreak resulted in an accumulative loss of 23.5 million (47%) outpatient visits and 0.6 million (55%) discharged inpatients in Shanghai, and a loss of 150.3 million (14%) outpatient visits and 3.6 million (7%) discharged inpatients in other regions. We find that for every 10-percentage point reduction in the relative change of move-out index, a 2.7 (95% CI: 2.0-3.4) percentage point decline in the relative change of outpatient visits, and a 4.3 (95% CI: 3.5-5.2) percentage points decline in the relative change of inpatient discharges.ConclusionsThe Shanghai COVID-19 Omicron outbreak associates with a substantial reduction in outpatient visits and inpatient discharges within Shanghai and other regions in China. The stringency of the COVID-19 lockdown policies associates with more profound reductions in healthcare utilization.

Dataset Information

Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak.

Background

Methods

Results

Conclusion

Supplementary materials

Publications

Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak.

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