scholarly journals Evaluation of China's Hubei Control Strategy for COVID-19 Epidemic: An Observational Study

2020 ◽  
Author(s):  
Yu Liu ◽  
Fangfang Zheng ◽  
Zhicheng Du ◽  
Jinghua Li ◽  
Jing Gu ◽  
...  
Keyword(s):  
Public Health ◽  
Control Strategy ◽  
Case Reports ◽  
Human Mobility ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Opposite Pattern ◽  
Effective Response ◽  
Response Timing ◽  
Importation Risk

Abstract Background: To fight against COVID-19, many policymakers are wavering on stricter public health interventions. However, relying on these measures but different strategies, both in and out of China’s Hubei province basically contained the epidemic in late February 2020. This study aimed to assess the response process and estimate time-varying effect of Hubei control strategy to provide insights for intervention design and implementation.Methods: We retrospectively compared the spread and control of COVID-19 between China’s Hubei (excluding Wuhan) and non-Hubei areas using data that includes case reports, human mobility, and public health interventions from 1 January to 29 February, 2020. The static and dynamic risk assessment models were developed to statistically investigate the effect trends of Hubei control strategy on case growth after adjusting importation risk and response timing with non-Hubei strategy as a contrast.Results: The analysis detected much higher but differential importation risk in Hubei. The response timing largely coincided with the importation risk in non-Hubei areas, but Hubei areas showed an opposite pattern. A careful and comprehensive comparison showed that Hubei control strategy implemented interventions characterized by unprecedentedly strict and ‘monitored’ self-quarantine at home, while non-Hubei strategy included physical distancing measures to reduce contact among individuals within or between populations. In contrast with non-Hubei control strategy, Hubei strategy showed a much higher, non-linear and gradually diminishing protective effect with at least 3 times fewer cases.Conclusions: A risk-based control strategy is crucial to design an effective response for COVID-19 control. Our study demonstrates that the stricter Hubei strategy can achieve much better control effectiveness. These findings highlight the health benefits of precise and differentiated strategies informed by constant monitoring of outbreak risk and policy impacts.

scholarly journals Evaluation of China’s Hubei control strategy for COVID-19 epidemic: an observational study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yu Liu ◽  
Fangfang Zheng ◽  
Zhicheng Du ◽  
Jinghua Li ◽  
Jing Gu ◽  
...  
Keyword(s):  
Public Health ◽  
Control Strategy ◽  
Case Reports ◽  
Human Mobility ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Opposite Pattern ◽  
Effective Response ◽  
Response Timing ◽  
Importation Risk

Abstract Background To fight against COVID-19, many policymakers are wavering on stricter public health interventions. Examining the different strategies both in and out of China’s Hubei province, which contained the epidemic in late February 2020, could yield valuable guidance for the management of future pandemics. This study assessed the response process and estimated the time-varying effects of the Hubei control strategy. Analysis of these strategies provides insights for the design and implementation of future policy interventions. Methods We retrospectively compared the spread and control of COVID-19 between China’s Hubei (excluding Wuhan) and non-Hubei areas using data that includes case reports, human mobility, and public health interventions from 1 January to 29 February 2020. Static and dynamic risk assessment models were developed to statistically investigate the effects of the Hubei control strategy on the virus case growth after adjusting importation risk and policy response timing with the non-Hubei strategy as a control. Results The analysis detected much higher but differential importation risk in Hubei. The response timing largely coincided with the importation risk in non-Hubei areas, but Hubei areas showed an opposite pattern. Rather than a specific intervention assessment, a comprehensive comparison showed that the Hubei control strategy implemented severe interventions characterized by unprecedentedly strict and ‘monitored’ self-quarantine at home, while the non-Hubei strategy included physical distancing measures to reduce contact among individuals within or between populations. In contrast with the non-Hubei control strategy, the Hubei strategy showed a much higher, non-linear and gradually diminishing protective effect with at least 3 times fewer cases. Conclusions A risk-based control strategy was crucial to the design of an effective response to the COVID-19 outbreak. Our study demonstrates that the stricter Hubei strategy achieves a stronger controlling effect compared to other strategies. These findings highlight the health benefits and policy impacts of precise and differentiated strategies informed by constant monitoring of outbreak risk.

scholarly journals State heterogeneity of human mobility and COVID-19 epidemics in the European Union

2020 ◽  
Author(s):  
Xiaoling Yuan ◽  
Kun Hu ◽  
Jie Xu ◽  
Xuchen Zhang ◽  
Wei Bao ◽  
...  
Keyword(s):  
Public Health ◽  
European Union ◽  
Human Mobility ◽  
Turning Points ◽  
Temporal Trends ◽  
The State ◽  
Health Interventions ◽  
The European Union ◽  
Public Health Interventions ◽  
The Eu

SummaryHuman mobility was associated with epidemic changes of coronavirus disease 2019 (COVID-19) in China, where strict public health interventions reduced human mobility and COVID-19 epidemics. But its association with COVID-19 epidemics in the European Union (EU) is unclear. In this quasi-experimental study, we modelled the temporal trends in human mobility and epidemics of COVID-19 in the 27 EU states between January 15 and May 9, 2020. COVID-19 and human mobility had 3 trend-segments, including an upward trend in COVID-19 daily incidence and a downward trend in most human mobilities in the middle segment. Compared with the EU states farther from Italy, the state-wide lockdown dates were more likely linked to turning points of human mobilities in the EU states closer to Italy, which were also more likely linked to second turning points of COVID-19 epidemics. Among the examined human mobilities, the second turning points in driving mobility and the first turning points in parks mobility were the best factors that connected lockdown dates and COVID-19 epidemics in the EU states closer to Italy. Our findings highlight the state- and mobility-heterogeneity in the associations of public health interventions and human mobility with changes of COVID-19 epidemics in the EU states.

scholarly journals An analysis of COVID-19 clusters in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Sengupta ◽  
Bhaswati Ganguli ◽  
Sugata SenRoy ◽  
Aditya Chatterjee
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Population Density ◽  
Case Studies ◽  
Compartment Model ◽  
Health Interventions ◽  
Disease Spread ◽  
Second Phase ◽  
Public Health Interventions ◽  
Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

scholarly journals Impact of reduction of susceptibility to SARS-CoV-2 on epidemic dynamics in four early-seeded metropolitan regions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thomas J. Barrett ◽  
Karen C. Patterson ◽  
Timothy M. James ◽  
Peter Krüger
Keyword(s):  
Public Health ◽  
Growth Phase ◽  
Chronic Phase ◽  
Health Interventions ◽  
Resource Utilisation ◽  
Policy Planning ◽  
Infection Rates ◽  
Public Health Interventions ◽  
Metropolitan Regions ◽  
Epidemic Dynamics

AbstractAs we enter a chronic phase of the SARS-CoV-2 pandemic, with uncontrolled infection rates in many places, relative regional susceptibilities are a critical unknown for policy planning. Tests for SARS-CoV-2 infection or antibodies are indicative but unreliable measures of exposure. Here instead, for four highly-affected countries, we determine population susceptibilities by directly comparing country-wide observed epidemic dynamics data with that of their main metropolitan regions. We find significant susceptibility reductions in the metropolitan regions as a result of earlier seeding, with a relatively longer phase of exponential growth before the introduction of public health interventions. During the post-growth phase, the lower susceptibility of these regions contributed to the decline in cases, independent of intervention effects. Forward projections indicate that non-metropolitan regions will be more affected during recurrent epidemic waves compared with the initially heavier-hit metropolitan regions. Our findings have consequences for disease forecasts and resource utilisation.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

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