Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan

Cui, Qianqian; Z, Shi; Yimamaidi, Duman; Hu, Ben; Zhang, Zhuo; Saqib, Muhammad; Zohaib, Ali; Baikadamova, Gulnara; Yersyn, Mukhanbetkaliyev; Hu, Zengyun; Li, Shi-Zhu

doi:10.1186/s40249-023-01072-5

Cited by 5 publications

(11 citation statements)

References 49 publications

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“…It is important to note that the TDINN algorithm overcomes some disadvantages of traditional transmission dynamic models for simulating the development process of the COVID-19 epidemic. For example, in the classic compartment model, the contact rate and quarantine rate are usually assumed to be constant or particular time-dependent functions, respectively, to describe the intensities of control interventions [ 10 , 12 ]. That is, to simulate outbreaks in different regions, we need to pre-set various particular parameter values and/or time-dependent functions to quantify the continuously adjusted control measures in different regions, which significantly limits the performance of the transmission dynamic models.…”

Section: Discussionmentioning

confidence: 99%

“…In traditional mechanism-based models, researchers usually incorporated constant contact rate and quarantine/isolation rate for simplicity to analyze the transmission risk [ 6 ], model the impact of contact tracing and quarantine on the development of COVID-19 [ 7 , 8 ] and evaluate the independent effectiveness of vaccines [ 9 ]. There are also a large number of literatures in which the specific functions were supposed to represent the dynamic changes in intensity of interventions for comparing the effectiveness of various control strategies [ 10 ], understanding the drivers of multiple waves of outbreaks [ 11 ] and exploring the transmission mechanism of COVID-19 with different intervention patterns [ 12 ]. Moreover, Wang et al [ 13 ] considered a dynamic epidemiological model with a piecewise contact rate and quarantine rate to simulate the dynamics of the Omicron variant in Shanghai, and explored the feasibility of different control patterns in avoiding subsequent waves.…”

Section: Introductionmentioning

confidence: 99%

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Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

He,

Tang,

Xiao

2023

PLoS Comput Biol

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During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi’an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

He,

Tang,

Xiao

2023

PLoS Comput Biol

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“…where NAE and NRMSE are normalized by the averaged values of the observed time series ( 33 ). In the model, a smaller disco value indicates better overall performance, and vice versa.…”

Section: Methodsmentioning

confidence: 99%

“…This new index DISO is a merge of different statistical metrics including correlation coefficient (CC), absolute error (AE), and root mean square error (RMSE) according to the distance between the simulated model and observed field in a three-dimension space coordinate system. This method is used to quantitatively evaluate the comprehensive accuracy of joinpoint regression model, which is based on the Euclidean distance and flexible determination of statistical metrics and their numbers from the Da Dao Zhi Jian concept (30)(31)(32)(33). The formula is as follows:…”

Section: Joinpoint Regressionmentioning

confidence: 99%

Scaling law characteristics and spatiotemporal multicomponent analysis of syphilis from 2016 to 2022 in Zhejiang Province, China

Wu,

Xue,

et al. 2023

Front. Public Health

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BackgroundSyphilis has caused epidemics for hundreds of years, and the global syphilis situation remains serious. The reported incidence rate of syphilis in Zhejiang Province has ranked first in the province in terms of notifiable infectious diseases for many years and is the highest in China. This study attempts to use the scaling law theory to study the relationship between population size and different types of syphilis epidemics, while also exploring the main driving factors affecting the incidence of syphilis in different regions.MethodsData on syphilis cases and affected populations at the county level were obtained from the China Disease Control and Prevention Information System. The scaling relationship between different stages of syphilis and population size was explained by scaling law. The trend of the incidence from 2016 to 2022 was tested by the joinpoint regression. The index of distance between indices of simulation and observation (DISO) was applied to evaluate the overall performance of joinpoint regression model. Furthermore, a multivariate time series model was employed to identify the main driving components that affected the occurrence of syphilis at the county level. The p value less than 0.05 or confidence interval (CI) does not include 0 represented statistical significance for all the tests.ResultsFrom 2016 to 2022, a total of 204,719 cases of syphilis were reported in Zhejiang Province, including 2 deaths, all of which were congenital syphilis. Latent syphilis accounted for 79.47% of total syphilis cases. The annual percent change (APCs) of all types of syphilis, including primary syphilis, secondary syphilis, tertiary syphilis, congenital syphilis and latent syphilis, were − 21.70% (p < 0.001, 95% CI: −26.70 to −16.30), −16.80% (p < 0.001, 95% CI: −20.30 to −13.30), −8.70% (p < 0.001, 95% CI: −11.30 to −6.00), −39.00% (p = 0.001, 95% CI: −49.30 to −26.60) and − 7.10% (p = 0.008, 95% CI: −11.20 to −2.80), respectively. The combined scaling exponents of primary syphilis, secondary syphilis, tertiary syphilis, congenital syphilis and latent syphilis based on the random effects model were 0.95 (95% CI: 0.88 to 1.01), 1.14 (95% CI: 1.12 to 1.16), 0.43 (95% CI: 0.37 to 0.49), 0.0264 (95% CI: −0.0047 to 0.0575) and 0.88 (95% CI: 0.82 to 0.93), respectively. The overall average effect values of the endemic component, spatiotemporal component and autoregressive component for all counties were 0.24, 0.035 and 0.72, respectively. The values of the autoregressive component for most counties were greater than 0.7. The endemic component of the top 10 counties with the highest values was greater than 0.34. Two counties with value of the spatiotemporal component higher than 0.1 were Xihu landscape county and Shengsi county. From 2016 to 2022, the endemic and autoregressive components of each county showed obvious seasonal changes.ConclusionThe scaling exponent had both temporal trend characteristics and significant heterogeneity in the association between each type of syphilis and population size. Primary syphilis and latent syphilis exhibited a linear pattern, secondary syphilis presented a superlinear pattern, and tertiary syphilis exhibited a sublinear pattern. This suggested that further prevention of infection and transmission among high-risk populations and improvement of diagnostic accuracy in underdeveloped areas is needed. The autoregressive components and the endemic components were the main driving factors that affected the occurrence of syphilis. Targeted prevention and control strategies must be developed based on the main driving modes of the epidemic in each county.

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“…A study conducted in the UK utilized the SEIR-D model to forecast the number of infections in local areas, gauge healthcare requirements, and anticipate needs and isolation capacity within regional hospitals [ 14 ]. Cui et al [ 15 ] employed an improved SEIR model to accurately simulate and predict the transmission dynamics of COVID-19 in two low-income countries, namely Kazakhstan and Pakistan. Their findings offer valuable insights, serving as a reference for low-income countries in formulating effective prevention and control strategies.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study

Wang,

Zhang,

et al. 2024

Infect Dis Ther

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Introduction The 2019 novel coronavirus (COVID-19) has been recognized as the most severe human infectious disease pandemic in the past century. To enhance our ability to control potential infectious diseases in the future, this study simulated the influence of nucleic acid testing on the transmission of COVID-19 across varied scenarios. Additionally, it assessed the demand for nucleic acid testing under different circumstances, aiming to furnish a decision-making foundation for the implementation of nucleic acid screening measures, the provision of emergency materials, and the allocation of human resources. Methods Considering the transmission dynamics of COVID-19 and the preventive measures implemented by countries, we explored three distinct levels of epidemic intensity: community transmission, outbreak, and sporadic cases. Integrating the theory of scenario analysis, we formulated six hypothetical epidemic scenarios, each corresponding to possible occurrences during different phases of the pandemic. We developed an improved SEIR model, validated its accuracy using real-world data, and conducted a comprehensive analysis and prediction of COVID-19 infections under these six scenarios. Simultaneously, we assessed the testing resource requirements associated with each scenario. Results We compared the predicted number of infections simulated by the modified SEIR model with the actual reported cases in Israel to validate the model. The root mean square error (RMSE) was 350.09, and the R -squared ( R 2 ) was 0.99, indicating a well-fitted model. Scenario 4 demonstrated the most effective prevention and control outcomes. Strengthening non-pharmaceutical interventions and increasing nucleic acid testing frequency, even under low testing capacity, resulted in a delayed epidemic peak by 78 days. The proportion of undetected cases decreased from 77.83% to 31.21%, and the overall testing demand significantly decreased, meeting maximum demand even with low testing capacity. The initiation of testing influenced case detection probability. Under high testing capacity, increasing testing frequency elevated the detection rate from 36.40% to 77.83%. Nucleic acid screening proved effective in reducing the demand for testing resources under diverse epidemic prevention and control strategies. While effective interventions and nucleic acid screening measures substantially diminished the demand for testing-related resources, varying degrees of insufficient testing capacity may still persist. Conclusions The nucleic acid detection strategy proves effective in promptly identifying and isolating infected individuals, thereby mitigating the infection peak and extending the time to peak. In situations with constrained testing capacity, implementing more stringent measures can notably decrease the number of infections and alleviate resource demands. The improved SEIR...

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Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan

Cited by 5 publications

References 49 publications

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

Scaling law characteristics and spatiotemporal multicomponent analysis of syphilis from 2016 to 2022 in Zhejiang Province, China

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study

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