Retrospective prediction of the epidemic trend of COVID‐19 in Wuhan at four phases

Li, Mengyuan; Guo, Xiaonan; Wang, Qianqian

doi:10.1002/jmv.26781

Cited by 5 publications

(8 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…COVID-19 has affected ~199 M individuals, resulting in ~4.24 M deaths worldwide as of August 3, 2021. The scientific community has made tireless efforts to find a suitable remedy for COVID-19 since it was first reported in China's Wuhan Province (Yamamoto et al, 2020;Li et al, 2021;Mei et al, 2021;Yang et al, 2021;Zhu et al, 2021). Of most concern at present is the arrival of new, mutated, genetic variants (e.g.…”

Section: Introductionmentioning

confidence: 99%

Niclosamide-Exfoliated Anionic Clay Nanohybrid Repurposed as an Antiviral Drug for Tackling Covid-19; Oral Formulation with Tween 60/Eudragit S100

Rejinold

Piao

Choi

et al. 2021

Clays and clay miner.

View full text Add to dashboard Cite

The ongoing pandemic, COVID-19 (SARS-CoV-2), has afflicted millions of people around the world, necessitating that the scientific community work, diligently and promptly, on suitable medicaments. Although vaccination programs have been run globally, the new variants of COVID-19 make it difficult to restrict the spread of the virus by vaccination alone. The combination of vaccination with anti-viral drug formulation is an ideal strategy for tackling the current pandemic situation. Drugs approved by the United States Food and Drug Administration (FDA), such as Remdesivir, have been found to be of little or no benefit. On the other hand, re-purposing of FDA-approved drugs, such as niclosamide (NIC), has offered promise but its applicability is limited due to its poor aqueous solubility and, therefore, low bioavailability. With advanced nano-pharmaceutical approaches, re-purposing this drug in a suitable drug-carrier for a better outcome may be possible. In the current study, an attempt was made to explore the loading of NIC into exfoliated layered double hydroxide nanoparticles (X-LDH NPs); prepared NIC-X-LDH NPs were further modified with eudragit S100 (ES100), an enteric coating polymer, to make the final product, ES100-NIC-X-LDH NPs, to improve absorption by the gastro/intestinal tract (GIT). Furthermore, Tween 60 was added as a coating on ES100-NIC-X-LDH NPs, not just to enhance its in vitro and in vivo stability, but also to enhance its mucoadhesive property, and to obtain, ultimately, better in vivo pharmacokinetic (PK) parameters upon oral administration. Release of NIC from Tween 60-ES100-NIC-X-LDH NPs was found to be greater under gastro/intestinal solution within a shorter period of time than the uncoated samples. The in vivo analysis revealed that Tween 60-ES100-NIC-X-LDH NPs were able to maintain a therapeutically relevant NIC plasma concentration in terms of PK parameters compared to the commercially available Yomesan®, proving that the new formulation might prove to be an effective oral drug-delivery system to deal with the SARS-CoV-2 viral infections. Further studies are required to ensure their safety and anti-viral efficacy.

show abstract

Section: Introductionmentioning

confidence: 99%

Niclosamide-Exfoliated Anionic Clay Nanohybrid Repurposed as an Antiviral Drug for Tackling Covid-19; Oral Formulation with Tween 60/Eudragit S100

Rejinold

Piao

Choi

et al. 2021

Clays and clay miner.

View full text Add to dashboard Cite

show abstract

“…A comprehensible method for defining pandemic stages is rarely found in the literature on COVID-19. Existing phase models (Ghosh & Cartone, 2020; Benita & Gasca-Sanchez 2021; Li et al 2021; Zawbaa et al 2022) define the beginning of each stage relatively arbitrarily based on individual indicators, such as incidence rates, mortality rates, or the implementation of countermeasures like lockdowns and social distancing. Typical types of phases include ‘beginning’, ‘outbreak’, ‘recession’, and ‘plateau’ (Li et al 2021).…”

Section: Methodsmentioning

confidence: 99%

“…Existing phase models (Ghosh & Cartone, 2020;Benita & Gasca-Sanchez 2021;Li et al 2021;Zawbaa et al 2022) define the beginning of each stage relatively arbitrarily based on individual indicators, such as incidence rates, mortality rates, or the implementation of countermeasures like lockdowns and social distancing. Typical types of phases include 'beginning', 'outbreak', 'recession', and 'plateau' (Li et al 2021). Schilling et al (2022) used a multivariant approach by combining several variables for their phase model for Germany, although their method of delineating phases remains unclear.…”

Section: Identifying Change Points Of Pandemic Severitymentioning

confidence: 99%

Waves in time, but not in space – An analysis of pandemic severity of COVID-19 in Germany based on spatio-temporal clustering

Kuebart

Stabler

2023

Preprint

View full text Add to dashboard Cite

While pandemic waves are often studied on the national scale, they typically are not distributed evenly within countries. This paper employs a novel approach to analyze the tempo-spatial dynamics of the COVID-19 pandemic in Germany. First, we base the analysis on a composite indicator of pandemic severity to gain a more robust understanding of the temporal dynamics of the pandemic. Second, we subdivide the pandemic during the years 2020 and 2021 into fifteen phases, each with a coherent trend of pandemic severity. Third, we analyze the patterns of spatial association during each phase. Fourth, similar types of trajectories of pandemic severity among all German counties were identified through hierarchical clustering. The results imply that the hotspots and cold spots of the first four waves of the pandemic were relatively stationary in space so that the pandemic moved in time but less in space.

show abstract

“…Since the outbreak of COVID-19, many scholars have used statistical methods [1][2][3][4][5], mechanism models [6][7][8][9][10] and deep learning methods [11][12][13][14][15][16][17][18] to predict the COVID-19 epidemic trend. Although the pure deep learning method has higher accuracy when compared with mechanism model in predicting the development trend of the epidemic, it cannot reflect the specific influencing factors of the epidemic trend like mechanism model and is less instructive for further taking corresponding prevention and control measures.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, our focus of interest was put to the functionalization of transmission rate β. A few of researchers have redefined the population classification based on the SIR model in order to predict the epidemic trend of COVID-19 more accurately [6][7][8][9][10]. These methods, however, ignored the time-varying characteristics of the transmission rate β so that they were not capable to improve the prediction accuracy of COVID-19 effectively.…”

Section: Introductionmentioning

confidence: 99%

Nesting the SIRV model with NAR, LSTM and statistical methods to fit and predict COVID-19 epidemic trend in Africa

Wang

Yang

et al. 2023

BMC Public Health

View full text Add to dashboard Cite

Objective Compared with other regions in the world, the transmission characteristics of the COVID-19 epidemic in Africa are more obvious, has a unique transmission mode in this region; At the same time, the data related to the COVID-19 epidemic in Africa is characterized by low data quality and incomplete data coverage, which makes the prediction method of COVID-19 epidemic suitable for other regions unable to achieve good results in Africa. In order to solve the above problems, this paper proposes a prediction method that nests the in-depth learning method in the mechanism model. From the experimental results, it can better solve the above problems and better adapt to the transmission characteristics of the COVID-19 epidemic in African countries. Methods Based on the SIRV model, the COVID-19 transmission rate and trend from September 2021 to January 2022 of the top 15 African countries (South Africa, Morocco, Tunisia, Libya, Egypt, Ethiopia, Kenya, Zambia, Algeria, Botswana, Nigeria, Zimbabwe, Mozambique, Uganda, and Ghana) in the accumulative number of COVID-19 confirmed cases was fitted by using the data from Worldometer. Non-autoregressive (NAR), Long-short term memory (LSTM), Autoregressive integrated moving average (ARIMA) models, Gaussian and polynomial functions were used to predict the transmission rate β in the next 7, 14, and 21 days. Then, the predicted transmission rate βs were substituted into the SIRV model to predict the number of the COVID-19 active cases. The error analysis was conducted using root-mean-square error (RMSE) and mean absolute percentage error (MAPE). Results The fitting curves of the 7, 14, and 21 days were consistent with and higher than the original curves of daily active cases (DAC). The MAPE between the fitted and original 7-day DAC was only 1.15% and increased with the longer of predict days. Both the predicted β and DAC of the next 7, 14, and 21 days by NAR and LSTM nested models were closer to the real ones than other three ones. The minimum RMSEs for the predicted number of COVID-19 active cases in the next 7, 14, and 21 days were 12,974, 14,152, and 12,211 people, respectively when the order of magnitude for was 106, with the minimum MAPE being 1.79%, 1.97%, and 1.64%, respectively. Conclusion Nesting the SIRV model with NAR, LSTM, ARIMA methods etc. through functionalizing β respectively could obtain more accurate fitting and predicting results than these models/methods alone for the number of confirmed COVID-19 cases in Africa in which nesting with NAR had the highest accuracy for the 14-day and 21-day predictions. The nested model was of high significance for early understanding of the COVID-19 disease burden and preparedness for the response.

show abstract

Retrospective prediction of the epidemic trend of COVID‐19 in Wuhan at four phases

Cited by 5 publications

References 11 publications

Niclosamide-Exfoliated Anionic Clay Nanohybrid Repurposed as an Antiviral Drug for Tackling Covid-19; Oral Formulation with Tween 60/Eudragit S100

Niclosamide-Exfoliated Anionic Clay Nanohybrid Repurposed as an Antiviral Drug for Tackling Covid-19; Oral Formulation with Tween 60/Eudragit S100

Waves in time, but not in space – An analysis of pandemic severity of COVID-19 in Germany based on spatio-temporal clustering

Nesting the SIRV model with NAR, LSTM and statistical methods to fit and predict COVID-19 epidemic trend in Africa

Contact Info

Product

Resources

About