Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020

Roosa, Kimberlyn; Lee, Yiseul; Luo, Ruiyan; Kirpich, Alexander; Rothenberg, Richard; Hyman, James M.; Yan, Ping; Chowell, Gerardo

doi:10.3390/jcm9020596

Cited by 217 publications

(206 citation statements)

References 17 publications

(24 reference statements)

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“…It is used in microbiological research, diagnostics and in the production of antiviral vaccines-all situations that require knowledge of the amount of virus being analyzed or used. We used tissue-culture infectious dose (TCID) per milliliter for COVID-19 persistence evaluation [15][16][17][18][19].…”

Section: Rationalementioning

confidence: 99%

COVID-19 Surface Persistence: A Recent Data Summary and Its Importance for Medical and Dental Settings

Fiorillo

Cervino

Matarese

et al. 2020

IJERPH

150

149

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Recently, due to the coronavirus pandemic, many guidelines and anti-contagion strategies continue to report unclear information about the persistence of coronavirus disease 2019 in the environment. This certainly generates insecurity and fear in people, with an important psychological component that is not to be underestimated at this stage of the pandemic. The purpose of this article is to highlight all the sources currently present in the literature concerning the persistence of the different coronaviruses in the environment as well as in medical and dental settings. As this was a current study, there are still not many sources in the literature, and scientific strategies are moving towards therapy and diagnosis, rather than knowing the characteristics of the virus. Such an article could be an aid to summarize virus features and formulate new guidelines and anti-spread strategies.

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Section: Rationalementioning

confidence: 99%

COVID-19 Surface Persistence: A Recent Data Summary and Its Importance for Medical and Dental Settings

Fiorillo

Cervino

Matarese

et al. 2020

IJERPH

150

149

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“…If we determine the peak of the outbreak from the 5 days moving average of the incidence curve, then there are 15 provinces taking 7-11 days from their first case to their peak, 9 provinces taking 12-15 days, and 6 provinces taking more than 15 days. If we define the end of the outbreak as the day when the 5 days moving average of the growth rate becomes smaller than 1%, then 7 provinces spent 8-12 days from the peak to the end, 7 provinces spent 13-16 days, 13 provinces spent 17-20 days, and 2 provinces spent 21-22 days. For the six provinces that have the longest duration from the start of their outbreak to the peak (more than 15 days), it took 8-13…”

Section: Quantification Of the Initial Reactions And Ramping Up Of Comentioning

confidence: 99%

Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world

Darcet

Wang

et al. 2020

Preprint

102

131

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Background: the COVID-19 has been successfully contained in China but is spreading all over the world. We use phenomenological models to dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province.We use the experience from China to analyze the calibration results on Japan, South Korea, Iran, Italy and Europe, and make future scenario projections. Methods: we calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries and Europe as a whole. The different models provide upper and lower bounds of our scenario predictions. Results:We quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogenous situations across provinces. Based on Chinese experience, we identify a high risk in Japan with estimated total confirmed cases as of March 25 being 1574 (95% CI: [880, 2372]), and 5669 (95% CI: [988, 11340]) by June. For South Korea, we expect the number of infected cases to approach the ceiling, 7928 (95% CI: [6341, 9754]), in 20 days. We estimate 0.15% (95% CI: [0.03%, 0.30%]) of Italian population to be infected in a positive scenario. We would expect 114867 people infected in Europe in 10 days, in a negative but probable scenario, corresponding to 0.015% European population. Conclusions:The extreme containment measures implemented by China were very effective with some instructive variations across provinces. For other countries, it is almost inevitable to see the continuation of the outbreak in the coming months. Japan and Italy are in serious situations with no short-term end to the outbreak to be expected. There is a significant risk concerning the upcoming July 2020 Summer Olympics in Tokyo. Iran's situation is highly uncertain with unclear and negative future scenarios, while South Korea is approaching the end of the outbreak. Both Europe and the USA are at early stages of the outbreak, posing significant health and economic risks to the world in absence of serious measures.

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“…Also the authors determined the number of cases with the epidemic growth model Fan et al [19]. Roosa et al (2020) analyzed the number of cases in some regions in China with generalized logistic growth model (GLM), Richards Model and Sub-Epidemic Model in a short-term (10 days). They found that the number of cases will increase.…”

Section: Discussionmentioning

confidence: 99%

Modelling of Covid-19 Outbreak Indicators in China Between January and April

Çelik

Ankaralı

Pasin

2020

Preprint

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Background:The aim of this study is to explain the changes of outbreak indicators for coronavirus in China with nonlinear models and time series analysis. There are lots of methods for modelling. But we want to determine the best mathematical model and the best time series method among other models. Methods:The data was obtained between January 22 and April 21, 2020 from China records. The number of total cases and the number of total deaths were used for the calculations. For modelling Weibull, Negative Exponential, Von Bertalanffy, Janoscheck, Lundqvist-Korf and Sloboda models were used and AR, MA, ARMA, Holt, Brown and Damped models were used for time series. The determination coefficient (R 2 ), Pseudo R 2 and mean square error were used for nonlinear modelling as criteria for determining the model that best describes the number of cases, the number of total deaths and BIC (Bayesian Information Criteria) was used for time series.Results: According to our results, the Sloboda model among the growth curves and ARIMA (0,2,1) model among the times series models were most suitable models for modelling of the number of total cases. In addition Lundqvist-Korf model among the growth curves and Holt linear trend exponential smoothing model among the times series models were most suitable model for modelling of the number of total deaths. Our time series models forecast that the number of total cases will 83311 on 5 May and the number of total deaths will be 5273. Conclusions:Because results of the modelling has providing information on measures to be taken and giving prior information for subsequent similar situations, it is of great importance modeling outbreak indicators for each country separately.

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Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020

Cited by 217 publications

References 17 publications

COVID-19 Surface Persistence: A Recent Data Summary and Its Importance for Medical and Dental Settings

COVID-19 Surface Persistence: A Recent Data Summary and Its Importance for Medical and Dental Settings

Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world

Modelling of Covid-19 Outbreak Indicators in China Between January and April

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