The Prediction for Development of COVID-19 in Global Major Epidemic Areas Through Empirical Trends in China by Utilizing State Transition Matrix Model

Zheng, Zhong; Wu, Ke; Yao, Zhixian; Zheng, Xinyi; Zheng, Junhua; Chen, Jian

doi:10.21203/rs.3.rs-20001/v2

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…The authors of Zheng et al 29 presented the state transition matrix model for datewise predictions with data predicted for short durations. The authors of Tuli et al 30 proposed a wide‐range Covid‐19 data to be predicted across various countries with the help of machine learning.…”

Section: Related Workmentioning

confidence: 99%

An approach to forecast impact of Covid‐19 using supervised machine learning model

et al. 2021

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The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

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Section: Related Workmentioning

confidence: 99%

An approach to forecast impact of Covid‐19 using supervised machine learning model

et al. 2021

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“…Authors in Ref. [127] adopt a state transfer matrix model, which similarly to compartmental models, to describe the different states of individuals that got in contact with the virus. Hence, this model could be classified as a compartmental model.…”

Section: Statistical Modelsmentioning

confidence: 99%

Non-pharmaceutical interventions during the COVID-19 pandemic: a rapid review

Perra

2020

Preprint

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Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travels bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I aim to review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 347 articles written by more than 2518 of authors in the last 12 months. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities *

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“…Batista 8 predicted the pandemic should peak around Feb 9 th , 2020, but it shows no sign of slowing down into late-March, 2020. Zheng et al 9 predicted about 20,000 cases in South Korea, which is unlikely to happen given its current flat trend around 9,000. Models used in these forecasting are mainly the susceptible-infected-removed (SIR) models and its variants 5-8 .…”

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confidence: 95%

“…Models used in these forecasting are mainly the susceptible-infected-removed (SIR) models and its variants 5-8 . Others include state transition model 9 , parametric growth curve models such as logistic curves 10 , and auto regressive integrated moving average (ARIMA) models 11 .…”

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confidence: 99%

Government Responses Matter: Predicting Covid-19 cases in US using an empirical Bayesian time series framework

Liu

Guo

2020

Preprint

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Since the Covid-19 outbreak, researchers have been predicting how the epidemic will evolve, especially the number in each country, through using parametric extrapolations based on the history. In reality, the epidemic progressing in a particular country depends largely on its policy responses and interventions. Since the outbreaks in some countries are earlier than United States, the prediction of US cases can benefit from incorporating the similarity in their trajectories. We propose an empirical Bayesian time series framework to predict US cases using different countries as prior reference.

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The Prediction for Development of COVID-19 in Global Major Epidemic Areas Through Empirical Trends in China by Utilizing State Transition Matrix Model

Cited by 5 publications

References 9 publications

An approach to forecast impact of Covid‐19 using supervised machine learning model

An approach to forecast impact of Covid‐19 using supervised machine learning model

Non-pharmaceutical interventions during the COVID-19 pandemic: a rapid review

Government Responses Matter: Predicting Covid-19 cases in US using an empirical Bayesian time series framework

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