COVID-19: Forecasting confirmed cases and deaths with a simple time series model

[1]  Aboul Ella Hassanien,et al.  DAY LEVEL FORECASTING FOR CORONAVIRUS DISEASE (COVID-19) , 2020, International Journal of Healthcare Information Systems and Informatics.

[2]  M. Bizzarri Chronicle of a Pandemic Foretold , 2020 .

[3]  Jurgen A. Doornik,et al.  Short-term forecasting of the coronavirus pandemic , 2020, International Journal of Forecasting.

[4]  Fabiana Zama,et al.  Monitoring Italian COVID-19 spread by a forced SEIRD model , 2020, PloS one.

[5]  Yaneer Bar-Yam,et al.  Opinion: What models can and cannot tell us about COVID-19 , 2020, Proceedings of the National Academy of Sciences.

[6]  Aboul Ella Hassanien,et al.  A machine learning forecasting model for COVID-19 pandemic in India , 2020, Stochastic Environmental Research and Risk Assessment.

[7]  S. Pei,et al.  Differential Effects of Intervention Timing on COVID-19 Spread in the United States , 2020, Science Advances.

[8]  Sen Pei,et al.  Projection of COVID-19 Cases and Deaths in the US as Individual States Re-open May 4,2020 , 2020, medRxiv.

[9]  C. Murray Forecasting the impact of the first wave of the COVID-19 pandemic on hospital demand and deaths for the USA and European Economic Area countries , 2020, medRxiv.

[10]  Imperial College COVID-19 Response Team,et al.  Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update , 2020, 2004.11342.

[11]  Maytal Dahan,et al.  Projections for first-wave COVID-19 deaths across the US using social-distancing measures derived from mobile phones , 2020, medRxiv.

[12]  N. Taleb,et al.  Tail risk of contagious diseases , 2020, 2004.08658.

[13]  Fotios Petropoulos,et al.  Forecasting the novel coronavirus COVID-19 , 2020, PloS one.

[14]  Rajan Gupta,et al.  Trend Analysis and Forecasting of COVID-19 outbreak in India , 2020, medRxiv.

[15]  C. Whittaker,et al.  Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand , 2020 .

[16]  R. Trimble COVID-19 Dashboard , 2020 .

[17]  W. Liang,et al.  Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions , 2020, Journal of thoracic disease.

[18]  D. Missing ‘Instead of Coronavirus, the Hunger Will Kill Us.’ A Global Food Crisis Looms , 2020 .

[19]  T. Hale,et al.  Oxford COVID-19 Government Response Tracker , 2020 .

[20]  Robert L. Winkler,et al.  Why do some combinations perform better than others? , 2020 .

[21]  Yi-Ju Tseng,et al.  Developing epidemic forecasting models to assist disease surveillance for influenza with electronic health records , 2019, International Journal of Computers and Applications.

[22]  Zuhaimy Ismail,et al.  A Review of Epidemic Forecasting Using Artificial Neural Networks , 2019, International Journal of Epidemiologic Research.

[23]  M. Karami,et al.  Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran , 2019, BMC Research Notes.

[24]  Haruka Morita,et al.  Evaluation of mechanistic and statistical methods in forecasting influenza-like illness , 2018, Journal of The Royal Society Interface.

[25]  Fotios Petropoulos,et al.  Judgmental selection of forecasting models , 2018 .

[26]  Fotios Petropoulos,et al.  forecast: Forecasting functions for time series and linear models , 2018 .

[27]  Gerardo Chowell,et al.  The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt. , 2017, Epidemics.

[28]  Bryan T Grenfell,et al.  tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics , 2017, PloS one.

[29]  Madhav V. Marathe,et al.  A framework for evaluating epidemic forecasts , 2017, BMC Infectious Diseases.

[30]  Michael A. Irvine,et al.  Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework , 2017, Epidemics.

[31]  Ronald Rosenfeld,et al.  A human judgment approach to epidemiological forecasting , 2017, PLoS Comput. Biol..

[32]  Dylan B. George,et al.  Mathematical modeling of the West Africa Ebola epidemic , 2015, eLife.

[33]  Zaid Chalabi,et al.  Time series regression model for infectious disease and weather. , 2015, Environmental research.

[34]  Jieping Ye,et al.  Dynamic Poisson Autoregression for Influenza-Like-Illness Case Count Prediction , 2015, KDD.

[35]  Ronald Rosenfeld,et al.  Flexible Modeling of Epidemics with an Empirical Bayes Framework , 2014, PLoS Comput. Biol..

[36]  Alicia Karspeck,et al.  Real-Time Influenza Forecasts during the 2012–2013 Season , 2013, Nature Communications.

[37]  David L. Buckeridge,et al.  Predictive Validation of an Influenza Spread Model , 2013, PloS one.

[38]  J. Shaman,et al.  Forecasting seasonal outbreaks of influenza , 2012, Proceedings of the National Academy of Sciences.

[39]  Qi Li,et al.  Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. , 2012, The American journal of tropical medicine and hygiene.

[40]  Ireneous N. Soyiri,et al.  An overview of health forecasting , 2012, Environmental Health and Preventive Medicine.

[41]  J C Fackler,et al.  A multi-tiered time-series modelling approach to forecasting respiratory syncytial virus incidence at the local level , 2011, Epidemiology and Infection.

[42]  P. Dent The Black Swan: The Impact of the Highly Improbable (2nd edition) , 2010 .

[43]  Rob J Hyndman,et al.  Forecasting with Exponential Smoothing: The State Space Approach , 2008 .

[44]  Neil M. Ferguson,et al.  The effect of public health measures on the 1918 influenza pandemic in U.S. cities , 2007, Proceedings of the National Academy of Sciences.

[45]  Nassim Nicholas Taleb,et al.  The Black Swan: The Impact of the Highly Improbable , 2007 .

[46]  E. S. Gardner EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[47]  J. Taubenberger,et al.  1918 Influenza: the Mother of All Pandemics , 2006, Emerging infectious diseases.

[48]  M. Osterholm,et al.  Preparing for the next pandemic. , 2005, The New England journal of medicine.

[49]  James W. Taylor Exponential smoothing with a damped multiplicative trend , 2003 .

[50]  Rob J Hyndman,et al.  A state space framework for automatic forecasting using exponential smoothing methods , 2002 .

[51]  Leonard J. Tashman,et al.  Out-of-sample tests of forecasting accuracy: an analysis and review , 2000 .

[52]  T. N. Krishnamurti,et al.  Improved Weather and Seasonal Climate Forecasts from Multimodel Superensemble. , 1999, Science.

[53]  E. Langer The illusion of control. , 1975 .

[54]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[55]  J. Taubenberger,et al.  Influenza : the Mother of All Pandemics , 2022 .