Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries
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Abenvenuto, 2020
Anastassopoulou, 2020, Data-based analysis, modeling and forecasting of the COVID-19 outbreak, PLoS One, 15, 10.1371/journal.pone.0230405
Box, 1976
Dehesh, 2020
Dickey, 1981, Likelihood ratio statistics for autoregressive time series with a unit root, Econometrica, 1981, 1057, 10.2307/1912517
Fanelli, 2020, Analysis and forecast of COVID-19 spreading in China, Italy and France, Chaos, Solitons & Fractals, 134, 1, 10.1016/j.chaos.2020.109761
Granger, 1986
Grasselli, 2020, Critical care utilization for the COVID-19 outbreak in lombardy, Italy: Early experience and forecast during an emergency response, Journal of the American Medical Association, 323, 1545, 10.1001/jama.2020.4031
Gupta, 2020, Trend analysis and forecasting of COVID-19 outbreak in India, MedRxiv
Hu, 2020
Jia, 2020, Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China, medRxiv
John Hopkins University
Kim, 2020, AAEDM: theoretical dynamic epidemic DiffusionModel and covid-19 Korea pandemic cases, medRxiv
Lehmann, 2001, Long-term behaviour and cross-CorrelationWater quality analysis of the river elbe, Germany, Journal of Water Resources, 35, 2153
Li, 2020, Trend and forecasting of the COVID-19 outbreak in China, Journal of Information Security, 80, 469
Liu, 2020, COVID-19 progression timeline and effectiveness of response-to-spread interventions across the United States, medRxiv
Liu, 2011, Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model, BMC Infectious Diseases, 11, 218, 10.1186/1471-2334-11-218
Liu, 2020
Lover, 2020, Sentinel event surveillance to estimate total SARS-CoV-2 infections, United States, medRxiv
Luz, 2008, Time series analysis of dengue incidence in Rio de Januaryeiro, Brazil, The American Journal of Tropical Medicine and Hygiene, 79, 933, 10.4269/ajtmh.2008.79.933
Massonnaud, 2020, COVID-19: forecasting short term hospital needs in France, medRxiv
Rios, 2000, A statistical analysis of the seasonality in pulmonary tuberculosis, European Journal of Epidemiology, 16, 483, 10.1023/A:1007653329972
Roosa, 2020, 5, 256
Russo, 2020, Tracing day-zero and forecasting the fade out of the COVID-19 outbreak in lombardy, Italy: A compartmental modeling and numerical optimization approach, medRxiv
Shi, 2020, Temporal relationship between outbound traffic from Wuhan and the 2019 coronavirus disease (COVID-19) incidence in China, medRxiv
Shumway, 2010
Wei, 2006, 478
Wise, 2020
Wongkoon, 2012, Development of temporal modeling for prediction of dengue infection in Northeastern Thailand, Asian Pacific journal of tropical medicine, 5, 249, 10.1016/S1995-7645(12)60034-0
World Health Organization
Wu, 2020, Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study, Lancet, 395, 689, 10.1016/S0140-6736(20)30260-9
Yonar, 2020, Modeling and forecasting for the number of cases of the COVID-19 pandemic with the curve estimation models, the box-jenkins and exponential smoothing methods, EJMO, 4, 160
Zeynep, 2020
Zhan, 2020, Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding, medRxiv
