A novel sub-epidemic modeling framework for short-term forecasting epidemic waves

[1] Yan P(2018)A frailty model for intervention effectiveness against disease transmission when implemented with unobservable heterogeneity Math. Biosci. Eng 15 275-298

[2] Colgate SA(1989)Risk behavior-based model of the cubic growth of acquired immunodeficiency syndrome in the United States Proc Natl Acad Sci U S A 86 4793-4797

[3] Stanley EA(2009)Calculating the potential for within-flight transmission of influenza A (H1N1) BMC Med 7 81-1536

[4] Hyman JM(1998)Social network dynamics and HIV transmission Aids 12 1529-21

[5] Layne SP(2018)The RAPIDD ebola forecasting challenge: synthesis and lessons learnt Epidemics 22 13-3154

[6] Qualls C(2019)Assessing the performance of real-time epidemic forecasts: a case study of Ebola in the Western area region of Sierra Leone, 2014-15 PLoS Comput Biol 15 3146-516

[7] Wagner BG(2019)A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States Proc Natl Acad Sci U S A 116 509-131

[8] Coburn BJ(2004)Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures Am J Epidemiol 160 42-1301

[9] Blower S(2017)Perspectives on model forecasts of the 2014-2015 Ebola epidemic in West Africa: lessons and the way forward BMC Med 15 123-142

[10] Rothenberg RB(2019)Analyzing and forecasting the Ebola incidence in North Kivu, the Democratic Republic of the Congo from 2018-19 in real time Epidemics 27 1298-293

[1] Yan P(2018)A frailty model for intervention effectiveness against disease transmission when implemented with unobservable heterogeneity Math. Biosci. Eng 15 275-298

[2] Colgate SA(1989)Risk behavior-based model of the cubic growth of acquired immunodeficiency syndrome in the United States Proc Natl Acad Sci U S A 86 4793-4797

[3] Stanley EA(2009)Calculating the potential for within-flight transmission of influenza A (H1N1) BMC Med 7 81-1536

[4] Hyman JM(1998)Social network dynamics and HIV transmission Aids 12 1529-21

[5] Layne SP(2018)The RAPIDD ebola forecasting challenge: synthesis and lessons learnt Epidemics 22 13-3154

[6] Qualls C(2019)Assessing the performance of real-time epidemic forecasts: a case study of Ebola in the Western area region of Sierra Leone, 2014-15 PLoS Comput Biol 15 3146-516

[7] Wagner BG(2019)A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States Proc Natl Acad Sci U S A 116 509-131

[8] Coburn BJ(2004)Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures Am J Epidemiol 160 42-1301

[9] Blower S(2017)Perspectives on model forecasts of the 2014-2015 Ebola epidemic in West Africa: lessons and the way forward BMC Med 15 123-142

[10] Rothenberg RB(2019)Analyzing and forecasting the Ebola incidence in North Kivu, the Democratic Republic of the Congo from 2018-19 in real time Epidemics 27 1298-293