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Published in: BMC Public Health 1/2019

Open Access 01-12-2019 | Public Health | Debate

Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples

Authors: Chelsea S. Lutz, Mimi P. Huynh, Monica Schroeder, Sophia Anyatonwu, F. Scott Dahlgren, Gregory Danyluk, Danielle Fernandez, Sharon K. Greene, Nodar Kipshidze, Leann Liu, Osaro Mgbere, Lisa A. McHugh, Jennifer F. Myers, Alan Siniscalchi, Amy D. Sullivan, Nicole West, Michael A. Johansson, Matthew Biggerstaff

Published in: BMC Public Health | Issue 1/2019

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Abstract

Background

Infectious disease forecasting aims to predict characteristics of both seasonal epidemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts.

Main body

For forecasts to be fully integrated into public health decision-making, federal, state, and local officials must understand how forecasts were made, how to interpret forecasts, and how well the forecasts have performed in the past. Since the 2013–14 influenza season, the Influenza Division at the Centers for Disease Control and Prevention (CDC) has hosted collaborative challenges to forecast the timing, intensity, and short-term trajectory of influenza-like illness in the United States. Additional efforts to advance forecasting science have included influenza initiatives focused on state-level and hospitalization forecasts, as well as other infectious diseases. Using CDC influenza forecasting challenges as an example, this paper provides an overview of infectious disease forecasting; applications of forecasting to public health; and current work to develop best practices for forecast methodology, applications, and communication.

Conclusions

These efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science.
Literature
1.
go back to reference [No author]. American Meteorological Society. Enhancing Weather Information with Probability Forecasts. Bull Amer Meteor Soc. 2008;89. [No author]. American Meteorological Society. Enhancing Weather Information with Probability Forecasts. Bull Amer Meteor Soc. 2008;89.
2.
go back to reference Morss RE, Demuth JL, Lazo JK. Communicating uncertainty in weather forecasts: a survey of the U.S. public. Weather Forecast. 2008;23:974–91.CrossRef Morss RE, Demuth JL, Lazo JK. Communicating uncertainty in weather forecasts: a survey of the U.S. public. Weather Forecast. 2008;23:974–91.CrossRef
3.
go back to reference Moran KR, Fairchild G, Generous N, Hickmann K, Osthus D, Priedhorsky R, et al. Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast. J Infect Dis. 2016;214(suppl_4):S404–S8.CrossRef Moran KR, Fairchild G, Generous N, Hickmann K, Osthus D, Priedhorsky R, et al. Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast. J Infect Dis. 2016;214(suppl_4):S404–S8.CrossRef
4.
go back to reference Fischer LS, Santibanez S, Hatchett RJ, Jernigan DB, Meyers LA, Thorpe PG, et al. CDC grand rounds: modeling and public health decision-making. MMWR Morb Mortal Wkly Rep. 2016;65(48):1374–7.CrossRef Fischer LS, Santibanez S, Hatchett RJ, Jernigan DB, Meyers LA, Thorpe PG, et al. CDC grand rounds: modeling and public health decision-making. MMWR Morb Mortal Wkly Rep. 2016;65(48):1374–7.CrossRef
5.
go back to reference Glasser JW, Hupert N, McCauley MM, Hatchett R. Modeling and public health emergency responses: lessons from SARS. Epidemics. 2011;3(1):32–7.CrossRef Glasser JW, Hupert N, McCauley MM, Hatchett R. Modeling and public health emergency responses: lessons from SARS. Epidemics. 2011;3(1):32–7.CrossRef
6.
go back to reference Meltzer MI, Atkins CY, Santibanez S, Knust B, Petersen BW, Ervin ED, et al. Estimating the future number of cases in the Ebola epidemic--Liberia and Sierra Leone, 2014-2015. MMWR supplements. 2014;63(3):1–14.PubMed Meltzer MI, Atkins CY, Santibanez S, Knust B, Petersen BW, Ervin ED, et al. Estimating the future number of cases in the Ebola epidemic--Liberia and Sierra Leone, 2014-2015. MMWR supplements. 2014;63(3):1–14.PubMed
7.
go back to reference Holloway R, Rasmussen SA, Zaza S, Cox NJ, Jernigan DB. Updated preparedness and response framework for influenza pandemics. MMWR Recomm Rep. 2014;63(RR-06):1–18.PubMed Holloway R, Rasmussen SA, Zaza S, Cox NJ, Jernigan DB. Updated preparedness and response framework for influenza pandemics. MMWR Recomm Rep. 2014;63(RR-06):1–18.PubMed
8.
go back to reference Wernstedt K, Roberts PS, Arvai J, Redmond K. How emergency managers (mis?)interpret forecasts. Disasters. 2019;43(1):88-109.CrossRef Wernstedt K, Roberts PS, Arvai J, Redmond K. How emergency managers (mis?)interpret forecasts. Disasters. 2019;43(1):88-109.CrossRef
9.
go back to reference Gregory R, et al. Structured decision making: a practical guide to environmental management choices. Hoboken: Wiley–Blackwell; 2012.CrossRef Gregory R, et al. Structured decision making: a practical guide to environmental management choices. Hoboken: Wiley–Blackwell; 2012.CrossRef
10.
go back to reference Doms C, Kramer SC, Shaman J. Assessing the use of influenza forecasts and epidemiological modeling in public health decision making in the United States. Sci Rep. 2018;8(1):12406.CrossRef Doms C, Kramer SC, Shaman J. Assessing the use of influenza forecasts and epidemiological modeling in public health decision making in the United States. Sci Rep. 2018;8(1):12406.CrossRef
13.
go back to reference Heesterbeek H, Anderson RM, Andreasen V, Bansal S, De Angelis D, Dye C, et al. Modeling infectious disease dynamics in the complex landscape of global health. Science. 2015;347(6227):aaa4339.CrossRef Heesterbeek H, Anderson RM, Andreasen V, Bansal S, De Angelis D, Dye C, et al. Modeling infectious disease dynamics in the complex landscape of global health. Science. 2015;347(6227):aaa4339.CrossRef
14.
go back to reference Lee VJ, Lye DC, Wilder-Smith A. Combination strategies for pandemic influenza response - a systematic review of mathematical modeling studies. BMC Med. 2009;7:76.CrossRef Lee VJ, Lye DC, Wilder-Smith A. Combination strategies for pandemic influenza response - a systematic review of mathematical modeling studies. BMC Med. 2009;7:76.CrossRef
15.
go back to reference Smith NR, Trauer JM, Gambhir M, Richards JS, Maude RJ, Keith JM, et al. Agent-based models of malaria transmission: a systematic review. Malar J. 2018;17(1):299.CrossRef Smith NR, Trauer JM, Gambhir M, Richards JS, Maude RJ, Keith JM, et al. Agent-based models of malaria transmission: a systematic review. Malar J. 2018;17(1):299.CrossRef
16.
go back to reference Germann TC, Kadau K, Longini IM Jr, Macken CA. Mitigation strategies for pandemic influenza in the United States. Proc Natl Acad Sci U S A. 2006;103(15):5935–40.CrossRef Germann TC, Kadau K, Longini IM Jr, Macken CA. Mitigation strategies for pandemic influenza in the United States. Proc Natl Acad Sci U S A. 2006;103(15):5935–40.CrossRef
17.
go back to reference Chretien JP, George D, Shaman J, Chitale RA, McKenzie FE. Influenza forecasting in human populations: a scoping review. PLoS One. 2014;9(4):e94130.CrossRef Chretien JP, George D, Shaman J, Chitale RA, McKenzie FE. Influenza forecasting in human populations: a scoping review. PLoS One. 2014;9(4):e94130.CrossRef
18.
go back to reference Gambhir M, Bozio C, O'Hagan JJ, Uzicanin A, Johnson LE, Biggerstaff M, et al. Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential. Clin Infect Dis. 2015;60(Suppl 1):S11–9.CrossRef Gambhir M, Bozio C, O'Hagan JJ, Uzicanin A, Johnson LE, Biggerstaff M, et al. Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential. Clin Infect Dis. 2015;60(Suppl 1):S11–9.CrossRef
19.
go back to reference Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe MV. A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses. 2014 May;8(3):309–16.CrossRef Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe MV. A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses. 2014 May;8(3):309–16.CrossRef
20.
go back to reference Moss R, Zarebski A, Dawson P, McCaw JM. Forecasting influenza outbreak dynamics in Melbourne from internet search query surveillance data. Influenza Other Respir Viruses. 2016 Jul;10(4):314–23.CrossRef Moss R, Zarebski A, Dawson P, McCaw JM. Forecasting influenza outbreak dynamics in Melbourne from internet search query surveillance data. Influenza Other Respir Viruses. 2016 Jul;10(4):314–23.CrossRef
21.
go back to reference Yang W, Karspeck A, Shaman J. Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics. PLoS Comput Biol. 2014;10(4):e1003583.CrossRef Yang W, Karspeck A, Shaman J. Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics. PLoS Comput Biol. 2014;10(4):e1003583.CrossRef
22.
go back to reference Smith GJ, Bahl J, Vijaykrishna D, Zhang J, Poon LL, Chen H, et al. Dating the emergence of pandemic influenza viruses. Proc Natl Acad Sci U S A. 2009;106(28):11709–12.CrossRef Smith GJ, Bahl J, Vijaykrishna D, Zhang J, Poon LL, Chen H, et al. Dating the emergence of pandemic influenza viruses. Proc Natl Acad Sci U S A. 2009;106(28):11709–12.CrossRef
23.
go back to reference Taubenberger JK, Kash JC. Influenza virus evolution, host adaptation, and pandemic formation. Cell Host Microbe. 2010 Jun 25;7(6):440–51.CrossRef Taubenberger JK, Kash JC. Influenza virus evolution, host adaptation, and pandemic formation. Cell Host Microbe. 2010 Jun 25;7(6):440–51.CrossRef
24.
go back to reference Molinari NA, Ortega-Sanchez IR, Messonnier ML, Thompson WW, Wortley PM, Weintraub E, et al. The annual impact of seasonal influenza in the US: measuring disease burden and costs. Vaccine. 2007;25(27):5086–96.CrossRef Molinari NA, Ortega-Sanchez IR, Messonnier ML, Thompson WW, Wortley PM, Weintraub E, et al. The annual impact of seasonal influenza in the US: measuring disease burden and costs. Vaccine. 2007;25(27):5086–96.CrossRef
25.
go back to reference Reed C, Chaves SS, Daily Kirley P, Emerson R, Aragon D, Hancock EB, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369.CrossRef Reed C, Chaves SS, Daily Kirley P, Emerson R, Aragon D, Hancock EB, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369.CrossRef
26.
go back to reference Appiah GD, Blanton L, D'Mello T, Kniss K, Smith S, Mustaquim D, et al. Influenza activity - United States, 2014-15 season and composition of the 2015-16 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2015;64(21):583–90.PubMedPubMedCentral Appiah GD, Blanton L, D'Mello T, Kniss K, Smith S, Mustaquim D, et al. Influenza activity - United States, 2014-15 season and composition of the 2015-16 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2015;64(21):583–90.PubMedPubMedCentral
27.
go back to reference Davlin SL, Blanton L, Kniss K, Mustaquim D, Smith S, Kramer N, et al. Influenza activity - United States, 2015-16 season and composition of the 2016-17 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2016;65(22):567–75.CrossRef Davlin SL, Blanton L, Kniss K, Mustaquim D, Smith S, Kramer N, et al. Influenza activity - United States, 2015-16 season and composition of the 2016-17 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2016;65(22):567–75.CrossRef
28.
go back to reference Blanton L, Alabi N, Mustaquim D, Taylor C, Kniss K, Kramer N, et al. Update: influenza activity in the United States during the 2016-17 season and composition of the 2017-18 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2017;66(25):668–76.CrossRef Blanton L, Alabi N, Mustaquim D, Taylor C, Kniss K, Kramer N, et al. Update: influenza activity in the United States during the 2016-17 season and composition of the 2017-18 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2017;66(25):668–76.CrossRef
29.
go back to reference Garten R, Blanton L, Elal AIA, Alabi N, Barnes J, Biggerstaff M, et al. Update: influenza activity in the United States during the 2017-18 season and composition of the 2018-19 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2018 Jun 8;67(22):634–42.CrossRef Garten R, Blanton L, Elal AIA, Alabi N, Barnes J, Biggerstaff M, et al. Update: influenza activity in the United States during the 2017-18 season and composition of the 2018-19 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2018 Jun 8;67(22):634–42.CrossRef
30.
go back to reference Biggerstaff M, Alper D, Dredze M, Fox S, Fung IC, Hickmann KS, et al. Results from the Centers for Disease Control and Prevention's predict the 2013-2014 influenza season challenge. BMC Infect Dis. 2016;16:357.CrossRef Biggerstaff M, Alper D, Dredze M, Fox S, Fung IC, Hickmann KS, et al. Results from the Centers for Disease Control and Prevention's predict the 2013-2014 influenza season challenge. BMC Infect Dis. 2016;16:357.CrossRef
32.
go back to reference Biggerstaff M, Johansson M, Alper D, Brooks LC, Chakraborty P, Farrow DC, et al. Results from the second year of a collaborative effort to forecast influenza seasons in the United States. Epidemics. 2018 Sep;24:26–33.CrossRef Biggerstaff M, Johansson M, Alper D, Brooks LC, Chakraborty P, Farrow DC, et al. Results from the second year of a collaborative effort to forecast influenza seasons in the United States. Epidemics. 2018 Sep;24:26–33.CrossRef
33.
go back to reference McGowan CJ, Biggerstaff M, Johansson M, Apfeldorf KM, Ben-Nun M, Brooks L, et al. Collaborative efforts to forecast seasonal influenza in the United States, 2015-2016. Sci Rep. 2019;9(1):683.CrossRef McGowan CJ, Biggerstaff M, Johansson M, Apfeldorf KM, Ben-Nun M, Brooks L, et al. Collaborative efforts to forecast seasonal influenza in the United States, 2015-2016. Sci Rep. 2019;9(1):683.CrossRef
34.
go back to reference Brammer L, Blanton L, Epperson S, Mustaquim D, Bishop A, Kniss K, et al. Surveillance for influenza during the 2009 influenza a (H1N1) pandemic-United States, April 2009-march 2010. Clin Infect Dis. 2011;52(Suppl 1):S27–35.CrossRef Brammer L, Blanton L, Epperson S, Mustaquim D, Bishop A, Kniss K, et al. Surveillance for influenza during the 2009 influenza a (H1N1) pandemic-United States, April 2009-march 2010. Clin Infect Dis. 2011;52(Suppl 1):S27–35.CrossRef
37.
go back to reference Brammer L, Budd A, Cox N. Seasonal and pandemic influenza surveillance considerations for constructing multicomponent systems. Influenza Other Respir Viruses. 2009;3(2):51–8.CrossRef Brammer L, Budd A, Cox N. Seasonal and pandemic influenza surveillance considerations for constructing multicomponent systems. Influenza Other Respir Viruses. 2009;3(2):51–8.CrossRef
38.
go back to reference Vittinghoff E. Statistics for biology and health. New York: Springer; 2005. Regression methods in biostatistics: linear, logistic, survival, and repeated measures models. Vittinghoff E. Statistics for biology and health. New York: Springer; 2005. Regression methods in biostatistics: linear, logistic, survival, and repeated measures models.
39.
go back to reference Shaman J, Kandula S. Improved discrimination of influenza forecast accuracy using consecutive predictions. PLoS Curr. 2015;5:7. Shaman J, Kandula S. Improved discrimination of influenza forecast accuracy using consecutive predictions. PLoS Curr. 2015;5:7.
40.
go back to reference Hilden J, Habbema JD, Bjerregaard B. The measurement of performance in probabilistic diagnosis. II. Trustworthiness of the exact values of the diagnostic probabilities. Methods Inf Med. 1978;17(4):227–37.CrossRef Hilden J, Habbema JD, Bjerregaard B. The measurement of performance in probabilistic diagnosis. II. Trustworthiness of the exact values of the diagnostic probabilities. Methods Inf Med. 1978;17(4):227–37.CrossRef
41.
go back to reference Gneiting T, Raftery AE. Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc. 2007;102(477):359–78.CrossRef Gneiting T, Raftery AE. Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc. 2007;102(477):359–78.CrossRef
42.
go back to reference Reich NG, Osthus D, Ray EL, et al. Reply to Bracher: Scoring probabilistic forecasts to maximize public health interpretability. Proc Natl Acad Sci U S A. 2019;116(42):20811–20812.CrossRef Reich NG, Osthus D, Ray EL, et al. Reply to Bracher: Scoring probabilistic forecasts to maximize public health interpretability. Proc Natl Acad Sci U S A. 2019;116(42):20811–20812.CrossRef
43.
go back to reference Biggerstaff M, Dahlgren FS, Lutz CS, Huynh M, Johansson M, Reed C. Six seasons of forecasting influenza in the United States, 2013–14 to 2018–19. In: Council for State and Territorial Epidemiologists Annual Conference: 2019 June 5; Atlanta, GA. Biggerstaff M, Dahlgren FS, Lutz CS, Huynh M, Johansson M, Reed C. Six seasons of forecasting influenza in the United States, 2013–14 to 2018–19. In: Council for State and Territorial Epidemiologists Annual Conference: 2019 June 5; Atlanta, GA.
44.
go back to reference Biggerstaff M, Dahlgren FS, Lutz CS, Johansson M, Reed C. Six seasons of forecasting influenza in the United States, 2013–14 to 2018–19. In: Options X for the Control of Influenza: 2019 August 29; Suntec, Singapore. Biggerstaff M, Dahlgren FS, Lutz CS, Johansson M, Reed C. Six seasons of forecasting influenza in the United States, 2013–14 to 2018–19. In: Options X for the Control of Influenza: 2019 August 29; Suntec, Singapore.
47.
go back to reference Brennen A, George D, Sieniawki G, Reed C, Lutz CS, Dahlgren FS, Biggerstaff M. Viziflu: an open-source tool for visualizing seasonal influenza forecasting results and uncertainties. In: Epidemics 7th International Conference on Infectious Disease Dynamics: 2019 December 3–6. Brennen A, George D, Sieniawki G, Reed C, Lutz CS, Dahlgren FS, Biggerstaff M. Viziflu: an open-source tool for visualizing seasonal influenza forecasting results and uncertainties. In: Epidemics 7th International Conference on Infectious Disease Dynamics: 2019 December 3–6.
53.
go back to reference Johansson MA, Powers AM, Pesik N, Cohen NJ, Staples JE. Nowcasting the spread of chikungunya virus in the Americas. PLoS One. 2014;9(8):e104915.CrossRef Johansson MA, Powers AM, Pesik N, Cohen NJ, Staples JE. Nowcasting the spread of chikungunya virus in the Americas. PLoS One. 2014;9(8):e104915.CrossRef
54.
go back to reference Greene SK, Lim S, Fine A. Identifying areas at greatest risk for recent Zika virus importation - New York City, 2016. PLoS Curr. 2018;25:10. Greene SK, Lim S, Fine A. Identifying areas at greatest risk for recent Zika virus importation - New York City, 2016. PLoS Curr. 2018;25:10.
55.
go back to reference Kandula S, Hsu D, Shaman J. Subregional nowcasts of seasonal influenza using search trends. J Med Internet Res. 2017;19(11):e370.CrossRef Kandula S, Hsu D, Shaman J. Subregional nowcasts of seasonal influenza using search trends. J Med Internet Res. 2017;19(11):e370.CrossRef
56.
go back to reference Lu FS, Hou S, Baltrusaitis K, Shah M, Leskovec J, Sosic R, et al. Accurate influenza monitoring and forecasting using novel internet data streams: a case study in the Boston Metropolis. JMIR Public Health Surveill. 2018;4(1):e4.CrossRef Lu FS, Hou S, Baltrusaitis K, Shah M, Leskovec J, Sosic R, et al. Accurate influenza monitoring and forecasting using novel internet data streams: a case study in the Boston Metropolis. JMIR Public Health Surveill. 2018;4(1):e4.CrossRef
57.
go back to reference Yang W, Olson DR, Shaman J. Forecasting influenza outbreaks in boroughs and neighborhoods of New York City. PLoS Comput Biol. 2016;12(11):e1005201.CrossRef Yang W, Olson DR, Shaman J. Forecasting influenza outbreaks in boroughs and neighborhoods of New York City. PLoS Comput Biol. 2016;12(11):e1005201.CrossRef
Metadata
Title
Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples
Authors
Chelsea S. Lutz
Mimi P. Huynh
Monica Schroeder
Sophia Anyatonwu
F. Scott Dahlgren
Gregory Danyluk
Danielle Fernandez
Sharon K. Greene
Nodar Kipshidze
Leann Liu
Osaro Mgbere
Lisa A. McHugh
Jennifer F. Myers
Alan Siniscalchi
Amy D. Sullivan
Nicole West
Michael A. Johansson
Matthew Biggerstaff
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2019
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-019-7966-8

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