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Published in: Environmental Health and Preventive Medicine 1/2013

01-01-2013 | Review

An overview of health forecasting

Authors: Ireneous N. Soyiri, Daniel D. Reidpath

Published in: Environmental Health and Preventive Medicine | Issue 1/2013

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Abstract

Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.
Literature
1.
go back to reference Armstrong JS. Principles of forecasting: a handbook for researchers and practitioners. Norwell: Kluwer Academic Publishers; 2001. Armstrong JS. Principles of forecasting: a handbook for researchers and practitioners. Norwell: Kluwer Academic Publishers; 2001.
2.
go back to reference Lewis JB, McGrath RJ, Seidel LF. Essentials of applied quantitative methods for health services managers. Sudbury: Jones and Bartlett Publishers, LLC.; 2011. Lewis JB, McGrath RJ, Seidel LF. Essentials of applied quantitative methods for health services managers. Sudbury: Jones and Bartlett Publishers, LLC.; 2011.
3.
go back to reference Antoniou SA, Antoniou GA, Granderath FA, Mavroforou A, Giannoukas AD, Antoniou AI. Reflections of the Hippocratic Oath in modern medicine. World J Surg. 2010;34(12):3075–9.PubMedCrossRef Antoniou SA, Antoniou GA, Granderath FA, Mavroforou A, Giannoukas AD, Antoniou AI. Reflections of the Hippocratic Oath in modern medicine. World J Surg. 2010;34(12):3075–9.PubMedCrossRef
4.
go back to reference Chang A, Lad EM, Lad SP. Hippocrates’ influence on the origins of neurosurgery. Neurosurg Focus. 2007;23(1):E9.CrossRef Chang A, Lad EM, Lad SP. Hippocrates’ influence on the origins of neurosurgery. Neurosurg Focus. 2007;23(1):E9.CrossRef
5.
go back to reference Chadwick J, Mann WN. Medicine. In: Lloyd GER, editor. Hippocratic writings. England: Penguin Group; 1978. p. 170–171. Chadwick J, Mann WN. Medicine. In: Lloyd GER, editor. Hippocratic writings. England: Penguin Group; 1978. p. 170–171.
7.
go back to reference Marno P, Chalder M, Laing-Morton T, Levy M, Sachon P, Halpin D. Can a health forecasting service offer COPD patients a novel way to manage their condition? J Health Serv Res Policy. 2010;15(3):150–5.PubMedCrossRef Marno P, Chalder M, Laing-Morton T, Levy M, Sachon P, Halpin D. Can a health forecasting service offer COPD patients a novel way to manage their condition? J Health Serv Res Policy. 2010;15(3):150–5.PubMedCrossRef
9.
go back to reference WHO. Using climate to predict infectious disease epidemics. In: Kuhn K, Campbell-Lendrum D, Haines A, Cox J, editors. World Health Organization. Geneva: World Health Organization; 2005. WHO. Using climate to predict infectious disease epidemics. In: Kuhn K, Campbell-Lendrum D, Haines A, Cox J, editors. World Health Organization. Geneva: World Health Organization; 2005.
10.
go back to reference Rogers DP, Shapiro MA, Brunet G, Cohen JC, Connor SJ, Diallo AA, et al. Health and climate—opportunities. Proc Environ Sci. 2010;1:37–54.CrossRef Rogers DP, Shapiro MA, Brunet G, Cohen JC, Connor SJ, Diallo AA, et al. Health and climate—opportunities. Proc Environ Sci. 2010;1:37–54.CrossRef
11.
go back to reference Makridakis S, Taleb N. Decision making and planning under low levels of predictability. Int J Forecast. 2009;25(4):716–33.CrossRef Makridakis S, Taleb N. Decision making and planning under low levels of predictability. Int J Forecast. 2009;25(4):716–33.CrossRef
12.
go back to reference Vaughan Williams L, Stekler HO. Sports forecasting. Int J Forecast. 2010;26(3):445–7.CrossRef Vaughan Williams L, Stekler HO. Sports forecasting. Int J Forecast. 2010;26(3):445–7.CrossRef
13.
go back to reference Vaughan Williams L. Information efficiency in financial and betting markets. Cambridge: Cambridge University Press; 2005. p. 1–123. Vaughan Williams L. Information efficiency in financial and betting markets. Cambridge: Cambridge University Press; 2005. p. 1–123.
14.
go back to reference McMichael AJ, Campbell-Lendrum DH, Corvalán CF, Ebi KL, Githeko AK, Scheraga JD, et al., editors. Climate change and human health: risks and responses. Geneva: World Health Organization; 2003. McMichael AJ, Campbell-Lendrum DH, Corvalán CF, Ebi KL, Githeko AK, Scheraga JD, et al., editors. Climate change and human health: risks and responses. Geneva: World Health Organization; 2003.
15.
go back to reference WMO. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation. In: Klein Tank AMG, Zwiers FW, Zhang X, editors. Climate data and monitoring. Geneva: World Meteorological Organization; 2009. WMO. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation. In: Klein Tank AMG, Zwiers FW, Zhang X, editors. Climate data and monitoring. Geneva: World Meteorological Organization; 2009.
16.
go back to reference Manton KG, Soldo BJ, Vierck E. “Death and taxes”: a contrary view. Popul Today. 1984;12(11):2, 8–9. Manton KG, Soldo BJ, Vierck E. “Death and taxes”: a contrary view. Popul Today. 1984;12(11):2, 8–9.
17.
go back to reference Rogge JR. Population trends and the status of population policy in Africa. J Geogr. 1982;81(5):164–74.CrossRef Rogge JR. Population trends and the status of population policy in Africa. J Geogr. 1982;81(5):164–74.CrossRef
18.
go back to reference Fildes R. The forecasting journals and their contribution to forecasting research: citation analysis and expert opinion. Int J Forecast. 2006;22(3):415–32.CrossRef Fildes R. The forecasting journals and their contribution to forecasting research: citation analysis and expert opinion. Int J Forecast. 2006;22(3):415–32.CrossRef
19.
go back to reference Armstrong JS, Green KC. Demand forecasting: evidence-based methods. In: Working Paper 24/05, Demandforecasting35-Monashdoc. Department of Econometrics and Business Statistics, Monash University, Australia; 2005. Armstrong JS, Green KC. Demand forecasting: evidence-based methods. In: Working Paper 24/05, Demandforecasting35-Monashdoc. Department of Econometrics and Business Statistics, Monash University, Australia; 2005.
20.
go back to reference Fildes R, Goodwin P, Lawrence M, Nikolopoulos K. Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. Int J Forecast. 2009;25(1):3–23.CrossRef Fildes R, Goodwin P, Lawrence M, Nikolopoulos K. Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. Int J Forecast. 2009;25(1):3–23.CrossRef
21.
go back to reference Lawrence M, Goodwin P, O’Connor M, Önkal D. Judgmental forecasting: a review of progress over the last 25 years. Int J Forecast. 2006;22(3):493–518.CrossRef Lawrence M, Goodwin P, O’Connor M, Önkal D. Judgmental forecasting: a review of progress over the last 25 years. Int J Forecast. 2006;22(3):493–518.CrossRef
22.
go back to reference Wright G, Lawrence MJ, Collopy F. The role and validity of judgment in forecasting. Int J Forecast. 1996;12(1):1–8.CrossRef Wright G, Lawrence MJ, Collopy F. The role and validity of judgment in forecasting. Int J Forecast. 1996;12(1):1–8.CrossRef
23.
go back to reference McGregor GR, Watkin HA, Cox M. Relationships between the seasonality of temperature and ischaemic heart disease mortality: implications for climate based health forecasting. Clim Res. 2004;25(3):253–63.CrossRef McGregor GR, Watkin HA, Cox M. Relationships between the seasonality of temperature and ischaemic heart disease mortality: implications for climate based health forecasting. Clim Res. 2004;25(3):253–63.CrossRef
24.
go back to reference Marno P, Bryden C, Bird W, Watkin HA. How different measures of cold weather affect chronic obstructive pulmonary disease (COPD) hospital admissions in London. Eur Respir Rev. 2006;15(101):185–6.CrossRef Marno P, Bryden C, Bird W, Watkin HA. How different measures of cold weather affect chronic obstructive pulmonary disease (COPD) hospital admissions in London. Eur Respir Rev. 2006;15(101):185–6.CrossRef
25.
go back to reference Shi L, van Meijgaard J, Fielding J. Forecasting diabetes prevalence in California: a microsimulation. Prev Chronic Dis. 2011;8(4):A80. (Epub 2011 Jun 2015).PubMed Shi L, van Meijgaard J, Fielding J. Forecasting diabetes prevalence in California: a microsimulation. Prev Chronic Dis. 2011;8(4):A80. (Epub 2011 Jun 2015).PubMed
26.
go back to reference Wargon M, Guidet B, Hoang TD, Hejblum G. A systematic review of models for forecasting the number of emergency department visits. Emerg Med J. 2009;26(6):395–9.PubMedCrossRef Wargon M, Guidet B, Hoang TD, Hejblum G. A systematic review of models for forecasting the number of emergency department visits. Emerg Med J. 2009;26(6):395–9.PubMedCrossRef
28.
go back to reference Ioannidis JPA. Limits to forecasting in personalized medicine: an overview. Int J Forecast. 2009;25(4):773–83.CrossRef Ioannidis JPA. Limits to forecasting in personalized medicine: an overview. Int J Forecast. 2009;25(4):773–83.CrossRef
29.
go back to reference van Meijgaard J, Fielding J, Shimkhada R, Eslami E, Cole B. Forecasting health outcomes related to air quality for placer county. Issue brief. Health Forecasting, UCLA School of Public Health; 2010. van Meijgaard J, Fielding J, Shimkhada R, Eslami E, Cole B. Forecasting health outcomes related to air quality for placer county. Issue brief. Health Forecasting, UCLA School of Public Health; 2010.
30.
go back to reference Sekhri N, Chisholm R, Longhi A, Evans P, Rilling M, Wilson E et al. Principles for forecasting demand for global health products. In: Global health forecasting working group, background paper. The Center for Global Development; 2006. Sekhri N, Chisholm R, Longhi A, Evans P, Rilling M, Wilson E et al. Principles for forecasting demand for global health products. In: Global health forecasting working group, background paper. The Center for Global Development; 2006.
31.
go back to reference Hoot NR, LeBlanc LJ, Jones I, Levin SR, Zhou C, Gadd CS, et al. Forecasting emergency department crowding: a discrete event simulation. Ann Emerg Med. 2008;52(2):116–25.PubMedCrossRef Hoot NR, LeBlanc LJ, Jones I, Levin SR, Zhou C, Gadd CS, et al. Forecasting emergency department crowding: a discrete event simulation. Ann Emerg Med. 2008;52(2):116–25.PubMedCrossRef
32.
go back to reference McCarthy ML, Zeger SL, Ding R, Aronsky D, Hoot NR, Kelen GD. The challenge of predicting demand for emergency department services. Acad Emerg Med. 2008;15(4):337–46.PubMedCrossRef McCarthy ML, Zeger SL, Ding R, Aronsky D, Hoot NR, Kelen GD. The challenge of predicting demand for emergency department services. Acad Emerg Med. 2008;15(4):337–46.PubMedCrossRef
33.
go back to reference Champion R, Kinsman LD, Lee GA, Masman KA, May EA, Mills TM, et al. Forecasting emergency department presentations. Aust Health Rev. 2007;31(1):83–90.PubMedCrossRef Champion R, Kinsman LD, Lee GA, Masman KA, May EA, Mills TM, et al. Forecasting emergency department presentations. Aust Health Rev. 2007;31(1):83–90.PubMedCrossRef
34.
go back to reference Gentry L, Calantone RJ, Cui SA. The forecasting classification grid: a typology for method selection. J Glob Bus Manag. 2006;2(1):48–60. Gentry L, Calantone RJ, Cui SA. The forecasting classification grid: a typology for method selection. J Glob Bus Manag. 2006;2(1):48–60.
35.
go back to reference Poikolainen K. A comment diagnosis as a means of health forecasting. Soc Sci Med Part A Med Psychol Med Sociol. 1979;13:165–6. Poikolainen K. A comment diagnosis as a means of health forecasting. Soc Sci Med Part A Med Psychol Med Sociol. 1979;13:165–6.
36.
go back to reference Henning KJ. What is syndromic surveillance? MMWR Morb Mortal Wkly Rep. 2004;53(Suppl):5–11.PubMed Henning KJ. What is syndromic surveillance? MMWR Morb Mortal Wkly Rep. 2004;53(Suppl):5–11.PubMed
37.
go back to reference Unkel S, Farrington CP, Garthwaite PH, Robertson C, Andrews N. Statistical methods for the prospective detection of infectious disease outbreaks: a review. J R Stat Soc Ser A. 2011;175(1):49–82. Unkel S, Farrington CP, Garthwaite PH, Robertson C, Andrews N. Statistical methods for the prospective detection of infectious disease outbreaks: a review. J R Stat Soc Ser A. 2011;175(1):49–82.
38.
go back to reference Burkom HS, Murphy SP, Shmueli G. Automated time series forecasting for biosurveillance. Stat Med. 2007;26(22):4202–18.PubMedCrossRef Burkom HS, Murphy SP, Shmueli G. Automated time series forecasting for biosurveillance. Stat Med. 2007;26(22):4202–18.PubMedCrossRef
39.
go back to reference Krishna S, Boren SA, Balas EA. Healthcare via cell phones: a systematic review. Telemed J E Health. 2009;15(3):231–40.PubMedCrossRef Krishna S, Boren SA, Balas EA. Healthcare via cell phones: a systematic review. Telemed J E Health. 2009;15(3):231–40.PubMedCrossRef
40.
go back to reference Jessup M, Wallis M, Boyle J, Crilly J, Lind J, Green D, et al. Implementing an emergency department patient admission predictive tool: insights from practice. J Health Organ Manag. 2010;24(3):306–18.PubMed Jessup M, Wallis M, Boyle J, Crilly J, Lind J, Green D, et al. Implementing an emergency department patient admission predictive tool: insights from practice. J Health Organ Manag. 2010;24(3):306–18.PubMed
41.
go back to reference Hemming D, Colman A, James P, Kaye N, Marno P, McNeall D, et al. Framework for COPD forecasting in the UK using weather and climate change predictions. IOP Conf Ser Earth Environ Sci. 2009;6:142021.CrossRef Hemming D, Colman A, James P, Kaye N, Marno P, McNeall D, et al. Framework for COPD forecasting in the UK using weather and climate change predictions. IOP Conf Ser Earth Environ Sci. 2009;6:142021.CrossRef
42.
go back to reference Oh J, Kim B. Prediction model for demands of the health meteorological information using a decision tree method. Asian Nurs Res. 2010;4(3):151–62.CrossRef Oh J, Kim B. Prediction model for demands of the health meteorological information using a decision tree method. Asian Nurs Res. 2010;4(3):151–62.CrossRef
43.
go back to reference van Meijgaard J, Fielding JE, Kominski GF. Assessing and forecasting population health: integrating knowledge and beliefs in a comprehensive framework. Public Health Rep. 2009;124(6):778–89.PubMed van Meijgaard J, Fielding JE, Kominski GF. Assessing and forecasting population health: integrating knowledge and beliefs in a comprehensive framework. Public Health Rep. 2009;124(6):778–89.PubMed
45.
go back to reference Sanders NR. Forecasting theory. In: Wiley encyclopedia of electrical and electronics engineering. New York: Wiley; 2001. Sanders NR. Forecasting theory. In: Wiley encyclopedia of electrical and electronics engineering. New York: Wiley; 2001.
46.
go back to reference Zaninotto P, Wardle H, Stamatakis E, Mindell J, Head J. Forecasting obesity to 2010. In: DOH: Publications and Statistics; 2006. Zaninotto P, Wardle H, Stamatakis E, Mindell J, Head J. Forecasting obesity to 2010. In: DOH: Publications and Statistics; 2006.
47.
go back to reference Soyiri IN, Reidpath DD. Evolving forecasting classifications and applications in health forecasting. Int J Gen Med. 2012;5(1):381–9.PubMed Soyiri IN, Reidpath DD. Evolving forecasting classifications and applications in health forecasting. Int J Gen Med. 2012;5(1):381–9.PubMed
48.
go back to reference Barnett AG, Dobson AJ. Analysing seasonal health data. Heidelberg: Springer; 2009. Barnett AG, Dobson AJ. Analysing seasonal health data. Heidelberg: Springer; 2009.
49.
go back to reference Chatfield C. The analysis of time series: an introduction. 6th ed. London: CRC Press; 2004. Chatfield C. The analysis of time series: an introduction. 6th ed. London: CRC Press; 2004.
50.
go back to reference Shumway RH, Stoffer DS. Time series analysis and its applications with R examples. 2nd ed. New York: Springer; 2006. Shumway RH, Stoffer DS. Time series analysis and its applications with R examples. 2nd ed. New York: Springer; 2006.
51.
go back to reference Zhang GP, Qi M. Neural network forecasting for seasonal and trend time series. Eur J Oper Res. 2005;160(2):501–14.CrossRef Zhang GP, Qi M. Neural network forecasting for seasonal and trend time series. Eur J Oper Res. 2005;160(2):501–14.CrossRef
52.
go back to reference Reis B, Mandl K. Time series modeling for syndromic surveillance. BMC Med Inform Decis Mak. 2003;3(1):2.PubMedCrossRef Reis B, Mandl K. Time series modeling for syndromic surveillance. BMC Med Inform Decis Mak. 2003;3(1):2.PubMedCrossRef
53.
go back to reference Sharma P, Chandra A, Kaushik SC. Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City. Environ Monit Assess. 2008;157(1–4):105–12.PubMed Sharma P, Chandra A, Kaushik SC. Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City. Environ Monit Assess. 2008;157(1–4):105–12.PubMed
54.
go back to reference Wang XK, Lu WZ. Seasonal variation of air pollution index: Hong Kong case study. Chemosphere. 2006;63(8):1261–72.PubMedCrossRef Wang XK, Lu WZ. Seasonal variation of air pollution index: Hong Kong case study. Chemosphere. 2006;63(8):1261–72.PubMedCrossRef
55.
go back to reference Armstrong JS. Findings from evidence-based forecasting: methods for reducing forecast error. Int J Forecast. 2006;22(3):583–98.CrossRef Armstrong JS. Findings from evidence-based forecasting: methods for reducing forecast error. Int J Forecast. 2006;22(3):583–98.CrossRef
56.
go back to reference Medina DC, Findley SE, Guindo B, Doumbia S. Forecasting non-stationary diarrhea, acute respiratory infection, and malaria time-series in Niono, Mali. PLoS One. 2007;2(11):e1181.PubMedCrossRef Medina DC, Findley SE, Guindo B, Doumbia S. Forecasting non-stationary diarrhea, acute respiratory infection, and malaria time-series in Niono, Mali. PLoS One. 2007;2(11):e1181.PubMedCrossRef
57.
go back to reference Mott JA, Mannino DM, Alverson CJ, Kiyu A, Hashim J, Lee T, et al. Cardiorespiratory hospitalizations associated with smoke exposure during the 1997, Southeast Asian forest fires. Int J Hyg Environ Health. 2005;208(1–2):75–85.PubMedCrossRef Mott JA, Mannino DM, Alverson CJ, Kiyu A, Hashim J, Lee T, et al. Cardiorespiratory hospitalizations associated with smoke exposure during the 1997, Southeast Asian forest fires. Int J Hyg Environ Health. 2005;208(1–2):75–85.PubMedCrossRef
58.
go back to reference Hyndman RJ, Koehler AB, Snyder RD, Grose S. A state space framework for automatic forecasting using exponential smoothing methods. Int J Forecast. 2002;18(3):439–54.CrossRef Hyndman RJ, Koehler AB, Snyder RD, Grose S. A state space framework for automatic forecasting using exponential smoothing methods. Int J Forecast. 2002;18(3):439–54.CrossRef
59.
60.
go back to reference Hao L, Naiman DQ (eds) Quantile regression. USA: Sage Publications, Inc; 2007. Hao L, Naiman DQ (eds) Quantile regression. USA: Sage Publications, Inc; 2007.
61.
go back to reference Yu K, Lu Z, Stander J. Quantile regression: applications and current research areas. J R Stat Soc Ser D. 2003;52(3):331–50.CrossRef Yu K, Lu Z, Stander J. Quantile regression: applications and current research areas. J R Stat Soc Ser D. 2003;52(3):331–50.CrossRef
63.
go back to reference Williams JS. Assessing the suitability of fractional polynomial methods in health services research: a perspective on the categorization epidemic. J Health Serv Res Policy. 2011;16(3):147–52.PubMedCrossRef Williams JS. Assessing the suitability of fractional polynomial methods in health services research: a perspective on the categorization epidemic. J Health Serv Res Policy. 2011;16(3):147–52.PubMedCrossRef
64.
go back to reference WHO. Health service delivery. In: WHO programmes and projects: health systems; 2010. WHO. Health service delivery. In: WHO programmes and projects: health systems; 2010.
66.
go back to reference Bradley VM. Placing emergency department crowding on the decision agenda. J Emerg Nurs. 2005;31(3):247–58.PubMedCrossRef Bradley VM. Placing emergency department crowding on the decision agenda. J Emerg Nurs. 2005;31(3):247–58.PubMedCrossRef
67.
go back to reference Derlet RW. Overcrowding in emergency departments: increased demand and decreased capacity. Ann Emerg Med. 2002;39(4):430–2.PubMedCrossRef Derlet RW. Overcrowding in emergency departments: increased demand and decreased capacity. Ann Emerg Med. 2002;39(4):430–2.PubMedCrossRef
68.
go back to reference Morrison DS, McLoone P. Changing patterns of hospital admission for asthma, 1981–97. Thorax. 2001;56(9):687–90.PubMedCrossRef Morrison DS, McLoone P. Changing patterns of hospital admission for asthma, 1981–97. Thorax. 2001;56(9):687–90.PubMedCrossRef
69.
go back to reference Lambe S, Washington DL, Fink A, Herbst K, Liu H, Fosse JS, et al. Trends in the use and capacity of California’s emergency departments, 1990–1999. Ann Emerg Med. 2002;39(4):389–96.PubMedCrossRef Lambe S, Washington DL, Fink A, Herbst K, Liu H, Fosse JS, et al. Trends in the use and capacity of California’s emergency departments, 1990–1999. Ann Emerg Med. 2002;39(4):389–96.PubMedCrossRef
70.
go back to reference Soyiri IN, Reidpath DD, Sarran C. Asthma length of stay in hospitals in London 2001–2006: demographic, diagnostic and temporal factors. PLoS One. 2011;6(11):e27184.PubMedCrossRef Soyiri IN, Reidpath DD, Sarran C. Asthma length of stay in hospitals in London 2001–2006: demographic, diagnostic and temporal factors. PLoS One. 2011;6(11):e27184.PubMedCrossRef
71.
go back to reference Buckeridge DL, Burkom H, Campbell M, Hogan WR, Moore AW. Algorithms for rapid outbreak detection: a research synthesis. J Biomed Inform. 2005;38(2):99–113.PubMedCrossRef Buckeridge DL, Burkom H, Campbell M, Hogan WR, Moore AW. Algorithms for rapid outbreak detection: a research synthesis. J Biomed Inform. 2005;38(2):99–113.PubMedCrossRef
72.
go back to reference Sun Y, Heng B, Seow Y, Seow E. Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg Med. 2009;9(1):1.PubMedCrossRef Sun Y, Heng B, Seow Y, Seow E. Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg Med. 2009;9(1):1.PubMedCrossRef
73.
go back to reference Schweigler LM, Desmond JS, McCarthy ML, Bukowski KJ, Ionides EL, Younger JG. Forecasting models of emergency department crowding. Acad Emerg Med. 2009;16(4):301–8.PubMedCrossRef Schweigler LM, Desmond JS, McCarthy ML, Bukowski KJ, Ionides EL, Younger JG. Forecasting models of emergency department crowding. Acad Emerg Med. 2009;16(4):301–8.PubMedCrossRef
74.
go back to reference Lisa MS, Jeffrey SD, Melissa LM, Kyle JB, Edward LI, John GY. Forecasting models of emergency department crowding. Acad Emerg Med. 2009;16(4):301–8.CrossRef Lisa MS, Jeffrey SD, Melissa LM, Kyle JB, Edward LI, John GY. Forecasting models of emergency department crowding. Acad Emerg Med. 2009;16(4):301–8.CrossRef
75.
go back to reference Myers MF, Rogers DJ, Cox J, Flahault A, Hay SI. Forecasting disease risk for increased epidemic preparedness in public health. Adv Parasitol. 2000;47:309–30.PubMedCrossRef Myers MF, Rogers DJ, Cox J, Flahault A, Hay SI. Forecasting disease risk for increased epidemic preparedness in public health. Adv Parasitol. 2000;47:309–30.PubMedCrossRef
76.
go back to reference Cooper R, O’Hara R. Patients’ and staffs’ experiences of an automated telephone weather forecasting service. J Health Serv Res Policy. 2010;15(Suppl 2):41–6.PubMedCrossRef Cooper R, O’Hara R. Patients’ and staffs’ experiences of an automated telephone weather forecasting service. J Health Serv Res Policy. 2010;15(Suppl 2):41–6.PubMedCrossRef
77.
go back to reference Maheswaran R, Pearson T, Hoysal N, Campbell MJ. Evaluation of the impact of a health forecast alert service on admissions for chronic obstructive pulmonary disease in Bradford and Airedale. J Public Health. 2009;32(1):97–102. Maheswaran R, Pearson T, Hoysal N, Campbell MJ. Evaluation of the impact of a health forecast alert service on admissions for chronic obstructive pulmonary disease in Bradford and Airedale. J Public Health. 2009;32(1):97–102.
Metadata
Title
An overview of health forecasting
Authors
Ireneous N. Soyiri
Daniel D. Reidpath
Publication date
01-01-2013
Publisher
Springer Japan
Published in
Environmental Health and Preventive Medicine / Issue 1/2013
Print ISSN: 1342-078X
Electronic ISSN: 1347-4715
DOI
https://doi.org/10.1007/s12199-012-0294-6

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