A scoping review of malaria forecasting: past work and future directions

BMJ Open - Tập 2 Số 6 - Trang e001992 - 2012
Kate Zinszer1, Aman Verma, Katia Charland, Timothy F. Brewer, John S. Brownstein, Zhuoyu Sun, David L. Buckeridge
1Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada

Tóm tắt

ObjectivesThere is a growing body of literature on malaria forecasting methods and the objective of our review is to identify and assess methods, including predictors, used to forecast malaria.DesignScoping review. Two independent reviewers searched information sources, assessed studies for inclusion and extracted data from each study.Information sourcesSearch strategies were developed and the following databases were searched: CAB Abstracts, EMBASE, Global Health, MEDLINE, ProQuest Dissertations & Theses and Web of Science. Key journals and websites were also manually searched.Eligibility criteria for included studiesWe included studies that forecasted incidence, prevalence or epidemics of malaria over time. A description of the forecasting model and an assessment of the forecast accuracy of the model were requirements for inclusion. Studies were restricted to human populations and to autochthonous transmission settings.ResultsWe identified 29 different studies that met our inclusion criteria for this review. The forecasting approaches included statistical modelling, mathematical modelling and machine learning methods. Climate-related predictors were used consistently in forecasting models, with the most common predictors being rainfall, relative humidity, temperature and the normalised difference vegetation index. Model evaluation was typically based on a reserved portion of data and accuracy was measured in a variety of ways including mean-squared error and correlation coefficients. We could not compare the forecast accuracy of models from the different studies as the evaluation measures differed across the studies.ConclusionsApplying different forecasting methods to the same data, exploring the predictive ability of non-environmental variables, including transmission reducing interventions and using common forecast accuracy measures will allow malaria researchers to compare and improve models and methods, which should improve the quality of malaria forecasting.

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Tài liệu tham khảo

Christophers, 1911, Epidemic malaria of the Punjab: with a note of a method of predicting epidemic years, Trans Committee Stud Malaria India, 2, 17

10.1186/1748-5908-5-69

Adimi, 2010, Towards malaria risk prediction in Afghanistan using remote sensing, Malar J, 9, 125, 10.1186/1475-2875-9-125

Chatterjee, 2009, Multi-step polynomial regression method to model and forecast malaria incidence, PLoS ONE, 4, e4726, 10.1371/journal.pone.0004726

10.1186/1475-2875-6-129

10.1111/j.1365-3156.2008.02166.x

Rahman, 2011, Modelling and prediction of malaria vector distribution in Bangladesh from remote-sensing data, Int J Remote Sens, 32, 1233, 10.1080/01431160903527447

Roy, 2011, Theoretical investigation of malaria prevalence in two Indian cities using the response surface method, Malar J, 10, 301, 10.1186/1475-2875-10-301

10.1186/1475-2875-3-44

10.1186/1475-2875-9-185

Yacob, 1947, Preliminary forecasts of the incidence of malaria in the Punjab, Ind J Malariol, 1, 491

Yan, 2007, Establishment of a dynamic model of malaria outbreak in Chongqing municipality, J Trop Med (Guangzhou), 7, 801

10.1046/j.1365-3156.2002.00924.x

Briët, 2008, Models for short term malaria prediction in Sri Lanka, Malar J, 7, 76, 10.1186/1475-2875-7-76

Liu, 2011, Epidemiological analysis on malaria incidence in China from 2004 to 2009 by time series model, Chin J Vector Biol Control, 22, 134

10.1186/1475-2875-9-251

Wen, 2004, Prediction of malaria incidence in malaria epidemic area with time series model, J Fourth Military Med Univ, 25, 507

Zhang, 2010, Meteorological variables and malaria in a Chinese temperate city: a twenty-year time-series data analysis, Environ Int, 36, 439, 10.1016/j.envint.2010.03.005

Zhou, 2007, Application of ARIMA model on prediction of malaria incidence, J Pathogen Biol, 2, 284

Zhu, 2007, Study on the feasibility for ARIMA model application to predict malaria incidence in an unstable malaria area, Chin J Parasitol Parasitic Dis, 25, 232

Gaudart, 2009, Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali, Malar J, 8, 61, 10.1186/1475-2875-8-61

Laneri, 2010, Forcing versus feedback: epidemic malaria and monsoon rains in northwest India, PLoS Comput Biol, 6, 1, 10.1371/journal.pcbi.1000898

Cunha, 2010, Use of an artificial neural network to predict the incidence of malaria in the city of Canta, state of Roraima, Rev Soc Brasil Med Trop, 43, 567, 10.1590/S0037-86822010000500019

Gao, 2003, Study on meteorological factors-based neural network model of malaria, Chin J Epidemiol, 24, 831

Kiang, 2006, Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand, Geospat Health, 1, 71, 10.4081/gh.2006.282

Fang, 1991, Interval division, forecasting and decline tendency estimation model of malaria incidence in Xuzhou City, Chin J Parasitol Parasitic Dis, 9, 284

Gao, 2007, Establishment and estimation of a GM (1,1) grey model for forecasting of malaria epidemic situation in Shenzhen Longgang areas, J Pathogen Biol, 2, 357

Guo, 2011, A study on the trend of malaria incidence in China in the recent 20 years with GM (1,1), J Trop Med (Guangzhou), 11, 639

Gill, 1927, The forecasting of malaria epidemics with special reference to the malaria forecast for the year 1926, Ind J Med Res, 15, 265

Medina, 2007, Forecasting non-stationary diarrhea, acute respiratory infection, and malaria time-series in Niono, Mali, PLoS One, 2, 1, 10.1371/journal.pone.0001181

Xu, 2005, The application of GM (1,1) grey model in the forecasting of malaria epidemic situation, Chin J Parasitic Dis Control, 18, 178

Box GEP Jenkins GM Reinsel GC . Time series analysis: forecasting and control. Hoboken, NJ: John Wiley & Sons, 2008.

Chatfield, 1978, The Holt-Winters forecasting procedure, J Roy Statist Soc, 27, 264

MacDonald G . The epidemiology and control of malaria. London: Oxford University Press, 1957.

Anderson JA . An introduction to neural networks. Cambridge, MA: The MIT Press, 1995.

Darlington RB . A comparison to ARIMA. http://www.psych.cornell.edu/darlington/series/series2.htm (accessed 30 May 2012).

Chatfield C . The analysis of time series: an introduction. London: Chapman & Hall, 2004.

Shumway RH Stoffer DS . Time series analysis and its applications: with R examples. New York: Springer, 2006.

Zeileis, 2004, Econometric computing with HC and HAC covariance matrix estimators, J Statist Software, 11, 1, 10.18637/jss.v011.i10

Deng, 1989, Introduction to Grey system theory, J Grey System, 1, 1

Lin, 2005, A gray system modelling approach to the prediction of calibration intervals, IEEE Trans Instr Measure, 54, 297, 10.1109/TIM.2004.840234

Tseng, 2001, Applied hybrid grey model to forecast seasonal time series, Technol Forecasting Soc Change, 67, 291, 10.1016/S0040-1625(99)00098-0

10.1016/0001-706X(91)90026-G

Chatfield, 1993, Neural networks: forecasting breakthrough or passing fad?, Int J Forecasting, 9, 1, 10.1016/0169-2070(93)90043-M

10.1016/S0895-4356(96)00002-9

Chatfield, 1997, Forecasting in the 1990s, J Roy Statist Soc, 46, 461

Jose, 2008, Simple robust averages of forecasts: Some empirical results, Int J Forecasting, 24, 163, 10.1016/j.ijforecast.2007.06.001

10.1016/S1471-4922(00)01763-3

10.1214/10-STS330

Hyndman RJ Athanasopoulos G . Forecasting: principles and practice. 2012. http://otexts.com/fpp/ (accessed 3 May 2012).

10.1016/j.ijforecast.2006.03.001

Armstrong, 1992, Error measures for generalizing about forecasting methods—empirical comparisons, Int J Forecasting, 8, 69, 10.1016/0169-2070(92)90008-W

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BMJ Open

Tập 2 Số 6

e001992

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