Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia
The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito- borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level d...
- Autores:
-
Zhao, Naizhuo
Charland, Katia
Carabali, Mabel
Nsoesie, Elaine
Maheu-Giroux, Mathieu
Rees, Erin
Yuan, Mengru
Garcia, Cesar
Jaramillo Ramírez, Gloria Isabel
Zinser, Kate
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2020
- Institución:
- Universidad Cooperativa de Colombia
- Repositorio:
- Repositorio UCC
- Idioma:
- OAI Identifier:
- oai:repository.ucc.edu.co:20.500.12494/32758
- Acceso en línea:
- https://hdl.handle.net/20.500.12494/32758
- Palabra clave:
- Arbovirus
Prediction
Network
Arbovirus
Prediction
Network
- Rights
- openAccess
- License
- Atribución – No comercial – Sin Derivar
| id |
COOPER2_db84bbf5cf4c62d092ad6f6c52f19e5c |
|---|---|
| oai_identifier_str |
oai:repository.ucc.edu.co:20.500.12494/32758 |
| network_acronym_str |
COOPER2 |
| network_name_str |
Repositorio UCC |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia |
| title |
Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia |
| spellingShingle |
Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia Arbovirus Prediction Network Arbovirus Prediction Network |
| title_short |
Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia |
| title_full |
Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia |
| title_fullStr |
Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia |
| title_full_unstemmed |
Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia |
| title_sort |
Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia |
| dc.creator.fl_str_mv |
Zhao, Naizhuo Charland, Katia Carabali, Mabel Nsoesie, Elaine Maheu-Giroux, Mathieu Rees, Erin Yuan, Mengru Garcia, Cesar Jaramillo Ramírez, Gloria Isabel Zinser, Kate |
| dc.contributor.author.none.fl_str_mv |
Zhao, Naizhuo Charland, Katia Carabali, Mabel Nsoesie, Elaine Maheu-Giroux, Mathieu Rees, Erin Yuan, Mengru Garcia, Cesar Jaramillo Ramírez, Gloria Isabel Zinser, Kate |
| dc.subject.spa.fl_str_mv |
Arbovirus Prediction Network |
| topic |
Arbovirus Prediction Network Arbovirus Prediction Network |
| dc.subject.other.spa.fl_str_mv |
Arbovirus Prediction Network |
| description |
The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito- borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department’s data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-09-24 |
| dc.date.accessioned.none.fl_str_mv |
2021-01-22T00:21:55Z |
| dc.date.available.none.fl_str_mv |
2021-01-22T00:21:55Z |
| dc.type.none.fl_str_mv |
Artículos Científicos |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| format |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| status_str |
publishedVersion |
| dc.identifier.uri.spa.fl_str_mv |
10.1371/journal.pntd.0008056 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12494/32758 |
| dc.identifier.bibliographicCitation.spa.fl_str_mv |
Zhao, N., Charland, K., Carabali, M., Nsoesie, E.O., Maheu-Giroux, M., Rees, E., et al. (2020) Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLoS Negl Trop Dis 14(9): e0008056. https://doi.org/10.1371/ journal.pntd.0008056 |
| identifier_str_mv |
10.1371/journal.pntd.0008056 Zhao, N., Charland, K., Carabali, M., Nsoesie, E.O., Maheu-Giroux, M., Rees, E., et al. (2020) Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLoS Negl Trop Dis 14(9): e0008056. https://doi.org/10.1371/ journal.pntd.0008056 |
| url |
https://hdl.handle.net/20.500.12494/32758 |
| dc.relation.ispartofjournal.spa.fl_str_mv |
PLos Neglected Tropical Diseases |
| dc.relation.references.spa.fl_str_mv |
Lambrechts L, Scott TW, Gubler DJ. Consequences of the expanding global distribution of Aedes albopictus for dengue virus transmission. PLoS Neglected Tropical Diseases 2010; 4(5): e646. https://doi. org/10.1371/journal.pntd.0000646 PMID: 20520794 Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CLet.al. The global distribution and burden of dengue. Nature 2013; 496:504–507. https://doi.org/10.1038/nature12060 PMID: 23563266 Morin CW, Comrie AC, Ernst K. Climate and dengue transmission: evidence and implications. Environmental Health Perspectives 2013; 121(11–12): 1264. https://doi.org/10.1289/ehp.1306556 PMID: 24058050 Shepard DS, Undurraga EA, Hallasa YA, Stanaway JD. The global economic burden of dengue: a systematic analysis. Lancet Infectious Diseases 2016; 16:935–941. https://doi.org/10.1016/S1473-3099 (16)00146-8 PMID: 27091092 Soyiri IN, Reidpath DD. An overview of health forecasting. Environmental Health and Preventive Medicine 2013; 18(1):1–9. https://doi.org/10.1007/s12199-012-0294-6 PMID: 22949173 Racloz V, Ramsey R, Tong S, Hu W. Surveillance of dengue fever virus: A review of epidemiological models and early warning systems. PLoS Neglected Tropical Diseases 2012; 6(5):e1648. https://doi. org/10.1371/journal.pntd.0001648 PMID: 22629476 Gambhir S, Malik SK, Kumar Y, The diagnosis of dengue disease: An evaluation of three machine learning approaches. International Journal of Healthcare Information Systems and Informatics 2018; 13:1–19. https://doi.org/10.4018/ijhisi.2018040101 PMID: 32913425 Naish S, Dale P, Mackenzie JS, McBride J, Mengersen K, Tong S, Climate change and dengue: a critical and systematic review of quantitative modelling approaches. BMC Infectious Diseases 2014; 14:167. https://doi.org/10.1186/1471-2334-14-167 PMID: 24669859 Gharbi M, Quenel P, Gustave J, Cassadou S, Ruche GL, Girdary L, et al. Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors. BMC Infectious Diseases 2011; 11:166. https://doi.org/10.1186/1471-2334-11-166 PMID: 21658238 Hu W, Clements A, Williams G, Tong S, Dengue fever and El Niño/Southern Oscillation in Queensland, Australia: a time series predictive model. Occupational & Environmental Medicine 2010; 67:307–311. Dom NC, Hassan AA, Latif ZA, Ismail R, Generating temporal model using climate variables for the prediction of dengue cases in Subang Jaya, Malasia. Asian Pacific Journal of Tropical Disease 2013; 3:352–361. Cortes F, Turchi Martelli CM, Arraes de Alencar Ximenes R, Montarroyos UR, Siqueira Junior JB, Gonc¸alves Cruz O, et al. Time series analysis of dengue surveillance data in two Brazilian cities. Acta Tropica. 2018; 182:190–7. https://doi.org/10.1016/j.actatropica.2018.03.006 PMID: 29545150 Johansson MA, Reich NG, Hota A, Brownstein JS, Santillana M, Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico. Scientific Reports 2016; 6:33707. https://doi.org/10.1038/srep33707 PMID: 27665707 Niu M, Wang Y, Sun S, Li Y, A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting. Atmospheric Environment 2016; 134:168–180. Chen M-Y, Chen B-T, A hybrid fuzzy time series model based on granular computing for stock price forecasting. Information Sciences 2015; 294:227–241. Wang P, Zhang H, Qin Z, Zhang G, A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting. Atmospheric Pollution Research 2017; 8: 850–860. Zhao N, Liu Y, Vanos JK, Cao G, Day-of-week and seasonal patterns of PM2.5 concentrations over the United States: Time-series analyses using the Prophet procedure. Atmospheric Environment 2018; 192:116–127. Breiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Statistical Science 2001; 16(3): 199–231. Murphy KP. Machine Learning: a probabilistic perspective. MIT Press, 2012. Guo P, Liu T, Zhang Q, Wang L, Xiao J, Zhang Q, et al. Developing a dengue forecast model using machine learning: A case study in China. PLoS Neglected Tropical Diseases 2017; 11:e0005973. https://doi.org/10.1371/journal.pntd.0005973 PMID: 29036169 Scavuzzo JM, Trucco F, Espinosa M, Tauro CB, Abril M, Scavuzzo CM, et al. Modeling dengue vector population using remotely sensed data and machine learning. Acta Tropica 2018; 185:167–175. https://doi.org/10.1016/j.actatropica.2018.05.003 PMID: 29777650 Althouse BM, Ng YY, Cummings DAT, Prediction of dengue incidence using serach query surveillance. PLoS Neglected Tropical Diseases 2011; 5:e1258. https://doi.org/10.1371/journal.pntd.0001258 PMID: 21829744 Laureano-Rosario AE, Duncvan AP, Mendez-Lazaro PA, Garcia-Rejon JE, Gomez-Carro S, Farfan-Ale J, et al. Application of artificial neural networks for dengue fever outbreak predictions in the northwest coast of Yucatan, Mexico and San Juan, Puerto Rico. Tropical Medicine and Infectious Disease 2018; 3:5. Raczko E, Zagajewski B, Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. European Journal of Remote Sensing 2017; 50:144–154. Meyer H, Kulhnlein M, Appelhans T, Nauss T, Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals. Atmospheric Research 2016; 169:424–433. Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, Chica-Rivas M, Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews 2015; 71:804–818. Statnikov A, Wang L, Aliferis CF, A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 2008; 9:319. https://doi.org/ 10.1186/1471-2105-9-319 PMID: 18647401 Nsoesie EO, Beckman R, Marathe M, Lewis B, Prediction of an epidemic curve: A supervised classification approach. Statistical communications in infectious diseases. 2011; 3(1):5. https://doi.org/10.2202/ 1948-4690.1038 PMID: 22997545 Vasquez P, Loria A, Sanchez F, Barboza LA, Climate-driven statistical models as effective predictors of local dengue incidence in Costa Rica: A generalized additive model and random forest approach. arXiv 2019; 1907.13095. Olmoguez ILG, Catindig MAC, Amongos MFL, Lazan AF, Developing a dengue forecasting model: A case study in Iligan city. International Journal of Advanced Computer Science and Applications 2019; 10(9):281–286. Carvajal TM, Viacrusis KM, Hernandez LFT, Ho HT, Amalin DM, Watanabe K, Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines. BMC Infectious Diseases 2018; 18:183. https://doi.org/10.1186/s12879-018-3066- 0 PMID: 29665781 Rehman NA, Kalyanaraman S, Ahmad T, Pervaiz F, Saif U, Subramanian L, Fine-grained dengue forecasting using telephone triage services. Science Advances 2016; 2(7): e1501215. https://doi.org/10. 1126/sciadv.1501215 PMID: 27419226 Freeze J, Erraguntla M, Verma A, Data integration and predictive analysis system for disease prophylaxis: Incorporating dengue fever forecasts. Proceedings of the 51st Hawaii International Conference on System Science 2018; 913–922. Dinh L, Chowell G, Rothenberg R, Growth scaling for the early dynamics of HIV/AIDS epidemics in Brazil and the influence of socio-demographic factors. Journal of Theoretical Biology 2018; 442:79–86. https://doi.org/10.1016/j.jtbi.2017.12.030 PMID: 29330056 Chretien J-P, Riley S, George DB, Mathematical modeling of the West Aftica Ebola epidemic. eLIFE 2015; 4:e09186. https://doi.org/10.7554/eLife.09186 PMID: 26646185 Cardona-Ospina JA, Villamil-Go´mez WE, Jimenez-Canizales CE, Castañeda-Herna´ndez DM, Rodrı´- guez-Morales AJ. Estimating the burden of disease and the economic cost attributable to chikungunya, Colombia, 2014. Transactions of the Royal Society of Tropical Medicine and Hygiene 2015; 109 (12):793–802. https://doi.org/10.1093/trstmh/trv094 PMID: 26626342 Villar LA, Rojas DP, Besada-Lombana S, Sarti E. Epidemiological trends of dengue disease in Colombia (2000–2011): a systematic review. PLoS Neglected Tropical Diseases 2015; 9(3): e0003499. https://doi.org/10.1371/journal.pntd.0003499 PMID: 25790245 Ospina Martinez ML, Martinez Duran ME, Pacheco Garcı´a OE, Bonilla HQ, Pe´rez NT., Protocolo de vigilancia en salud pu´blica enfermedad por virus Zika. PRO-R02.056. Bogota (Colombia): Instituto Nacional de Salud, 2017. Available from: http://bvs.minsa.gob.pe/local/MINSA/3449.pdf (last accessed December 16, 2019). Beketov MA, Yurchenko YA, Belevich OE, Liess M, What environmental factors are important determinants of structure, species richness, and abundance of mosquito assemblages? Journal of Medical Entomology 2010; 47:129–139. https://doi.org/10.1603/me09150 PMID: 20380292 Joyce RJ CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology 2004; 5:487– 503. Koyadun S, Butraporn P, Kittayapong P, Ecologic and sociodemographic risk determinants for dengue transmission in urban areas in Thailand. Interdisciplinary Perspectives on Infectious Diseases 2012; 2012:907494. https://doi.org/10.1155/2012/907494 PMID: 23056042 Reiter P, Climate change and mosquito-borne disease. Environmental Health Perspectives 2001; 109 (supplement 1):141–161. https://doi.org/10.1289/ehp.01109s1141 PMID: 11250812 Soghaier MA, Himatt S, Osman KE, Okoued SI, Seidahmed OE, Beatty ME, et al., Cross-sectional community-based study of the socio-demographic factors associated with the prevalence of dengue in the eastern part of Sudan in 2011. BMC Public Health 2015; 15:558. https://doi.org/10.1186/s12889- 015-1913-0 PMID: 26084275 Kannan Maharajan M, Rajiah K, Singco Belotindos JA, Bases MS. Social determinants predicting the knowledge, attitudes, and practices of women toward zika virus infection Frontiers in Public Health 2020; 8:170. https://doi.org/10.3389/fpubh.2020.00170 PMID: 32582602 Couse Quinn S, Kumar S. Health inequalities and infectious disease epidemics: A challenge for global health security. Biosecurity and Bioterrorism: Biodefense Srategy, Practice, and Science 2014; 12 (5):263–273. Breiman L, Random forests. Machine learning 2001; 45(1):5–32. Hulme M, New M. Dependence of large-scale precipitation climatologies on temporal and spatial sampling. Journal of Climate, 1997; 10:1099–1113 Papacharalampous GA, Tyralis H, Evaluation of random forests and prophet for daily streamflow forecasting. Advances in Geosciences 2018; 45:201–208. Lu L, Lin H, Tian L, Yang W, Sun J, Liu Q, Time series analysis of dengue fever and weather in Guangzhou, China, BMC Public Health 2009; 9:395. https://doi.org/10.1186/1471-2458-9-395 PMID: 19860867 Chen S-C. Liao C-M, Chio C-P, Chou H-H, You S-H, Cheng Y-H, lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: Insights from a statistical analysis. Science of The Total Environment 2010; 408(19):469–4075. Cheong YL, Burkart K, Leitao PJ, Lakes T, Assessing weather effects on dengue disease in Malaysia, International Journal of Environmental Research and Public Health 2013; 10(12):6319–6334. https:// doi.org/10.3390/ijerph10126319 PMID: 24287855 Chang K, Chen C-D, Shih C-M, Lee T-C, Wu M-T, Wu D-C, et al., Time-lagging interplay effect and excess risk of meteorological/mosquito parameters and petrochemical gas explosion on dengue incidence. Scientific reports 2016; 6:35028. https://doi.org/10.1038/srep35028 PMID: 27733774 Chen Y, Ong JHY, Rajarethinam J, Yap G, Ng LC, Cook AR. Neighbourhood level real-time forecasting of dengue cases in tropical urban Singapore. BMC Medicine 2018; 16(1):129. https://doi.org/10.1186/ s12916-018-1108-5 PMID: 30078378 Eastin MD, Delmelle E, Casas I, Wexler J, Self C, Intra-and interseasonal autoregressive prediction of dengue outbreaks using local weather and regional climate for a tropical environment in Colombia. The American Journal of Tropical Medicine and Hygiene 2014; 91(3):598–610. https://doi.org/10.4269/ ajtmh.13-0303 PMID: 24957546 Bostan N, Javed S, Amen N, Eqani SAMAS, Tahir F, Bokhari H, Dengue fever virus in Pakistan: effects of seasonal pattern and temperature change on distribution of vector and virus. Reviews in Medical Virology 2017; 27(1):e1899. Oidtman RJ, Lai S, Huang Z, Yang J, Siraj AS, Reiner RC, et al., Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China, Nature Communications 2019; 10:1148. https://doi.org/10.1038/s41467-019-09035-x PMID: 30850598 Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. Spring, Berlin, 2008. Liaw A, Wiener M. Breiman and Culter’s random forests for classification and regression. 2018. Available from: https://cran.r-project.org/web/packages/randomForest/randomForest.pdf (last accessed May 7, 2020) Peng Z, Letu H, Wang T, Shi C, Zhao C, Tana G, Zhao N, Dai T, Tang R, Shang H, Shi J, Chen L. Estimation of shortwave solar radiation using the artificial neural network from Himawari-8 satellite imagery over China. Journal of Quantitative Spectroscopy and Radiative Transfer 2020; 240: 106672. Hyndman RJ, Khandakar Y. Automatic time series forecasting: The forecast package for R. Journal of Statistical Software 2008; 27: 1–22. Reich NG, Lessler J, Sakrejda K, Lauer SA, Iamsirithaworn S, Cummings DAT. Case study in evaluating time series prediction models using the relative mean absolute error. The American Statistician 2016; 70: 285–292. https://doi.org/10.1080/00031305.2016.1148631 PMID: 28138198 Liu Y, Cao G, Zhao N, Mulligan K, Ye X. Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach. Environmental Pollution 2018; 235: 272–282. https:// doi.org/10.1016/j.envpol.2017.12.070 PMID: 29291527 Grziwotz F, Strauß JF, Hsieh C-h, Telschow A. Empirical dynamic modelling identifies different responses of Aedes Polynesiensis subpopulations to natural environmental variables. Scientific Reports 2018; 8: 16768. https://doi.org/10.1038/s41598-018-34972-w PMID: 30425277 da Cruz Ferreira DA, Degener CM, de Almeida Marques-Toledo C, Bendati MM, Fetzer LO, Teixeira CP, Eiras AE. Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika. Parasites Vectors 2017; 10: 78. https://doi.org/10.1186/s13071-017-2025-8 PMID: 28193291 Manica M, Filipponi F, D’Alessandro A, Screti A, Neteler M, Rosà R, et al. Spatial and Temporal Hot Spots of Aedes albopictus Abundance inside and outside a South European Metropolitan Area. PLoS Neglected Tropical Diseases 2016; 10(6): e0004758. https://doi.org/10.1371/journal.pntd.0004758 PMID: 27333276 Mulligan K, Dixon J, Sinn C-L J, Elliott SJ. Is dengue a disease of poverty? A systematic review. Pathogens and Global Health 2015; 109(1): 10–18. https://doi.org/10.1179/2047773214Y.0000000168 PMID: 25546339 Tapia-Conyer R, Me´ndez-Galva´n JF, Gallardo-Rinco´n H. The growing burden of dengue in Latin America. Journal of Clinical Virology 2009; 46: S3–S6. https://doi.org/10.1016/S1386-6532(09)70286-0 PMID: 19800563 Adams EA, Boateng GO, Amoyaw JA. Socioeconomic and demographic predictors of potable water and sanitation access in Ghana. Social Indicators Research 2016; 126(2): 673–687. de Janvry A, Sadoulet E. Growth, poverty, and inequality in Latin America: A causal analysis, 1970–94. The review of Income and Wealth 2000; 46(3): 267–287. Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E. Deep learning applications and challenges in big data analytics. Journal of Big Data 2015; 2:1. Ong J, Liu X, Rajarethinam J, Kok SY, Liang S, Tang CS, et al., Mapping dengue risk in Singapore using random forest. PLoS Neglected Tropical Diseases 2018; 12(6):e0006587. https://doi.org/10. 1371/journal.pntd.0006587 PMID: 29912940 Williams RJ, Zipser D, A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1989; 1(2):270–280. |
| dc.rights.license.none.fl_str_mv |
Atribución – No comercial – Sin Derivar |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Atribución – No comercial – Sin Derivar http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
1-16 |
| dc.coverage.temporal.spa.fl_str_mv |
14(9) |
| dc.publisher.spa.fl_str_mv |
Universidad Cooperativa de Colombia, Facultad de Ciencias de la Salud, Medicina, Villavicencio |
| dc.publisher.program.spa.fl_str_mv |
Medicina |
| dc.publisher.place.spa.fl_str_mv |
Villavicencio |
| institution |
Universidad Cooperativa de Colombia |
| bitstream.url.fl_str_mv |
https://repository.ucc.edu.co/bitstreams/77666efc-7737-42a2-94cc-b5a37d97e89b/download https://repository.ucc.edu.co/bitstreams/113a559e-bf6c-4d82-8f56-c82a966facf1/download https://repository.ucc.edu.co/bitstreams/367578f4-40b4-4442-8d3c-2787e70d169b/download https://repository.ucc.edu.co/bitstreams/8ba905e2-8194-47e4-bf4c-54a14da95a4e/download |
| bitstream.checksum.fl_str_mv |
3bce4f7ab09dfc588f126e1e36e98a45 b2ce7c8ff6451109859fcef3a1f70278 60b17a273fb2e3be9688997747879ad9 881ade4dffae608891afa318aa4b88a2 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Cooperativa de Colombia |
| repository.mail.fl_str_mv |
bdigital@metabiblioteca.com |
| _version_ |
1811565656089296896 |
| spelling |
Zhao, NaizhuoCharland, KatiaCarabali, MabelNsoesie, ElaineMaheu-Giroux, MathieuRees, ErinYuan, MengruGarcia, CesarJaramillo Ramírez, Gloria IsabelZinser, Kate14(9)2021-01-22T00:21:55Z2021-01-22T00:21:55Z2020-09-2410.1371/journal.pntd.0008056https://hdl.handle.net/20.500.12494/32758Zhao, N., Charland, K., Carabali, M., Nsoesie, E.O., Maheu-Giroux, M., Rees, E., et al. (2020) Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLoS Negl Trop Dis 14(9): e0008056. https://doi.org/10.1371/ journal.pntd.0008056The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito- borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department’s data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends.kate.zinszer@umontreal.cagloria.jaramillor@campusucc.edu.cocesar.garcia@campusucc.edu.co1-16Universidad Cooperativa de Colombia, Facultad de Ciencias de la Salud, Medicina, VillavicencioMedicinaVillavicencioArbovirusPredictionNetworkArbovirusPredictionNetworkMachine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in ColombiaArtículos Científicoshttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAtribución – No comercial – Sin Derivarinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2PLos Neglected Tropical DiseasesLambrechts L, Scott TW, Gubler DJ. Consequences of the expanding global distribution of Aedes albopictus for dengue virus transmission. PLoS Neglected Tropical Diseases 2010; 4(5): e646. https://doi. org/10.1371/journal.pntd.0000646 PMID: 20520794Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CLet.al. The global distribution and burden of dengue. Nature 2013; 496:504–507. https://doi.org/10.1038/nature12060 PMID: 23563266Morin CW, Comrie AC, Ernst K. Climate and dengue transmission: evidence and implications. Environmental Health Perspectives 2013; 121(11–12): 1264. https://doi.org/10.1289/ehp.1306556 PMID: 24058050Shepard DS, Undurraga EA, Hallasa YA, Stanaway JD. The global economic burden of dengue: a systematic analysis. Lancet Infectious Diseases 2016; 16:935–941. https://doi.org/10.1016/S1473-3099 (16)00146-8 PMID: 27091092Soyiri IN, Reidpath DD. An overview of health forecasting. Environmental Health and Preventive Medicine 2013; 18(1):1–9. https://doi.org/10.1007/s12199-012-0294-6 PMID: 22949173Racloz V, Ramsey R, Tong S, Hu W. Surveillance of dengue fever virus: A review of epidemiological models and early warning systems. PLoS Neglected Tropical Diseases 2012; 6(5):e1648. https://doi. org/10.1371/journal.pntd.0001648 PMID: 22629476Gambhir S, Malik SK, Kumar Y, The diagnosis of dengue disease: An evaluation of three machine learning approaches. International Journal of Healthcare Information Systems and Informatics 2018; 13:1–19. https://doi.org/10.4018/ijhisi.2018040101 PMID: 32913425Naish S, Dale P, Mackenzie JS, McBride J, Mengersen K, Tong S, Climate change and dengue: a critical and systematic review of quantitative modelling approaches. BMC Infectious Diseases 2014; 14:167. https://doi.org/10.1186/1471-2334-14-167 PMID: 24669859Gharbi M, Quenel P, Gustave J, Cassadou S, Ruche GL, Girdary L, et al. Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors. BMC Infectious Diseases 2011; 11:166. https://doi.org/10.1186/1471-2334-11-166 PMID: 21658238Hu W, Clements A, Williams G, Tong S, Dengue fever and El Niño/Southern Oscillation in Queensland, Australia: a time series predictive model. Occupational & Environmental Medicine 2010; 67:307–311.Dom NC, Hassan AA, Latif ZA, Ismail R, Generating temporal model using climate variables for the prediction of dengue cases in Subang Jaya, Malasia. Asian Pacific Journal of Tropical Disease 2013; 3:352–361.Cortes F, Turchi Martelli CM, Arraes de Alencar Ximenes R, Montarroyos UR, Siqueira Junior JB, Gonc¸alves Cruz O, et al. Time series analysis of dengue surveillance data in two Brazilian cities. Acta Tropica. 2018; 182:190–7. https://doi.org/10.1016/j.actatropica.2018.03.006 PMID: 29545150Johansson MA, Reich NG, Hota A, Brownstein JS, Santillana M, Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico. Scientific Reports 2016; 6:33707. https://doi.org/10.1038/srep33707 PMID: 27665707Niu M, Wang Y, Sun S, Li Y, A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting. Atmospheric Environment 2016; 134:168–180.Chen M-Y, Chen B-T, A hybrid fuzzy time series model based on granular computing for stock price forecasting. Information Sciences 2015; 294:227–241.Wang P, Zhang H, Qin Z, Zhang G, A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting. Atmospheric Pollution Research 2017; 8: 850–860.Zhao N, Liu Y, Vanos JK, Cao G, Day-of-week and seasonal patterns of PM2.5 concentrations over the United States: Time-series analyses using the Prophet procedure. Atmospheric Environment 2018; 192:116–127.Breiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Statistical Science 2001; 16(3): 199–231.Murphy KP. Machine Learning: a probabilistic perspective. MIT Press, 2012.Guo P, Liu T, Zhang Q, Wang L, Xiao J, Zhang Q, et al. Developing a dengue forecast model using machine learning: A case study in China. PLoS Neglected Tropical Diseases 2017; 11:e0005973. https://doi.org/10.1371/journal.pntd.0005973 PMID: 29036169Scavuzzo JM, Trucco F, Espinosa M, Tauro CB, Abril M, Scavuzzo CM, et al. Modeling dengue vector population using remotely sensed data and machine learning. Acta Tropica 2018; 185:167–175. https://doi.org/10.1016/j.actatropica.2018.05.003 PMID: 29777650Althouse BM, Ng YY, Cummings DAT, Prediction of dengue incidence using serach query surveillance. PLoS Neglected Tropical Diseases 2011; 5:e1258. https://doi.org/10.1371/journal.pntd.0001258 PMID: 21829744Laureano-Rosario AE, Duncvan AP, Mendez-Lazaro PA, Garcia-Rejon JE, Gomez-Carro S, Farfan-Ale J, et al. Application of artificial neural networks for dengue fever outbreak predictions in the northwest coast of Yucatan, Mexico and San Juan, Puerto Rico. Tropical Medicine and Infectious Disease 2018; 3:5.Raczko E, Zagajewski B, Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. European Journal of Remote Sensing 2017; 50:144–154.Meyer H, Kulhnlein M, Appelhans T, Nauss T, Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals. Atmospheric Research 2016; 169:424–433.Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, Chica-Rivas M, Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews 2015; 71:804–818.Statnikov A, Wang L, Aliferis CF, A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 2008; 9:319. https://doi.org/ 10.1186/1471-2105-9-319 PMID: 18647401Nsoesie EO, Beckman R, Marathe M, Lewis B, Prediction of an epidemic curve: A supervised classification approach. Statistical communications in infectious diseases. 2011; 3(1):5. https://doi.org/10.2202/ 1948-4690.1038 PMID: 22997545Vasquez P, Loria A, Sanchez F, Barboza LA, Climate-driven statistical models as effective predictors of local dengue incidence in Costa Rica: A generalized additive model and random forest approach. arXiv 2019; 1907.13095.Olmoguez ILG, Catindig MAC, Amongos MFL, Lazan AF, Developing a dengue forecasting model: A case study in Iligan city. International Journal of Advanced Computer Science and Applications 2019; 10(9):281–286.Carvajal TM, Viacrusis KM, Hernandez LFT, Ho HT, Amalin DM, Watanabe K, Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines. BMC Infectious Diseases 2018; 18:183. https://doi.org/10.1186/s12879-018-3066- 0 PMID: 29665781Rehman NA, Kalyanaraman S, Ahmad T, Pervaiz F, Saif U, Subramanian L, Fine-grained dengue forecasting using telephone triage services. Science Advances 2016; 2(7): e1501215. https://doi.org/10. 1126/sciadv.1501215 PMID: 27419226Freeze J, Erraguntla M, Verma A, Data integration and predictive analysis system for disease prophylaxis: Incorporating dengue fever forecasts. Proceedings of the 51st Hawaii International Conference on System Science 2018; 913–922.Dinh L, Chowell G, Rothenberg R, Growth scaling for the early dynamics of HIV/AIDS epidemics in Brazil and the influence of socio-demographic factors. Journal of Theoretical Biology 2018; 442:79–86. https://doi.org/10.1016/j.jtbi.2017.12.030 PMID: 29330056Chretien J-P, Riley S, George DB, Mathematical modeling of the West Aftica Ebola epidemic. eLIFE 2015; 4:e09186. https://doi.org/10.7554/eLife.09186 PMID: 26646185Cardona-Ospina JA, Villamil-Go´mez WE, Jimenez-Canizales CE, Castañeda-Herna´ndez DM, Rodrı´- guez-Morales AJ. Estimating the burden of disease and the economic cost attributable to chikungunya, Colombia, 2014. Transactions of the Royal Society of Tropical Medicine and Hygiene 2015; 109 (12):793–802. https://doi.org/10.1093/trstmh/trv094 PMID: 26626342Villar LA, Rojas DP, Besada-Lombana S, Sarti E. Epidemiological trends of dengue disease in Colombia (2000–2011): a systematic review. PLoS Neglected Tropical Diseases 2015; 9(3): e0003499. https://doi.org/10.1371/journal.pntd.0003499 PMID: 25790245Ospina Martinez ML, Martinez Duran ME, Pacheco Garcı´a OE, Bonilla HQ, Pe´rez NT., Protocolo de vigilancia en salud pu´blica enfermedad por virus Zika. PRO-R02.056. Bogota (Colombia): Instituto Nacional de Salud, 2017. Available from: http://bvs.minsa.gob.pe/local/MINSA/3449.pdf (last accessed December 16, 2019).Beketov MA, Yurchenko YA, Belevich OE, Liess M, What environmental factors are important determinants of structure, species richness, and abundance of mosquito assemblages? Journal of Medical Entomology 2010; 47:129–139. https://doi.org/10.1603/me09150 PMID: 20380292Joyce RJ CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology 2004; 5:487– 503.Koyadun S, Butraporn P, Kittayapong P, Ecologic and sociodemographic risk determinants for dengue transmission in urban areas in Thailand. Interdisciplinary Perspectives on Infectious Diseases 2012; 2012:907494. https://doi.org/10.1155/2012/907494 PMID: 23056042Reiter P, Climate change and mosquito-borne disease. Environmental Health Perspectives 2001; 109 (supplement 1):141–161. https://doi.org/10.1289/ehp.01109s1141 PMID: 11250812Soghaier MA, Himatt S, Osman KE, Okoued SI, Seidahmed OE, Beatty ME, et al., Cross-sectional community-based study of the socio-demographic factors associated with the prevalence of dengue in the eastern part of Sudan in 2011. BMC Public Health 2015; 15:558. https://doi.org/10.1186/s12889- 015-1913-0 PMID: 26084275Kannan Maharajan M, Rajiah K, Singco Belotindos JA, Bases MS. Social determinants predicting the knowledge, attitudes, and practices of women toward zika virus infection Frontiers in Public Health 2020; 8:170. https://doi.org/10.3389/fpubh.2020.00170 PMID: 32582602Couse Quinn S, Kumar S. Health inequalities and infectious disease epidemics: A challenge for global health security. Biosecurity and Bioterrorism: Biodefense Srategy, Practice, and Science 2014; 12 (5):263–273.Breiman L, Random forests. Machine learning 2001; 45(1):5–32.Hulme M, New M. Dependence of large-scale precipitation climatologies on temporal and spatial sampling. Journal of Climate, 1997; 10:1099–1113Papacharalampous GA, Tyralis H, Evaluation of random forests and prophet for daily streamflow forecasting. Advances in Geosciences 2018; 45:201–208.Lu L, Lin H, Tian L, Yang W, Sun J, Liu Q, Time series analysis of dengue fever and weather in Guangzhou, China, BMC Public Health 2009; 9:395. https://doi.org/10.1186/1471-2458-9-395 PMID: 19860867Chen S-C. Liao C-M, Chio C-P, Chou H-H, You S-H, Cheng Y-H, lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: Insights from a statistical analysis. Science of The Total Environment 2010; 408(19):469–4075.Cheong YL, Burkart K, Leitao PJ, Lakes T, Assessing weather effects on dengue disease in Malaysia, International Journal of Environmental Research and Public Health 2013; 10(12):6319–6334. https:// doi.org/10.3390/ijerph10126319 PMID: 24287855Chang K, Chen C-D, Shih C-M, Lee T-C, Wu M-T, Wu D-C, et al., Time-lagging interplay effect and excess risk of meteorological/mosquito parameters and petrochemical gas explosion on dengue incidence. Scientific reports 2016; 6:35028. https://doi.org/10.1038/srep35028 PMID: 27733774Chen Y, Ong JHY, Rajarethinam J, Yap G, Ng LC, Cook AR. Neighbourhood level real-time forecasting of dengue cases in tropical urban Singapore. BMC Medicine 2018; 16(1):129. https://doi.org/10.1186/ s12916-018-1108-5 PMID: 30078378Eastin MD, Delmelle E, Casas I, Wexler J, Self C, Intra-and interseasonal autoregressive prediction of dengue outbreaks using local weather and regional climate for a tropical environment in Colombia. The American Journal of Tropical Medicine and Hygiene 2014; 91(3):598–610. https://doi.org/10.4269/ ajtmh.13-0303 PMID: 24957546Bostan N, Javed S, Amen N, Eqani SAMAS, Tahir F, Bokhari H, Dengue fever virus in Pakistan: effects of seasonal pattern and temperature change on distribution of vector and virus. Reviews in Medical Virology 2017; 27(1):e1899.Oidtman RJ, Lai S, Huang Z, Yang J, Siraj AS, Reiner RC, et al., Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China, Nature Communications 2019; 10:1148. https://doi.org/10.1038/s41467-019-09035-x PMID: 30850598Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. Spring, Berlin, 2008.Liaw A, Wiener M. Breiman and Culter’s random forests for classification and regression. 2018. Available from: https://cran.r-project.org/web/packages/randomForest/randomForest.pdf (last accessed May 7, 2020)Peng Z, Letu H, Wang T, Shi C, Zhao C, Tana G, Zhao N, Dai T, Tang R, Shang H, Shi J, Chen L. Estimation of shortwave solar radiation using the artificial neural network from Himawari-8 satellite imagery over China. Journal of Quantitative Spectroscopy and Radiative Transfer 2020; 240: 106672.Hyndman RJ, Khandakar Y. Automatic time series forecasting: The forecast package for R. Journal of Statistical Software 2008; 27: 1–22.Reich NG, Lessler J, Sakrejda K, Lauer SA, Iamsirithaworn S, Cummings DAT. Case study in evaluating time series prediction models using the relative mean absolute error. The American Statistician 2016; 70: 285–292. https://doi.org/10.1080/00031305.2016.1148631 PMID: 28138198Liu Y, Cao G, Zhao N, Mulligan K, Ye X. Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach. Environmental Pollution 2018; 235: 272–282. https:// doi.org/10.1016/j.envpol.2017.12.070 PMID: 29291527Grziwotz F, Strauß JF, Hsieh C-h, Telschow A. Empirical dynamic modelling identifies different responses of Aedes Polynesiensis subpopulations to natural environmental variables. Scientific Reports 2018; 8: 16768. https://doi.org/10.1038/s41598-018-34972-w PMID: 30425277da Cruz Ferreira DA, Degener CM, de Almeida Marques-Toledo C, Bendati MM, Fetzer LO, Teixeira CP, Eiras AE. Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika. Parasites Vectors 2017; 10: 78. https://doi.org/10.1186/s13071-017-2025-8 PMID: 28193291Manica M, Filipponi F, D’Alessandro A, Screti A, Neteler M, Rosà R, et al. Spatial and Temporal Hot Spots of Aedes albopictus Abundance inside and outside a South European Metropolitan Area. PLoS Neglected Tropical Diseases 2016; 10(6): e0004758. https://doi.org/10.1371/journal.pntd.0004758 PMID: 27333276Mulligan K, Dixon J, Sinn C-L J, Elliott SJ. Is dengue a disease of poverty? A systematic review. Pathogens and Global Health 2015; 109(1): 10–18. https://doi.org/10.1179/2047773214Y.0000000168 PMID: 25546339Tapia-Conyer R, Me´ndez-Galva´n JF, Gallardo-Rinco´n H. The growing burden of dengue in Latin America. Journal of Clinical Virology 2009; 46: S3–S6. https://doi.org/10.1016/S1386-6532(09)70286-0 PMID: 19800563Adams EA, Boateng GO, Amoyaw JA. Socioeconomic and demographic predictors of potable water and sanitation access in Ghana. Social Indicators Research 2016; 126(2): 673–687.de Janvry A, Sadoulet E. Growth, poverty, and inequality in Latin America: A causal analysis, 1970–94. The review of Income and Wealth 2000; 46(3): 267–287.Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E. Deep learning applications and challenges in big data analytics. Journal of Big Data 2015; 2:1.Ong J, Liu X, Rajarethinam J, Kok SY, Liang S, Tang CS, et al., Mapping dengue risk in Singapore using random forest. PLoS Neglected Tropical Diseases 2018; 12(6):e0006587. https://doi.org/10. 1371/journal.pntd.0006587 PMID: 29912940Williams RJ, Zipser D, A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1989; 1(2):270–280.PublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-84334https://repository.ucc.edu.co/bitstreams/77666efc-7737-42a2-94cc-b5a37d97e89b/download3bce4f7ab09dfc588f126e1e36e98a45MD52ORIGINALmachine learning 2020.pdfmachine learning 2020.pdfArtículoapplication/pdf1552130https://repository.ucc.edu.co/bitstreams/113a559e-bf6c-4d82-8f56-c82a966facf1/downloadb2ce7c8ff6451109859fcef3a1f70278MD51THUMBNAILmachine learning 2020.pdf.jpgmachine learning 2020.pdf.jpgGenerated Thumbnailimage/jpeg5961https://repository.ucc.edu.co/bitstreams/367578f4-40b4-4442-8d3c-2787e70d169b/download60b17a273fb2e3be9688997747879ad9MD53TEXTmachine learning 2020.pdf.txtmachine learning 2020.pdf.txtExtracted texttext/plain65578https://repository.ucc.edu.co/bitstreams/8ba905e2-8194-47e4-bf4c-54a14da95a4e/download881ade4dffae608891afa318aa4b88a2MD5420.500.12494/32758oai:repository.ucc.edu.co:20.500.12494/327582024-08-10 22:49:44.083restrictedhttps://repository.ucc.edu.coRepositorio Institucional Universidad Cooperativa de Colombiabdigital@metabiblioteca.comVU5JVkVSU0lEQUQgQ09PUEVSQVRJVkEgREUgQ09MT01CSUEKUkVQT1NJVE9SSU9TIElOU1RJVFVDSU9OQUxFUwpMSUNFTkNJQSBERSBVU08KClBvciBtZWRpbyBkZWwgcHJlc2VudGUgZG9jdW1lbnRvLCBlbCBBdXRvcihlcyksIG1heW9yIChlcykgZGUgZWRhZCwgcXVpZW4gZW4gYWRlbGFudGUgc2UgZGVub21pbmFyw6EgZWwgQVVUT1IsIGNvbmZpZXJlIGEgbGEgVU5JVkVSU0lEQUQgQ09PUEVSQVRJVkEgREUgQ09MT01CSUEsIGNvbiBOSVQuIDg2MC0wMjk5MjQtNywgdW5hIExJQ0VOQ0lBIERFIFVTTyBkZSBvYnJhLCBiYWpvIGxhcyBzaWd1aWVudGVzIGNvbmRpY2lvbmVzLgoKQ0zDgVVTVUxBUwoKUFJJTUVSQS4gT2JqZXRvLiBFTCBBVVRPUiBwb3IgZXN0ZSBhY3RvIGF1dG9yaXphIGxhIHV0aWxpemFjacOzbiBkZSBsYSBvYnJhLCBkZSBjb25mb3JtaWRhZCBjb24gbG8gZXN0aXB1bGFkbyBhIGNvbnRpbnVhY2nDs246IAoKKGEpIFBhcmEgZWZlY3RvcyBkZSBsYSBwcmVzZW50ZSBsaWNlbmNpYSBzZSBhdXRvcml6YSBsYSByZXByb2R1Y2Npw7NuIGRlIGxhIG9icmEgYW50ZXJpb3JtZW50ZSBjaXRhZGEsIGxhIGN1YWwgc2UgYWxvamFyw6EgZW4gZm9ybWF0byBkaWdpdGFsIGVuIGxhcyBwbGF0YWZvcm1hcyBvIHJlcG9zaXRvcmlvcyBhZG1pbmlzdHJhZG9zIHBvciBsYSBVTklWRVJTSURBRCBvIGVuIG90cm8gdGlwbyBkZSByZXBvc2l0b3Jpb3MgZXh0ZXJub3MgbyBww6FnaW5hcyB3ZWIgZXNjb2dpZG9zIHBvciBsYSBVTklWRVJTSURBRCwgcGFyYSBmaW5lcyBkZSBkaWZ1c2nDs24geSBkaXZ1bGdhY2nDs24uIEFkaWNpb25hbG1lbnRlLCBzZSBhdXRvcml6YSBhIHF1ZSBsb3MgdXN1YXJpb3MgaW50ZXJub3MgeSBleHRlcm5vcyBkZSBkaWNoYXMgcGxhdGFmb3JtYXMgbyByZXBvc2l0b3Jpb3MgcmVwcm9kdXpjYW4gbyBkZXNjYXJndWVuIGxhIG9icmEsIHNpbiDDoW5pbW8gZGUgbHVjcm8sIHBhcmEgZmluZXMgcHJpdmFkb3MsIGVkdWNhdGl2b3MgbyBhY2Fkw6ltaWNvczsgc2llbXByZSB5IGN1YW5kbyBubyBzZSB2aW9sZW4gYWN1ZXJkb3MgY29uIGVkaXRvcmVzLCBwZXJpb2RvcyBkZSBlbWJhcmdvIG8gYWN1ZXJkb3MgZGUgY29uZmlkZW5jaWFsaWRhZCBxdWUgYXBsaXF1ZW4uCgooYikgU2UgYXV0b3JpemEgbGEgY29tdW5pY2FjacOzbiBww7pibGljYSB5IGxhIHB1ZXN0YSBhIGRpc3Bvc2ljacOzbiBkZSBsYSBvYnJhIG1lbmNpb25hZGEsIGVuIGFjY2VzbyBhYmllcnRvLCBwYXJhIHN1IHV0aWxpemFjacOzbiBlbiBsYXMgcGxhdGFmb3JtYXMgbyByZXBvc2l0b3Jpb3MgYWRtaW5pc3RyYWRvcyBwb3IgbGEgVU5JVkVSU0lEQUQuCgooYykgTG8gYW50ZXJpb3IgZXN0YXLDoSBzdWpldG8gYSBsYXMgZGVmaW5pY2lvbmVzIGNvbnRlbmlkYXMgZW4gbGEgRGVjaXNpw7NuIEFuZGluYSAzNTEgZGUgMTk5MyB5IGxhIExleSAyMyBkZSAxOTgyLgoKClNFR1VOREEuIE9yaWdpbmFsaWRhZCB5IHJlY2xhbWFjaW9uZXMuIEVsIEFVVE9SIGRlY2xhcmEgcXVlIGxhIE9CUkEgZXMgb3JpZ2luYWwgeSBxdWUgZXMgZGUgc3UgY3JlYWNpw7NuIGV4Y2x1c2l2YSwgbm8gZXhpc3RpZW5kbyBpbXBlZGltZW50byBkZSBjdWFscXVpZXIgbmF0dXJhbGV6YSAoZW1iYXJnb3MsIHVzbyBkZSBtYXRlcmlhbCBwcm90ZWdpZG8gcG9yIGRlcmVjaG9zIGRlIGF1dG9yKSBwYXJhIGxhIGNvbmNlc2nDs24gZGUgbG9zIGRlcmVjaG9zIHByZXZpc3RvcyBlbiBlc3RlIGFjdWVyZG8uIEVsIEFVVE9SIHJlc3BvbmRlcsOhIHBvciBjdWFscXVpZXIgYWNjacOzbiBkZSByZWl2aW5kaWNhY2nDs24sIHBsYWdpbyB1IG90cmEgY2xhc2UgZGUgcmVjbGFtYWNpw7NuIHF1ZSBhbCByZXNwZWN0byBwdWRpZXJhIHNvYnJldmVuaXIuCgpURVJDRVJBLiBDb250cmFwcmVzdGFjacOzbi4gRWwgQVVUT1IgYXV0b3JpemEgYSBxdWUgc3Ugb2JyYSBzZWEgdXRpbGl6YWRhIGRlIGNvbmZvcm1pZGFkIGNvbiBsYSBjbMOhdXN1bGEgUFJJTUVSQSBkZSBmb3JtYSBncmF0dWl0YSwgZXMgZGVjaXIsIHF1ZSBsYSB1dGlsaXphY2nDs24gZGUgbGEgbWlzbWEgbm8gZ2VuZXJhIG5pbmfDum4gcGFnbyBvIHJlZ2Fsw61hcyBlbiBmYXZvciBkZSBlc3RlLgoKQ1VBUlRBLiBUaXR1bGFyaWRhZCBkZSBkZXJlY2hvcy4gRWwgcHJlc2VudGUgY29udHJhdG8gbm8gdHJhbnNmaWVyZSBsYSB0aXR1bGFyaWRhZCBkZSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBzb2JyZSBsYXMgb2JyYXMgYW50ZXJpb3JtZW50ZSBtZW5jaW9uYWRhcyBhIGxhIFVOSVZFUlNJREFELiDDmm5pY2FtZW50ZSBoYWNlIHJlbGFjacOzbiBhIHVuYSBsaWNlbmNpYSBubyBleGNsdXNpdmEgZW4gbG9zIHTDqXJtaW5vcyB5IGNvbmRpY2lvbmVzIGFudGVyaW9ybWVudGUgcGFjdGFkb3MuCgpRVUlOVEEuIENyw6lkaXRvcy4gTGEgVU5JVkVSU0lEQUQgc2UgY29tcHJvbWV0ZSBhIGRhciBhbCBBVVRPUiwgZWwgcmVjb25vY2ltaWVudG8gZGVudHJvIGNhZGEgZm9ybWEgZGUgdXRpbGl6YWNpw7NuIGVuIGxhIG9icmEuIExvcyBjcsOpZGl0b3MgZGViZW4gZmlndXJhciBlbiBjYWRhIHVubyBkZSBsb3MgZm9ybWF0b3MgbyByZWdpc3Ryb3MgZGUgcHVibGljYWNpw7NuLiBObyBjb25zdGl0dWlyw6EgdW5hIHZpb2xhY2nDs24gYSBsb3MgZGVyZWNob3MgbW9yYWxlcyBkZWwgYXV0b3IgbGEgbm8gcmVwcm9kdWNjacOzbiwgY29tdW5pY2FjacOzbiBvIGRlbcOhcyB1dGlsaXphY2lvbmVzIGRlIGxhIG9icmEuIExhIHV0aWxpemFjacOzbiBvIG5vIGRlIGxhIG9icmEsIGFzw60gY29tbyBzdSBmb3JtYSBkZSB1dGlsaXphY2nDs24gc2Vyw6EgZmFjdWx0YWQgZXhjbHVzaXZhIGRlIGxhIFVOSVZFUlNJREFELgogClNFWFRBLiBEdXJhY2nDs24geSB0ZXJyaXRvcmlvLiBMYSBwcmVzZW50ZSBsaWNlbmNpYSBkZSB1c28gcXVlIHNlIG90b3JnYSBhIGZhdm9yIGRlIGxhIFVOSVZFUlNJREFEIHRlbmRyw6EgdW5hIGR1cmFjacOzbiBlcXVpdmFsZW50ZSBhbCB0w6lybWlubyBkZSBwcm90ZWNjacOzbiBsZWdhbCBkZSBsYSBvYnJhIHkgcGFyYSB0b2RvcyBsb3MgcGHDrXNlcyBkZWwgbXVuZG8uCgpTw4lQVElNQS4gVXNvIGRlIENyZWF0aXZlIENvbW1vbnMuIEVsIEFVVE9SIGF1dG9yaXphcsOhIGxhIGRpZnVzacOzbiBkZSBzdSBjb250ZW5pZG8gYmFqbyB1bmEgbGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyBhdHJpYnVjacOzbiA0LjAgaW50ZXJuYWNpb25hbCwgcXVlIGRlYmVyw6EgaW5jbHVpcnNlIGVuIGVsIGNvbnRlbmlkby4gCgpPQ1RBVkEuIERlcmVjaG8gZGUgZXhjbHVzacOzbi4gQ2FkYSBhdXRvciBwdWVkZSBpbmRpY2FyIGVuIGVsIG1vbWVudG8gZGUgZGVww7NzaXRvIGRlbCBjb250ZW5pZG8gcXVlIGVsIHRleHRvIGNvbXBsZXRvIGRlIGxhIHByb2R1Y2Npw7NuIGFjYWTDqW1pY2EgbyBjaWVudMOtZmljYSBubyBlc3RlIGNvbiBhY2Nlc28gYWJpZXJ0byBlbiBlbCBSZXBvc2l0b3JpbyBJbnN0aXR1Y2lvbmFsIHBvciBtb3Rpdm9zIGRlIGNvbmZpZGVuY2lhbGlkYWQsIHBvcnF1ZSBzZSBlbmN1ZW50cmUgZW4gdsOtYXMgZGUgb2J0ZW5lciB1biBkZXJlY2hvIGRlIHByb3BpZWRhZCBpbmR1c3RyaWFsIG8gZXhpc3RpciBhY3VlcmRvcyBwcmV2aW9zIGNvbiB0ZXJjZXJvcyAoZWRpdG9yaWFsZXMsIHJldmlzdGFzIGNpZW50w61maWNhcywgb3RyYXMgaW5zdGl0dWNpb25lcykuIEVsIGF1dG9yIHNlIGNvbXByb21ldGUgYSBkZXBvc2l0YXIgbG9zIG1ldGFkYXRvcyBlIGluZm9ybWFyIGVsIHRpZW1wbyBkZSBlbWJhcmdvIGR1cmFudGUgZWwgY3VhbCBlbCB0ZXh0byBjb21wbGV0byB0ZW5kcsOhIGFjY2VzbyByZXN0cmluZ2lkby4gCgpOT1ZFTkEuIEVsIEFVVE9SIGFsIGFjZXB0YXIgZXN0YSBsaWNlbmNpYSBhZHVjZSBxdWUgZXN0YSBwcm9kdWNjacOzbiBzZSBkZXNhcnJvbGzDsyBlbiBlbCBwZXJpb2RvIGVuIHF1ZSBzZSBlbmN1ZW50cmEgY29uIHbDrW5jdWxvcyBjb24gTGEgVW5pdmVyc2lkYWQuCgpEw4lDSU1BLiBOb3JtYXMgYXBsaWNhYmxlcy4gUGFyYSBsYSBpbnRlcnByZXRhY2nDs24geSBjdW1wbGltaWVudG8gZGVsIHByZXNlbnRlIGFjdWVyZG8gbGFzIHBhcnRlcyBzZSBzb21ldGVuIGEgbGEgRGVjaXNpw7NuIEFuZGluYSAzNTEgZGUgMTk5MywgbGEgTGV5IDIzIGRlIDE5ODIgeSBkZW3DoXMgbm9ybWFzIGFwbGljYWJsZXMgZGUgQ29sb21iaWEuIEFkZW3DoXMsIGEgbGFzIG5vcm1hcyBJbnN0aXR1Y2lvbmFsZXMgcXVlIGFwbGlxdWVuLgoKTGEgcHJlc2VudGUgbGljZW5jaWEgc2UgYXV0b3JpemEgZW4gbGEgZmVjaGEgZGUgcHVibGljYWNpw7NuIGVuIGxvcyByZXBvc2l0b3Jpb3MgaW5zdGl0dWNpb25hbGVzLgo= |