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En el instante 30 de octubre de 2025, 2:20:33 UTC,
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a Predicción de COVID-19 en Hubei, China: optimización de modelos de contagio con Recocido Simulado
| f | 1 | { | f | 1 | { |
| 2 | "author": "Teun Hoven, Alberto Garc\u00eda Robledo, Mahboobeh | 2 | "author": "Teun Hoven, Alberto Garc\u00eda Robledo, Mahboobeh | ||
| 3 | Zangiabady", | 3 | Zangiabady", | ||
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| 9 | "value": "2025" | 9 | "value": "2025" | ||
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| 12 | "key": "Fecha", | ||||
| 13 | "value": "22/07/2025" | ||||
| 10 | }, | 14 | }, | ||
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| 12 | "key": "Identificador hash", | 16 | "key": "Identificador hash", | ||
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| 15 | { | 19 | { | ||
| 16 | "key": "Instituciones", | 20 | "key": "Instituciones", | ||
| 17 | "value": "SECIHTI-CentroGeo, University of Twente" | 21 | "value": "SECIHTI-CentroGeo, University of Twente" | ||
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| 19 | { | 23 | { | ||
| 20 | "key": "Subt\u00edtulo", | 24 | "key": "Subt\u00edtulo", | ||
| 21 | "value": "Una mejora al algoritmo NIPA con datos del brote en | 25 | "value": "Una mejora al algoritmo NIPA con datos del brote en | ||
| 22 | contextos metropolitanos" | 26 | contextos metropolitanos" | ||
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| 24 | { | 28 | { | ||
| 25 | "key": "Tipo", | 29 | "key": "Tipo", | ||
| 26 | "value": "Art\u00edculo en l\u00ednea" | 30 | "value": "Art\u00edculo en l\u00ednea" | ||
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| 28 | { | 32 | { | ||
| 29 | "key": "URL", | 33 | "key": "URL", | ||
| 30 | "value": | 34 | "value": | ||
| 31 | opolitanos-optimizacion-de-modelos-de-contagio-con-recocido-simulado/" | 35 | opolitanos-optimizacion-de-modelos-de-contagio-con-recocido-simulado/" | ||
| 32 | } | 36 | } | ||
| 33 | ], | 37 | ], | ||
| 34 | "groups": [ | 38 | "groups": [ | ||
| 35 | { | 39 | { | ||
| 36 | "description": "Este grupo re\u00fane los art\u00edculos de | 40 | "description": "Este grupo re\u00fane los art\u00edculos de | ||
| 37 | divulgaci\u00f3n publicados por el Observatorio Metropolitano del | 41 | divulgaci\u00f3n publicados por el Observatorio Metropolitano del | ||
| 38 | CentroGeo. Cada art\u00edculo presenta, en un lenguaje accesible y con | 42 | CentroGeo. Cada art\u00edculo presenta, en un lenguaje accesible y con | ||
| 39 | enfoque metropolitano, los principales hallazgos, metodolog\u00edas y | 43 | enfoque metropolitano, los principales hallazgos, metodolog\u00edas y | ||
| 40 | aplicaciones de los proyectos de investigaci\u00f3n desarrollados por | 44 | aplicaciones de los proyectos de investigaci\u00f3n desarrollados por | ||
| 41 | el observatorio. Los contenidos est\u00e1n hospedados en el portal web | 45 | el observatorio. Los contenidos est\u00e1n hospedados en el portal web | ||
| 42 | del Observatorio Metropolitano y buscan acercar el conocimiento | 46 | del Observatorio Metropolitano y buscan acercar el conocimiento | ||
| 43 | cient\u00edfico y t\u00e9cnico a la sociedad, fomentando la | 47 | cient\u00edfico y t\u00e9cnico a la sociedad, fomentando la | ||
| 44 | comprensi\u00f3n de los fen\u00f3menos urbanos y territoriales desde | 48 | comprensi\u00f3n de los fen\u00f3menos urbanos y territoriales desde | ||
| 45 | una perspectiva interdisciplinaria.", | 49 | una perspectiva interdisciplinaria.", | ||
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| 50 | "title": "Art\u00edculos en l\u00ednea" | 54 | "title": "Art\u00edculos en l\u00ednea" | ||
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| 59 | "metadata_created": "2025-10-24T00:46:52.021148", | 63 | "metadata_created": "2025-10-24T00:46:52.021148", | ||
| t | 60 | "metadata_modified": "2025-10-30T02:07:01.546774", | t | 64 | "metadata_modified": "2025-10-30T02:20:33.176701", |
| 61 | "name": "55235c78d83e", | 65 | "name": "55235c78d83e", | ||
| 62 | "notes": "En este estudio se propone una mejora al algoritmo de | 66 | "notes": "En este estudio se propone una mejora al algoritmo de | ||
| 63 | predicci\u00f3n NIPA (Network Inference-based Prediction Algorithm), | 67 | predicci\u00f3n NIPA (Network Inference-based Prediction Algorithm), | ||
| 64 | que se basa en modelos epid\u00e9micos de tipo SIR e inferencia de | 68 | que se basa en modelos epid\u00e9micos de tipo SIR e inferencia de | ||
| 65 | redes, mediante la aplicaci\u00f3n del algoritmo de Recocido Simulado | 69 | redes, mediante la aplicaci\u00f3n del algoritmo de Recocido Simulado | ||
| 66 | para optimizar par\u00e1metros cr\u00edticos. Esta optimizaci\u00f3n | 70 | para optimizar par\u00e1metros cr\u00edticos. Esta optimizaci\u00f3n | ||
| 67 | permite mejorar la predicci\u00f3n de la evoluci\u00f3n temporal de | 71 | permite mejorar la predicci\u00f3n de la evoluci\u00f3n temporal de | ||
| 68 | enfermedades infecciosas como el COVID-19, especialmente en regiones | 72 | enfermedades infecciosas como el COVID-19, especialmente en regiones | ||
| 69 | densamente pobladas. La validaci\u00f3n se realiz\u00f3 utilizando | 73 | densamente pobladas. La validaci\u00f3n se realiz\u00f3 utilizando | ||
| 70 | datos del brote de COVID-19 en la provincia china de Hubei entre el 21 | 74 | datos del brote de COVID-19 en la provincia china de Hubei entre el 21 | ||
| 71 | de enero y el 14 de febrero de 2020. Los resultados muestran que el | 75 | de enero y el 14 de febrero de 2020. Los resultados muestran que el | ||
| 72 | uso de Recocido Simulado mejora la precisi\u00f3n predictiva respecto | 76 | uso de Recocido Simulado mejora la precisi\u00f3n predictiva respecto | ||
| 73 | a t\u00e9cnicas tradicionales como la validaci\u00f3n cruzada, lo que | 77 | a t\u00e9cnicas tradicionales como la validaci\u00f3n cruzada, lo que | ||
| 74 | representa una contribuci\u00f3n relevante para la modelaci\u00f3n y | 78 | representa una contribuci\u00f3n relevante para la modelaci\u00f3n y | ||
| 75 | gesti\u00f3n de crisis sanitarias en entornos metropolitanos.", | 79 | gesti\u00f3n de crisis sanitarias en entornos metropolitanos.", | ||
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| 79 | "approval_status": "approved", | 83 | "approval_status": "approved", | ||
| 80 | "created": "2022-05-19T00:10:30.480393", | 84 | "created": "2022-05-19T00:10:30.480393", | ||
| 81 | "description": "Observatorio Metropolitano CentroGeo", | 85 | "description": "Observatorio Metropolitano CentroGeo", | ||
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| 101 | "description": "En este estudio se propone una mejora al | 105 | "description": "En este estudio se propone una mejora al | ||
| 102 | algoritmo de predicci\u00f3n NIPA (Network Inference-based Prediction | 106 | algoritmo de predicci\u00f3n NIPA (Network Inference-based Prediction | ||
| 103 | Algorithm), que se basa en modelos epid\u00e9micos de tipo SIR e | 107 | Algorithm), que se basa en modelos epid\u00e9micos de tipo SIR e | ||
| 104 | inferencia de redes, mediante la aplicaci\u00f3n del algoritmo de | 108 | inferencia de redes, mediante la aplicaci\u00f3n del algoritmo de | ||
| 105 | Recocido Simulado para optimizar par\u00e1metros cr\u00edticos. Esta | 109 | Recocido Simulado para optimizar par\u00e1metros cr\u00edticos. Esta | ||
| 106 | optimizaci\u00f3n permite mejorar la predicci\u00f3n de la | 110 | optimizaci\u00f3n permite mejorar la predicci\u00f3n de la | ||
| 107 | evoluci\u00f3n temporal de enfermedades infecciosas como el COVID-19, | 111 | evoluci\u00f3n temporal de enfermedades infecciosas como el COVID-19, | ||
| 108 | especialmente en regiones densamente pobladas. La validaci\u00f3n se | 112 | especialmente en regiones densamente pobladas. La validaci\u00f3n se | ||
| 109 | realiz\u00f3 utilizando datos del brote de COVID-19 en la provincia | 113 | realiz\u00f3 utilizando datos del brote de COVID-19 en la provincia | ||
| 110 | china de Hubei entre el 21 de enero y el 14 de febrero de 2020. Los | 114 | china de Hubei entre el 21 de enero y el 14 de febrero de 2020. Los | ||
| 111 | resultados muestran que el uso de Recocido Simulado mejora la | 115 | resultados muestran que el uso de Recocido Simulado mejora la | ||
| 112 | precisi\u00f3n predictiva respecto a t\u00e9cnicas tradicionales como | 116 | precisi\u00f3n predictiva respecto a t\u00e9cnicas tradicionales como | ||
| 113 | la validaci\u00f3n cruzada, lo que representa una contribuci\u00f3n | 117 | la validaci\u00f3n cruzada, lo que representa una contribuci\u00f3n | ||
| 114 | relevante para la modelaci\u00f3n y gesti\u00f3n de crisis sanitarias | 118 | relevante para la modelaci\u00f3n y gesti\u00f3n de crisis sanitarias | ||
| 115 | en entornos metropolitanos.", | 119 | en entornos metropolitanos.", | ||
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| 231 | "name": "inferencia-de-redes", | 235 | "name": "inferencia-de-redes", | ||
| 232 | "state": "active", | 236 | "state": "active", | ||
| 233 | "vocabulary_id": null | 237 | "vocabulary_id": null | ||
| 234 | }, | 238 | }, | ||
| 235 | { | 239 | { | ||
| 236 | "display_name": "inteligencia-artificial", | 240 | "display_name": "inteligencia-artificial", | ||
| 237 | "id": "77af2445-0e8a-40c2-bb38-f28434e2257b", | 241 | "id": "77af2445-0e8a-40c2-bb38-f28434e2257b", | ||
| 238 | "name": "inteligencia-artificial", | 242 | "name": "inteligencia-artificial", | ||
| 239 | "state": "active", | 243 | "state": "active", | ||
| 240 | "vocabulary_id": null | 244 | "vocabulary_id": null | ||
| 241 | }, | 245 | }, | ||
| 242 | { | 246 | { | ||
| 243 | "display_name": "metaheuristicas", | 247 | "display_name": "metaheuristicas", | ||
| 244 | "id": "da87ee6f-6919-4177-8494-9c879c19aaaa", | 248 | "id": "da87ee6f-6919-4177-8494-9c879c19aaaa", | ||
| 245 | "name": "metaheuristicas", | 249 | "name": "metaheuristicas", | ||
| 246 | "state": "active", | 250 | "state": "active", | ||
| 247 | "vocabulary_id": null | 251 | "vocabulary_id": null | ||
| 248 | }, | 252 | }, | ||
| 249 | { | 253 | { | ||
| 250 | "display_name": "modelado-computacional", | 254 | "display_name": "modelado-computacional", | ||
| 251 | "id": "38d761c6-f7e8-47c5-a32d-3d4791002aa6", | 255 | "id": "38d761c6-f7e8-47c5-a32d-3d4791002aa6", | ||
| 252 | "name": "modelado-computacional", | 256 | "name": "modelado-computacional", | ||
| 253 | "state": "active", | 257 | "state": "active", | ||
| 254 | "vocabulary_id": null | 258 | "vocabulary_id": null | ||
| 255 | }, | 259 | }, | ||
| 256 | { | 260 | { | ||
| 257 | "display_name": "modelo-nipa", | 261 | "display_name": "modelo-nipa", | ||
| 258 | "id": "c3082a50-aa4e-4068-95e7-3df48306a08d", | 262 | "id": "c3082a50-aa4e-4068-95e7-3df48306a08d", | ||
| 259 | "name": "modelo-nipa", | 263 | "name": "modelo-nipa", | ||
| 260 | "state": "active", | 264 | "state": "active", | ||
| 261 | "vocabulary_id": null | 265 | "vocabulary_id": null | ||
| 262 | }, | 266 | }, | ||
| 263 | { | 267 | { | ||
| 264 | "display_name": "modelo-sir", | 268 | "display_name": "modelo-sir", | ||
| 265 | "id": "6fe8a509-56f7-427f-91f3-558703237e22", | 269 | "id": "6fe8a509-56f7-427f-91f3-558703237e22", | ||
| 266 | "name": "modelo-sir", | 270 | "name": "modelo-sir", | ||
| 267 | "state": "active", | 271 | "state": "active", | ||
| 268 | "vocabulary_id": null | 272 | "vocabulary_id": null | ||
| 269 | }, | 273 | }, | ||
| 270 | { | 274 | { | ||
| 271 | "display_name": "movilidad-urbana", | 275 | "display_name": "movilidad-urbana", | ||
| 272 | "id": "3587f36b-4788-4a7e-bc95-2f8148421454", | 276 | "id": "3587f36b-4788-4a7e-bc95-2f8148421454", | ||
| 273 | "name": "movilidad-urbana", | 277 | "name": "movilidad-urbana", | ||
| 274 | "state": "active", | 278 | "state": "active", | ||
| 275 | "vocabulary_id": null | 279 | "vocabulary_id": null | ||
| 276 | }, | 280 | }, | ||
| 277 | { | 281 | { | ||
| 278 | "display_name": "optimizacion-de-parametros", | 282 | "display_name": "optimizacion-de-parametros", | ||
| 279 | "id": "fa5af9b3-8c68-49fa-b2b7-ccfa8a0c7d44", | 283 | "id": "fa5af9b3-8c68-49fa-b2b7-ccfa8a0c7d44", | ||
| 280 | "name": "optimizacion-de-parametros", | 284 | "name": "optimizacion-de-parametros", | ||
| 281 | "state": "active", | 285 | "state": "active", | ||
| 282 | "vocabulary_id": null | 286 | "vocabulary_id": null | ||
| 283 | }, | 287 | }, | ||
| 284 | { | 288 | { | ||
| 285 | "display_name": "pandemia", | 289 | "display_name": "pandemia", | ||
| 286 | "id": "1ce39a9e-a00c-4889-b532-d9a953283e38", | 290 | "id": "1ce39a9e-a00c-4889-b532-d9a953283e38", | ||
| 287 | "name": "pandemia", | 291 | "name": "pandemia", | ||
| 288 | "state": "active", | 292 | "state": "active", | ||
| 289 | "vocabulary_id": null | 293 | "vocabulary_id": null | ||
| 290 | }, | 294 | }, | ||
| 291 | { | 295 | { | ||
| 292 | "display_name": "politicas-publicas", | 296 | "display_name": "politicas-publicas", | ||
| 293 | "id": "22c036a5-0bb2-4c66-8c49-9def931d04a0", | 297 | "id": "22c036a5-0bb2-4c66-8c49-9def931d04a0", | ||
| 294 | "name": "politicas-publicas", | 298 | "name": "politicas-publicas", | ||
| 295 | "state": "active", | 299 | "state": "active", | ||
| 296 | "vocabulary_id": null | 300 | "vocabulary_id": null | ||
| 297 | }, | 301 | }, | ||
| 298 | { | 302 | { | ||
| 299 | "display_name": "prediccion-de-brotes", | 303 | "display_name": "prediccion-de-brotes", | ||
| 300 | "id": "0624dea3-ee58-409d-97c9-736142bfb5c3", | 304 | "id": "0624dea3-ee58-409d-97c9-736142bfb5c3", | ||
| 301 | "name": "prediccion-de-brotes", | 305 | "name": "prediccion-de-brotes", | ||
| 302 | "state": "active", | 306 | "state": "active", | ||
| 303 | "vocabulary_id": null | 307 | "vocabulary_id": null | ||
| 304 | }, | 308 | }, | ||
| 305 | { | 309 | { | ||
| 306 | "display_name": "prediccion-epidemica", | 310 | "display_name": "prediccion-epidemica", | ||
| 307 | "id": "adfdc304-bf0d-4f37-8fe6-4d9c56facd6a", | 311 | "id": "adfdc304-bf0d-4f37-8fe6-4d9c56facd6a", | ||
| 308 | "name": "prediccion-epidemica", | 312 | "name": "prediccion-epidemica", | ||
| 309 | "state": "active", | 313 | "state": "active", | ||
| 310 | "vocabulary_id": null | 314 | "vocabulary_id": null | ||
| 311 | }, | 315 | }, | ||
| 312 | { | 316 | { | ||
| 313 | "display_name": "propagacion-de-enfermedades", | 317 | "display_name": "propagacion-de-enfermedades", | ||
| 314 | "id": "35691547-270d-4a62-b42d-866168a61980", | 318 | "id": "35691547-270d-4a62-b42d-866168a61980", | ||
| 315 | "name": "propagacion-de-enfermedades", | 319 | "name": "propagacion-de-enfermedades", | ||
| 316 | "state": "active", | 320 | "state": "active", | ||
| 317 | "vocabulary_id": null | 321 | "vocabulary_id": null | ||
| 318 | }, | 322 | }, | ||
| 319 | { | 323 | { | ||
| 320 | "display_name": "recocido-simulado", | 324 | "display_name": "recocido-simulado", | ||
| 321 | "id": "7bce321b-089b-434b-9f3d-f74bf19acf1d", | 325 | "id": "7bce321b-089b-434b-9f3d-f74bf19acf1d", | ||
| 322 | "name": "recocido-simulado", | 326 | "name": "recocido-simulado", | ||
| 323 | "state": "active", | 327 | "state": "active", | ||
| 324 | "vocabulary_id": null | 328 | "vocabulary_id": null | ||
| 325 | }, | 329 | }, | ||
| 326 | { | 330 | { | ||
| 327 | "display_name": "simulacion-epidemiologica", | 331 | "display_name": "simulacion-epidemiologica", | ||
| 328 | "id": "e731ad0f-5818-4d75-947d-43a19dd3fca6", | 332 | "id": "e731ad0f-5818-4d75-947d-43a19dd3fca6", | ||
| 329 | "name": "simulacion-epidemiologica", | 333 | "name": "simulacion-epidemiologica", | ||
| 330 | "state": "active", | 334 | "state": "active", | ||
| 331 | "vocabulary_id": null | 335 | "vocabulary_id": null | ||
| 332 | }, | 336 | }, | ||
| 333 | { | 337 | { | ||
| 334 | "display_name": "simulacion-estocastica", | 338 | "display_name": "simulacion-estocastica", | ||
| 335 | "id": "9e171e79-4e61-4785-8b9d-f209a8f7a127", | 339 | "id": "9e171e79-4e61-4785-8b9d-f209a8f7a127", | ||
| 336 | "name": "simulacion-estocastica", | 340 | "name": "simulacion-estocastica", | ||
| 337 | "state": "active", | 341 | "state": "active", | ||
| 338 | "vocabulary_id": null | 342 | "vocabulary_id": null | ||
| 339 | }, | 343 | }, | ||
| 340 | { | 344 | { | ||
| 341 | "display_name": "validacion-cruzada", | 345 | "display_name": "validacion-cruzada", | ||
| 342 | "id": "14976846-94ec-45c1-beb4-6c398889ebfd", | 346 | "id": "14976846-94ec-45c1-beb4-6c398889ebfd", | ||
| 343 | "name": "validacion-cruzada", | 347 | "name": "validacion-cruzada", | ||
| 344 | "state": "active", | 348 | "state": "active", | ||
| 345 | "vocabulary_id": null | 349 | "vocabulary_id": null | ||
| 346 | }, | 350 | }, | ||
| 347 | { | 351 | { | ||
| 348 | "display_name": "wuhan", | 352 | "display_name": "wuhan", | ||
| 349 | "id": "92dd7681-09d4-4e12-94c5-6aa1df49c94b", | 353 | "id": "92dd7681-09d4-4e12-94c5-6aa1df49c94b", | ||
| 350 | "name": "wuhan", | 354 | "name": "wuhan", | ||
| 351 | "state": "active", | 355 | "state": "active", | ||
| 352 | "vocabulary_id": null | 356 | "vocabulary_id": null | ||
| 353 | } | 357 | } | ||
| 354 | ], | 358 | ], | ||
| 355 | "title": "Predicci\u00f3n de COVID-19 en Hubei, China: | 359 | "title": "Predicci\u00f3n de COVID-19 en Hubei, China: | ||
| 356 | optimizaci\u00f3n de modelos de contagio con Recocido Simulado", | 360 | optimizaci\u00f3n de modelos de contagio con Recocido Simulado", | ||
| 357 | "type": "dataset", | 361 | "type": "dataset", | ||
| 358 | "url": | 362 | "url": | ||
| 359 | politanos-optimizacion-de-modelos-de-contagio-con-recocido-simulado/", | 363 | politanos-optimizacion-de-modelos-de-contagio-con-recocido-simulado/", | ||
| 360 | "version": null | 364 | "version": null | ||
| 361 | } | 365 | } |
