BMC Med Inform Decis Mak - in silico surveillance: evaluating outbreak detection with simulation models.

Tópicos

{ control(1307) perform(991) simul(935) }
{ data(2317) use(1299) case(1017) }
{ detect(2391) sensit(1101) algorithm(908) }
{ spatial(1525) area(1432) region(1030) }
{ process(1125) use(805) approach(778) }
{ perform(999) metric(946) measur(919) }
{ implement(1333) system(1263) develop(1122) }
{ data(1737) use(1416) pattern(1282) }
{ treatment(1704) effect(941) patient(846) }
{ howev(809) still(633) remain(590) }
{ research(1218) medic(880) student(794) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ studi(2440) review(1878) systemat(933) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ case(1353) use(1143) diagnosi(1136) }
{ monitor(1329) mobil(1314) devic(1160) }
{ group(2977) signific(1463) compar(1072) }
{ use(976) code(926) identifi(902) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ method(1219) similar(1157) match(930) }
{ patient(2315) diseas(1263) diabet(1191) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ framework(1458) process(801) describ(734) }
{ learn(2355) train(1041) set(1003) }
{ algorithm(1844) comput(1787) effici(935) }
{ data(1714) softwar(1251) tool(1186) }
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{ signal(2180) analysi(812) frequenc(800) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ model(2220) cell(1177) simul(1124) }
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{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ method(2212) result(1239) propos(1039) }

Resumo

CKGROUND: Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors' objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols.METHODS: A detailed representation of the Boston area was constructed, based on data about individuals, locations, and activity patterns. Influenza-like illness (ILI) transmission was simulated, producing 100 years of in silico ILI data. Six different surveillance systems were designed and developed using gathered cases from the simulated disease data. Performance was measured by inserting test outbreaks into the surveillance streams and analyzing the likelihood and timeliness of detection.RESULTS: Detection of outbreaks varied from 21% to 95%. Increased coverage did not linearly improve detection probability for all surveillance systems. Relaxing the decision threshold for signaling outbreaks greatly increased false-positives, improved outbreak detection slightly, and led to earlier outbreak detection.CONCLUSIONS: Geographical distribution can be more important than coverage level. Detailed simulations of infectious disease transmission can be configured to represent nearly any conceivable scenario. They are a powerful tool for evaluating the performance of surveillance systems and methods used for outbreak detection.

Resumo Limpo

ckground detect outbreak crucial task public health offici yet gap remain systemat evalu outbreak detect protocol author object design implement test flexibl methodolog generat detail synthet surveil data provid realist geograph tempor cluster case use evalu outbreak detect protocolsmethod detail represent boston area construct base data individu locat activ pattern influenzalik ill ili transmiss simul produc year silico ili data six differ surveil system design develop use gather case simul diseas data perform measur insert test outbreak surveil stream analyz likelihood timeli detectionresult detect outbreak vari increas coverag linear improv detect probabl surveil system relax decis threshold signal outbreak great increas falseposit improv outbreak detect slight led earlier outbreak detectionconclus geograph distribut can import coverag level detail simul infecti diseas transmiss can configur repres near conceiv scenario power tool evalu perform surveil system method use outbreak detect

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