Spat Spatiotemporal Epidemiol - Supervised learning and prediction of spatial epidemics.

Tópicos

{ model(2341) predict(2261) use(1141) }
{ result(1111) use(1088) new(759) }
{ model(3404) distribut(989) bayesian(671) }
{ learn(2355) train(1041) set(1003) }
{ case(1353) use(1143) diagnosi(1136) }
{ model(3480) simul(1196) paramet(876) }
{ process(1125) use(805) approach(778) }
{ data(3963) clinic(1234) research(1004) }
{ spatial(1525) area(1432) region(1030) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ framework(1458) process(801) describ(734) }
{ data(2317) use(1299) case(1017) }
{ cost(1906) reduc(1198) effect(832) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ chang(1828) time(1643) increas(1301) }
{ control(1307) perform(991) simul(935) }
{ studi(1410) differ(1259) use(1210) }
{ system(1050) medic(1026) inform(1018) }
{ studi(1119) effect(1106) posit(819) }
{ monitor(1329) mobil(1314) devic(1160) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
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{ method(2212) result(1239) propos(1039) }
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{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
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{ extract(1171) text(1153) clinic(932) }
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{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ 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) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Parameter estimation for mechanistic models of infectious disease can be computationally intensive. Nsoesie et al. (2011) introduced an approach for inference on infectious disease data based on the idea of supervised learning. Their method involves simulating epidemics from various infectious disease models, and using classifiers built from the epidemic curve data to predict which model were most likely to have generated observed epidemic curves. They showed that the classification approach could fairly identify underlying characteristics of the disease system, without fitting various transmission models via, say, Bayesian Markov chain Monte Carlo. We extend this work to the case where the underlying infectious disease model is inherently spatial. Our goal is to compare the use of global epidemic curves for building the classifier, with the use of spatially stratified epidemic curves. We demonstrate these methods on simulated data and apply the method to analyze a tomato spotted wilt virus epidemic dataset.

Resumo Limpo

paramet estim mechanist model infecti diseas can comput intens nsoesi et al introduc approach infer infecti diseas data base idea supervis learn method involv simul epidem various infecti diseas model use classifi built epidem curv data predict model like generat observ epidem curv show classif approach fair identifi under characterist diseas system without fit various transmiss model via say bayesian markov chain mont carlo extend work case under infecti diseas model inher spatial goal compar use global epidem curv build classifi use spatial stratifi epidem curv demonstr method simul data appli method analyz tomato spot wilt virus epidem dataset

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