Spat Spatiotemporal Epidemiol - Statistical methods for bivariate spatial analysis in marked points. Examples in spatial epidemiology.

Tópicos

{ spatial(1525) area(1432) region(1030) }
{ estim(2440) model(1874) function(577) }
{ studi(1119) effect(1106) posit(819) }
{ data(1737) use(1416) pattern(1282) }
{ use(1733) differ(960) four(931) }
{ method(1969) cluster(1462) data(1082) }
{ method(1219) similar(1157) match(930) }
{ state(1844) use(1261) util(961) }
{ first(2504) two(1366) second(1323) }
{ structur(1116) can(940) graph(676) }
{ featur(3375) classif(2383) classifi(1994) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ general(901) number(790) one(736) }
{ case(1353) use(1143) diagnosi(1136) }
{ can(981) present(881) function(850) }
{ inform(2794) health(2639) internet(1427) }
{ sequenc(1873) structur(1644) protein(1328) }
{ framework(1458) process(801) describ(734) }
{ control(1307) perform(991) simul(935) }
{ howev(809) still(633) remain(590) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ decis(3086) make(1611) patient(1517) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ 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) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ 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) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ 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) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This article presents methods to analyze global spatial relationships between two variables in two different sets of fixed points. Analysis of spatial relationships between two phenomena is of great interest in health geography and epidemiology, especially to highlight competing interest between phenomena or evidence of a common environmental factor. Our general approach extends the Moran and Pearson indices to the bivariate case in two different sets of points. The case where the variables are Boolean is treated separately through methods using nearest neighbors distances. All tests use Monte-Carlo simulations to estimate their probability distributions, with options to distinguish spatial and no spatial correlation in the special case of identical sets analysis. Implementation in a Geographic Information System (SavGIS) and real examples are used to illustrate these spatial indices and methods in epidemiology.

Resumo Limpo

articl present method analyz global spatial relationship two variabl two differ set fix point analysi spatial relationship two phenomena great interest health geographi epidemiolog especi highlight compet interest phenomena evid common environment factor general approach extend moran pearson indic bivari case two differ set point case variabl boolean treat separ method use nearest neighbor distanc test use montecarlo simul estim probabl distribut option distinguish spatial spatial correl special case ident set analysi implement geograph inform system savgi real exampl use illustr spatial indic method epidemiolog

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