Spat Spatiotemporal Epidemiol - Global animal disease surveillance.

Tópicos

{ data(2317) use(1299) case(1017) }
{ visual(1396) interact(850) tool(830) }
{ analysi(2126) use(1163) compon(1037) }
{ research(1085) discuss(1038) issu(1018) }
{ spatial(1525) area(1432) region(1030) }
{ gene(2352) biolog(1181) express(1162) }
{ health(1844) social(1437) communiti(874) }
{ implement(1333) system(1263) develop(1122) }
{ framework(1458) process(801) describ(734) }
{ concept(1167) ontolog(924) domain(897) }
{ featur(1941) imag(1645) propos(1176) }
{ studi(1410) differ(1259) use(1210) }
{ result(1111) use(1088) new(759) }
{ model(3404) distribut(989) bayesian(671) }
{ method(1219) similar(1157) match(930) }
{ imag(2675) segment(2577) method(1081) }
{ motion(1329) object(1292) video(1091) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ method(984) reconstruct(947) comput(926) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ use(976) code(926) identifi(902) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(2212) result(1239) propos(1039) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ 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) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ import(1318) role(1303) understand(862) }
{ 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) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ 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) }
{ 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) }
{ 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) }
{ 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

Development and implementation of global animal disease surveillance has been limited by the lack of information systems that enable near real-time data capturing, sharing, analysis, and related decision- and policy-making. The objective of this paper is to describe requirements for global animal disease surveillance, including design and functionality of tools and methods for visualization and analysis of animal disease data. The paper also explores the potential application of techniques for spatial and spatio-temporal analysis on global animal disease surveillance, including for example, landscape genetics, social network analysis, and Bayesian modeling. Finally, highly pathogenic avian influenza data from Denmark and Sweden are used to illustrate the potential application of a novel system (Disease BioPortal) for data sharing, visualization, and analysis for regional and global surveillance efforts.

Resumo Limpo

develop implement global anim diseas surveil limit lack inform system enabl near realtim data captur share analysi relat decis policymak object paper describ requir global anim diseas surveil includ design function tool method visual analysi anim diseas data paper also explor potenti applic techniqu spatial spatiotempor analysi global anim diseas surveil includ exampl landscap genet social network analysi bayesian model final high pathogen avian influenza data denmark sweden use illustr potenti applic novel system diseas bioport data share visual analysi region global surveil effort

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