Spat Spatiotemporal Epidemiol - State transition detection in the spatio-temporal incidence of malaria.

Tópicos

{ risk(3053) factor(974) diseas(938) }
{ spatial(1525) area(1432) region(1030) }
{ sampl(1606) size(1419) use(1276) }
{ imag(2675) segment(2577) method(1081) }
{ high(1669) rate(1365) level(1280) }
{ detect(2391) sensit(1101) algorithm(908) }
{ control(1307) perform(991) simul(935) }
{ import(1318) role(1303) understand(862) }
{ state(1844) use(1261) util(961) }
{ use(976) code(926) identifi(902) }
{ inform(2794) health(2639) internet(1427) }
{ chang(1828) time(1643) increas(1301) }
{ model(2220) cell(1177) simul(1124) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ health(3367) inform(1360) care(1135) }
{ data(2317) use(1299) case(1017) }
{ group(2977) signific(1463) compar(1072) }
{ patient(1821) servic(1111) care(1106) }
{ analysi(2126) use(1163) compon(1037) }
{ result(1111) use(1088) new(759) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ 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) }
{ method(1219) similar(1157) match(930) }
{ 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) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ 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) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ 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) }
{ 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) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Mosquito-borne disease spread might exhibit irregular epidemic fronts caused by ecological heterogeneity in the risk factors. To determine Plasmodium vivax infection spread in north-eastern Venezuela, we used the State Transition Index (STI) to detect the spatial locations of malaria incidence boundaries and their dynamics over time. Then, we evaluated the role of population size on disease persistence. Boundary locations of malaria were found to be highly spatially variable. Waves of infection were observed in the spatial mosaics of large and small nearby localities due to a strong asynchrony in the epidemic dynamics between both host populations. Our results suggest that the epidemic spatial diffusion follows a hierarchy from large, populated villages (with few or no seasonal parasite fadeouts) to smaller, less populated localities, where infection outbreak was irregular or disease dynamics showed frequent fadeouts. Our findings stress the importance of malaria surveillance and control in these larger communities.

Resumo Limpo

mosquitoborn diseas spread might exhibit irregular epidem front caus ecolog heterogen risk factor determin plasmodium vivax infect spread northeastern venezuela use state transit index sti detect spatial locat malaria incid boundari dynam time evalu role popul size diseas persist boundari locat malaria found high spatial variabl wave infect observ spatial mosaic larg small nearbi local due strong asynchroni epidem dynam host popul result suggest epidem spatial diffus follow hierarchi larg popul villag season parasit fadeout smaller less popul local infect outbreak irregular diseas dynam show frequent fadeout find stress import malaria surveil control larger communiti

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