Sci Data - Global database of leishmaniasis occurrence locations, 1960-2012.

Tópicos

{ spatial(1525) area(1432) region(1030) }
{ data(2317) use(1299) case(1017) }
{ data(1737) use(1416) pattern(1282) }
{ search(2224) databas(1162) retriev(909) }
{ health(3367) inform(1360) care(1135) }
{ intervent(3218) particip(2042) group(1664) }
{ can(981) present(881) function(850) }
{ patient(2315) diseas(1263) diabet(1191) }
{ data(1714) softwar(1251) tool(1186) }
{ data(3963) clinic(1234) research(1004) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ sequenc(1873) structur(1644) protein(1328) }
{ take(945) account(800) differ(722) }
{ framework(1458) process(801) describ(734) }
{ concept(1167) ontolog(924) domain(897) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ import(1318) role(1303) understand(862) }
{ state(1844) use(1261) util(961) }
{ data(3008) multipl(1320) sourc(1022) }
{ use(1733) differ(960) four(931) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
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{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
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{ network(2748) neural(1063) input(814) }
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{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
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{ 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) }
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{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
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{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ 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) }
{ 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) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ 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) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ 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) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The leishmaniases are neglected tropical diseases of significant public health importance. However, information on their global occurrence is disparate and sparse. This database represents an attempt to collate reported leishmaniasis occurrences from 1960 to 2012. Methodology for the collection of data from the literature, abstraction of case locations and data processing procedures are described here. In addition, strain archives and online data resources were accessed. A total of 12,563 spatially and temporally unique occurrences of both cutaneous and visceral leishmaniasis comprise the database, ranging in geographic scale from villages to states. These data can be used for a variety of mapping and spatial analyses covering multiple resolutions.

Resumo Limpo

leishmanias neglect tropic diseas signific public health import howev inform global occurr dispar spars databas repres attempt collat report leishmaniasi occurr methodolog collect data literatur abstract case locat data process procedur describ addit strain archiv onlin data resourc access total spatial tempor uniqu occurr cutan viscer leishmaniasi compris databas rang geograph scale villag state data can use varieti map spatial analys cover multipl resolut

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