The development of Malaria Early Warning Systems for Africa
Identifieur interne : 005004 ( Istex/Corpus ); précédent : 005003; suivant : 005005The development of Malaria Early Warning Systems for Africa
Auteurs : Madeleine C. Thomson ; Stephen J. ConnorSource :
- Trends in Parasitology [ 1471-4922 ] ; 2001.
English descriptors
- KwdEn :
- Anthelmintic, Anthelmintic resistance, Cambridge university press, Case surveillance, Cerebral malaria, Childhood deaths, Collective immunity, Difference vegetation index, Different sectors, Drug resistance, Effective drugs, Effective health care, Effective treatment, Elsevier science, Environmental data, Environmental factors, Environmental information systems, Epidemic, Epidemic prediction, Excess rainfall, Falciparum, Front matter, Geographical information systems, Good indicator, Good indicators, Haemonchus contortus, Health data, Health services, Herd immunity, Highland areas, Important determinant, Indicator, Infectious diseases, Liverpool school, Major impact, Malaria, Malaria cases, Malaria control, Malaria early warning systems, Malaria endemicity, Malaria epidemic, Malaria epidemics, Malaria infection, Malaria morbidity, Malaria mortality, Malaria prevalence, Malaria stratification, Malaria transmission, Malaria transmission intensity, Mapping malaria risk, Maternal mortality, Mews, Modelling, Morbidity, Mortality, Mosquito, National impregnated bednet programme, Natural populations, Onchocerca volvulus, Onchocerciasis, Onchocerciasis control programme, Other sectors, Parasite, Parasite development rates, Parasite population, Parasitol, Parasitology, Parasitology september, Pembroke place, Periodic appearance, Plasmodium falciparum, Population dynamics, Pregnant women, Prevalence, Programme, Rainfall, Rainfall estimates, Rainfall levels, Review trends, Rural area, Satellite data, Seasonal, Seasonal climate forecasting, Seasonal climate forecasts, Seasonal forecasting, Seasonal forecasts, September, Severe malaria, Severe malaria morbidity, Significant predictor, Southern oscillation, Sporozoite, Sporozoite rate, Sporozoite rates, Statistical approaches, Statistical models, Strong evidence, Surface water, Techniques & Methods, Thomson, Timely fashion, Trends parasitol, Tropical medicine, Tropical regions, Vector control, Vector densities, Vector density, Vector population, Vector survival, Warning systems, Weather monitoring, West africa, epidemic prediction/prevention, malaria control, morbidity, mortality.
- Teeft :
- Anthelmintic, Anthelmintic resistance, Cambridge university press, Case surveillance, Cerebral malaria, Childhood deaths, Collective immunity, Difference vegetation index, Different sectors, Drug resistance, Effective drugs, Effective health care, Effective treatment, Elsevier science, Environmental data, Environmental factors, Environmental information systems, Epidemic, Epidemic prediction, Excess rainfall, Falciparum, Front matter, Geographical information systems, Good indicator, Good indicators, Haemonchus contortus, Health data, Health services, Herd immunity, Highland areas, Important determinant, Indicator, Infectious diseases, Liverpool school, Major impact, Malaria, Malaria cases, Malaria control, Malaria endemicity, Malaria epidemic, Malaria epidemics, Malaria infection, Malaria morbidity, Malaria mortality, Malaria prevalence, Malaria stratification, Malaria transmission, Malaria transmission intensity, Mapping malaria risk, Maternal mortality, Mews, Modelling, Morbidity, Mortality, Mosquito, National impregnated bednet programme, Natural populations, Onchocerca volvulus, Onchocerciasis, Onchocerciasis control programme, Other sectors, Parasite, Parasite development rates, Parasite population, Parasitol, Parasitology, Parasitology september, Pembroke place, Periodic appearance, Plasmodium falciparum, Population dynamics, Pregnant women, Prevalence, Programme, Rainfall, Rainfall estimates, Rainfall levels, Review trends, Rural area, Satellite data, Seasonal, Seasonal climate forecasting, Seasonal climate forecasts, Seasonal forecasting, Seasonal forecasts, September, Severe malaria, Severe malaria morbidity, Significant predictor, Southern oscillation, Sporozoite, Sporozoite rate, Sporozoite rates, Statistical approaches, Statistical models, Strong evidence, Surface water, Thomson, Timely fashion, Trends parasitol, Tropical medicine, Tropical regions, Vector control, Vector densities, Vector density, Vector population, Vector survival, Warning systems, Weather monitoring, West africa.
Abstract
Abstract: Current efforts to predict malaria epidemics focus on the role weather anomalies can play in epidemic prediction. Alongside weather monitoring and seasonal climate forecasts, epidemiological, social and environmental factors can also play a role in predicting the timing and severity of malaria epidemics. Such factors can be incorporated into a framework for malaria early warning.
Url:
DOI: 10.1016/S1471-4922(01)02077-3
Links to Exploration step
ISTEX:F61BA0EBAD22496A217F28C225C8DC3142643A24Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">The development of Malaria Early Warning Systems for Africa</title><author><name sortKey="Thomson, Madeleine C" sort="Thomson, Madeleine C" uniqKey="Thomson M" first="Madeleine C" last="Thomson">Madeleine C. Thomson</name><affiliation><mods:affiliation>MALSAT Research Group, Liverpool School of Tropical Medicine, Pembroke place, Liverpool, UK L35QA</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: mthomson@liv.ac.uk</mods:affiliation></affiliation></author><author><name sortKey="Connor, Stephen J" sort="Connor, Stephen J" uniqKey="Connor S" first="Stephen J" last="Connor">Stephen J. Connor</name><affiliation><mods:affiliation>DFID Malaria Knowledge Programme, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK L35QA</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:F61BA0EBAD22496A217F28C225C8DC3142643A24</idno><date when="2001" year="2001">2001</date><idno type="doi">10.1016/S1471-4922(01)02077-3</idno><idno type="url">https://api.istex.fr/document/F61BA0EBAD22496A217F28C225C8DC3142643A24/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">005004</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">005004</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">The development of Malaria Early Warning Systems for Africa</title><author><name sortKey="Thomson, Madeleine C" sort="Thomson, Madeleine C" uniqKey="Thomson M" first="Madeleine C" last="Thomson">Madeleine C. Thomson</name><affiliation><mods:affiliation>MALSAT Research Group, Liverpool School of Tropical Medicine, Pembroke place, Liverpool, UK L35QA</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: mthomson@liv.ac.uk</mods:affiliation></affiliation></author><author><name sortKey="Connor, Stephen J" sort="Connor, Stephen J" uniqKey="Connor S" first="Stephen J" last="Connor">Stephen J. Connor</name><affiliation><mods:affiliation>DFID Malaria Knowledge Programme, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK L35QA</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Trends in Parasitology</title><title level="j" type="abbrev">TREPAR</title><idno type="ISSN">1471-4922</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="2001">2001</date><biblScope unit="volume">17</biblScope><biblScope unit="issue">9</biblScope><biblScope unit="page" from="438">438</biblScope><biblScope unit="page" to="445">445</biblScope></imprint><idno type="ISSN">1471-4922</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1471-4922</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Anthelmintic</term><term>Anthelmintic resistance</term><term>Cambridge university press</term><term>Case surveillance</term><term>Cerebral malaria</term><term>Childhood deaths</term><term>Collective immunity</term><term>Difference vegetation index</term><term>Different sectors</term><term>Drug resistance</term><term>Effective drugs</term><term>Effective health care</term><term>Effective treatment</term><term>Elsevier science</term><term>Environmental data</term><term>Environmental factors</term><term>Environmental information systems</term><term>Epidemic</term><term>Epidemic prediction</term><term>Excess rainfall</term><term>Falciparum</term><term>Front matter</term><term>Geographical information systems</term><term>Good indicator</term><term>Good indicators</term><term>Haemonchus contortus</term><term>Health data</term><term>Health services</term><term>Herd immunity</term><term>Highland areas</term><term>Important determinant</term><term>Indicator</term><term>Infectious diseases</term><term>Liverpool school</term><term>Major impact</term><term>Malaria</term><term>Malaria cases</term><term>Malaria control</term><term>Malaria early warning systems</term><term>Malaria endemicity</term><term>Malaria epidemic</term><term>Malaria epidemics</term><term>Malaria infection</term><term>Malaria morbidity</term><term>Malaria mortality</term><term>Malaria prevalence</term><term>Malaria stratification</term><term>Malaria transmission</term><term>Malaria transmission intensity</term><term>Mapping malaria risk</term><term>Maternal mortality</term><term>Mews</term><term>Modelling</term><term>Morbidity</term><term>Mortality</term><term>Mosquito</term><term>National impregnated bednet programme</term><term>Natural populations</term><term>Onchocerca volvulus</term><term>Onchocerciasis</term><term>Onchocerciasis control programme</term><term>Other sectors</term><term>Parasite</term><term>Parasite development rates</term><term>Parasite population</term><term>Parasitol</term><term>Parasitology</term><term>Parasitology september</term><term>Pembroke place</term><term>Periodic appearance</term><term>Plasmodium falciparum</term><term>Population dynamics</term><term>Pregnant women</term><term>Prevalence</term><term>Programme</term><term>Rainfall</term><term>Rainfall estimates</term><term>Rainfall levels</term><term>Review trends</term><term>Rural area</term><term>Satellite data</term><term>Seasonal</term><term>Seasonal climate forecasting</term><term>Seasonal climate forecasts</term><term>Seasonal forecasting</term><term>Seasonal forecasts</term><term>September</term><term>Severe malaria</term><term>Severe malaria morbidity</term><term>Significant predictor</term><term>Southern oscillation</term><term>Sporozoite</term><term>Sporozoite rate</term><term>Sporozoite rates</term><term>Statistical approaches</term><term>Statistical models</term><term>Strong evidence</term><term>Surface water</term><term>Techniques & Methods</term><term>Thomson</term><term>Timely fashion</term><term>Trends parasitol</term><term>Tropical medicine</term><term>Tropical regions</term><term>Vector control</term><term>Vector densities</term><term>Vector density</term><term>Vector population</term><term>Vector survival</term><term>Warning systems</term><term>Weather monitoring</term><term>West africa</term><term>epidemic prediction/prevention</term><term>malaria control</term><term>morbidity</term><term>mortality</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Anthelmintic</term><term>Anthelmintic resistance</term><term>Cambridge university press</term><term>Case surveillance</term><term>Cerebral malaria</term><term>Childhood deaths</term><term>Collective immunity</term><term>Difference vegetation index</term><term>Different sectors</term><term>Drug resistance</term><term>Effective drugs</term><term>Effective health care</term><term>Effective treatment</term><term>Elsevier science</term><term>Environmental data</term><term>Environmental factors</term><term>Environmental information systems</term><term>Epidemic</term><term>Epidemic prediction</term><term>Excess rainfall</term><term>Falciparum</term><term>Front matter</term><term>Geographical information systems</term><term>Good indicator</term><term>Good indicators</term><term>Haemonchus contortus</term><term>Health data</term><term>Health services</term><term>Herd immunity</term><term>Highland areas</term><term>Important determinant</term><term>Indicator</term><term>Infectious diseases</term><term>Liverpool school</term><term>Major impact</term><term>Malaria</term><term>Malaria cases</term><term>Malaria control</term><term>Malaria endemicity</term><term>Malaria epidemic</term><term>Malaria epidemics</term><term>Malaria infection</term><term>Malaria morbidity</term><term>Malaria mortality</term><term>Malaria prevalence</term><term>Malaria stratification</term><term>Malaria transmission</term><term>Malaria transmission intensity</term><term>Mapping malaria risk</term><term>Maternal mortality</term><term>Mews</term><term>Modelling</term><term>Morbidity</term><term>Mortality</term><term>Mosquito</term><term>National impregnated bednet programme</term><term>Natural populations</term><term>Onchocerca volvulus</term><term>Onchocerciasis</term><term>Onchocerciasis control programme</term><term>Other sectors</term><term>Parasite</term><term>Parasite development rates</term><term>Parasite population</term><term>Parasitol</term><term>Parasitology</term><term>Parasitology september</term><term>Pembroke place</term><term>Periodic appearance</term><term>Plasmodium falciparum</term><term>Population dynamics</term><term>Pregnant women</term><term>Prevalence</term><term>Programme</term><term>Rainfall</term><term>Rainfall estimates</term><term>Rainfall levels</term><term>Review trends</term><term>Rural area</term><term>Satellite data</term><term>Seasonal</term><term>Seasonal climate forecasting</term><term>Seasonal climate forecasts</term><term>Seasonal forecasting</term><term>Seasonal forecasts</term><term>September</term><term>Severe malaria</term><term>Severe malaria morbidity</term><term>Significant predictor</term><term>Southern oscillation</term><term>Sporozoite</term><term>Sporozoite rate</term><term>Sporozoite rates</term><term>Statistical approaches</term><term>Statistical models</term><term>Strong evidence</term><term>Surface water</term><term>Thomson</term><term>Timely fashion</term><term>Trends parasitol</term><term>Tropical medicine</term><term>Tropical regions</term><term>Vector control</term><term>Vector densities</term><term>Vector density</term><term>Vector population</term><term>Vector survival</term><term>Warning systems</term><term>Weather monitoring</term><term>West africa</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Current efforts to predict malaria epidemics focus on the role weather anomalies can play in epidemic prediction. Alongside weather monitoring and seasonal climate forecasts, epidemiological, social and environmental factors can also play a role in predicting the timing and severity of malaria epidemics. Such factors can be incorporated into a framework for malaria early warning.</div></front></TEI><istex><corpusName>elsevier</corpusName><keywords><teeft><json:string>malaria</json:string><json:string>morbidity</json:string><json:string>onchocerciasis</json:string><json:string>malaria transmission</json:string><json:string>programme</json:string><json:string>sporozoite</json:string><json:string>parasitology</json:string><json:string>malaria epidemics</json:string><json:string>mews</json:string><json:string>parasitol</json:string><json:string>september</json:string><json:string>falciparum</json:string><json:string>parasitology september</json:string><json:string>seasonal climate forecasts</json:string><json:string>parasite</json:string><json:string>anthelmintic</json:string><json:string>sporozoite rates</json:string><json:string>modelling</json:string><json:string>seasonal forecasting</json:string><json:string>warning systems</json:string><json:string>malaria control</json:string><json:string>mosquito</json:string><json:string>malaria epidemic</json:string><json:string>onchocerca volvulus</json:string><json:string>vector densities</json:string><json:string>west africa</json:string><json:string>malaria prevalence</json:string><json:string>review trends</json:string><json:string>rainfall</json:string><json:string>onchocerciasis control programme</json:string><json:string>herd immunity</json:string><json:string>infectious diseases</json:string><json:string>seasonal forecasts</json:string><json:string>anthelmintic resistance</json:string><json:string>good indicator</json:string><json:string>cambridge university press</json:string><json:string>malaria cases</json:string><json:string>environmental factors</json:string><json:string>vector survival</json:string><json:string>vector density</json:string><json:string>malaria morbidity</json:string><json:string>malaria endemicity</json:string><json:string>satellite data</json:string><json:string>environmental information systems</json:string><json:string>southern oscillation</json:string><json:string>cerebral malaria</json:string><json:string>severe malaria</json:string><json:string>malaria infection</json:string><json:string>pregnant women</json:string><json:string>health services</json:string><json:string>excess rainfall</json:string><json:string>thomson</json:string><json:string>drug resistance</json:string><json:string>front matter</json:string><json:string>elsevier science</json:string><json:string>natural populations</json:string><json:string>rainfall estimates</json:string><json:string>population dynamics</json:string><json:string>timely fashion</json:string><json:string>vector population</json:string><json:string>parasite development rates</json:string><json:string>parasite population</json:string><json:string>sporozoite rate</json:string><json:string>strong evidence</json:string><json:string>tropical regions</json:string><json:string>periodic appearance</json:string><json:string>statistical approaches</json:string><json:string>statistical models</json:string><json:string>seasonal climate forecasting</json:string><json:string>different sectors</json:string><json:string>collective immunity</json:string><json:string>major impact</json:string><json:string>surface water</json:string><json:string>malaria transmission intensity</json:string><json:string>environmental data</json:string><json:string>vector control</json:string><json:string>effective drugs</json:string><json:string>rainfall levels</json:string><json:string>mapping malaria risk</json:string><json:string>malaria stratification</json:string><json:string>effective treatment</json:string><json:string>good indicators</json:string><json:string>geographical information systems</json:string><json:string>national impregnated bednet programme</json:string><json:string>epidemic prediction</json:string><json:string>case surveillance</json:string><json:string>significant predictor</json:string><json:string>plasmodium falciparum</json:string><json:string>other sectors</json:string><json:string>important determinant</json:string><json:string>severe malaria morbidity</json:string><json:string>haemonchus contortus</json:string><json:string>liverpool school</json:string><json:string>maternal mortality</json:string><json:string>childhood deaths</json:string><json:string>health data</json:string><json:string>rural area</json:string><json:string>effective health care</json:string><json:string>tropical medicine</json:string><json:string>malaria mortality</json:string><json:string>pembroke place</json:string><json:string>highland areas</json:string><json:string>trends parasitol</json:string><json:string>weather monitoring</json:string><json:string>difference vegetation index</json:string><json:string>prevalence</json:string><json:string>indicator</json:string><json:string>epidemic</json:string><json:string>seasonal</json:string><json:string>mortality</json:string></teeft></keywords><author><json:item><name>Madeleine C Thomson</name><affiliations><json:string>MALSAT Research Group, Liverpool School of Tropical Medicine, Pembroke place, Liverpool, UK L35QA</json:string><json:string>E-mail: mthomson@liv.ac.uk</json:string></affiliations></json:item><json:item><name>Stephen J Connor</name><affiliations><json:string>DFID Malaria Knowledge Programme, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK L35QA</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Review</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>malaria control</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>epidemic prediction/prevention</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>mortality</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>morbidity</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Malaria early warning systems</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Parasitology</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Techniques & Methods</value></json:item></subject><language><json:string>eng</json:string></language><originalGenre><json:string>Review article</json:string></originalGenre><abstract>Current efforts to predict malaria epidemics focus on the role weather anomalies can play in epidemic prediction. Alongside weather monitoring and seasonal climate forecasts, epidemiological, social and environmental factors can also play a role in predicting the timing and severity of malaria epidemics. Such factors can be incorporated into a framework for malaria early warning.</abstract><qualityIndicators><score>5.66</score><pdfVersion>1.2</pdfVersion><pdfPageSize>576 x 793 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>8</keywordCount><abstractCharCount>382</abstractCharCount><pdfWordCount>6290</pdfWordCount><pdfCharCount>43041</pdfCharCount><pdfPageCount>8</pdfPageCount><abstractWordCount>55</abstractWordCount></qualityIndicators><title>The development of Malaria Early Warning Systems for Africa</title><pii><json:string>S1471-4922(01)02077-3</json:string></pii><genre><json:string>review-article</json:string></genre><host><title>Trends in Parasitology</title><language><json:string>unknown</json:string></language><publicationDate>2001</publicationDate><issn><json:string>1471-4922</json:string></issn><pii><json:string>S1471-4922(00)X0008-6</json:string></pii><volume>17</volume><issue>9</issue><pages><first>438</first><last>445</last></pages><genre><json:string>journal</json:string></genre></host><categories><wos><json:string>science</json:string><json:string>parasitology</json:string></wos><scienceMetrix><json:string>health sciences</json:string><json:string>biomedical research</json:string><json:string>mycology & parasitology</json:string></scienceMetrix><inist><json:string>sciences appliquees, technologies et medecines</json:string><json:string>sciences biologiques et medicales</json:string><json:string>sciences medicales</json:string><json:string>psychopathologie. psychiatrie</json:string></inist></categories><publicationDate>2001</publicationDate><copyrightDate>2001</copyrightDate><doi><json:string>10.1016/S1471-4922(01)02077-3</json:string></doi><id>F61BA0EBAD22496A217F28C225C8DC3142643A24</id><score>1</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/F61BA0EBAD22496A217F28C225C8DC3142643A24/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/F61BA0EBAD22496A217F28C225C8DC3142643A24/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/F61BA0EBAD22496A217F28C225C8DC3142643A24/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">The development of Malaria Early Warning Systems for Africa</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>©2001 Elsevier Science Ltd</p></availability><date>2001</date></publicationStmt><notesStmt><note type="content">Section title: Review</note><note type="content">Fig. 1: Sporozoite rates in Anopheles gambiae (determined by ELISA) and malaria prevalence rates in children under five years of age from Gambian non-treated villages involved in the national impregnated bednet programme in 1991 (green dots) and 1992 (orange dots) (R2 = 0.8016, p <0.001). The number of mosquitoes sampled ranged from 142 to 5041 per village, and 30 to 60 children were tested for malaria in each village. The results demonstrated a positive relationship between malaria prevalence and sporozoites rates in the children studied 32,60.</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">The development of Malaria Early Warning Systems for Africa</title><author xml:id="author-0000"><persName><forename type="first">Madeleine C</forename><surname>Thomson</surname></persName><email>mthomson@liv.ac.uk</email><affiliation>MALSAT Research Group, Liverpool School of Tropical Medicine, Pembroke place, Liverpool, UK L35QA</affiliation></author><author xml:id="author-0001"><persName><forename type="first">Stephen J</forename><surname>Connor</surname></persName><affiliation>DFID Malaria Knowledge Programme, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK L35QA</affiliation></author><idno type="istex">F61BA0EBAD22496A217F28C225C8DC3142643A24</idno><idno type="DOI">10.1016/S1471-4922(01)02077-3</idno><idno type="PII">S1471-4922(01)02077-3</idno></analytic><monogr><title level="j">Trends in Parasitology</title><title level="j" type="abbrev">TREPAR</title><idno type="pISSN">1471-4922</idno><idno type="PII">S1471-4922(00)X0008-6</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="2001"></date><biblScope unit="volume">17</biblScope><biblScope unit="issue">9</biblScope><biblScope unit="page" from="438">438</biblScope><biblScope unit="page" to="445">445</biblScope></imprint></monogr></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2001</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Current efforts to predict malaria epidemics focus on the role weather anomalies can play in epidemic prediction. Alongside weather monitoring and seasonal climate forecasts, epidemiological, social and environmental factors can also play a role in predicting the timing and severity of malaria epidemics. Such factors can be incorporated into a framework for malaria early warning.</p></abstract><textClass><keywords scheme="keyword"><list><head>article-category</head><item><term>Review</term></item></list></keywords></textClass><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Keywords</head><item><term>malaria control</term></item><item><term>epidemic prediction/prevention</term></item><item><term>mortality</term></item><item><term>morbidity</term></item><item><term>Malaria early warning systems</term></item></list></keywords></textClass><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Keywords</head><item><term>Parasitology</term></item><item><term>Techniques & Methods</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2001">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/F61BA0EBAD22496A217F28C225C8DC3142643A24/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: ce:floats; body; tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"><istex:entity SYSTEM="gr1" NDATA="IMAGE" name="gr1"></istex:entity></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="rev" xml:lang="en"><item-info><jid>TREPAR</jid><aid>00002077</aid><ce:pii>S1471-4922(01)02077-3</ce:pii><ce:doi>10.1016/S1471-4922(01)02077-3</ce:doi><ce:copyright type="full-transfer" year="2001">Elsevier Science Ltd</ce:copyright><ce:doctopics><ce:doctopic><ce:text>Review</ce:text></ce:doctopic></ce:doctopics></item-info><head><ce:dochead><ce:textfn>Review</ce:textfn></ce:dochead><ce:title>The development of Malaria Early Warning Systems for Africa</ce:title><ce:author-group><ce:author><ce:given-name>Madeleine C</ce:given-name><ce:surname>Thomson</ce:surname><ce:cross-ref refid="AFF1"><ce:sup>a</ce:sup></ce:cross-ref><ce:e-address type="email">mthomson@liv.ac.uk</ce:e-address></ce:author><ce:author><ce:given-name>Stephen J</ce:given-name><ce:surname>Connor</ce:surname><ce:cross-ref refid="AFF2"><ce:sup>b</ce:sup></ce:cross-ref></ce:author><ce:affiliation id="AFF1"><ce:label>a</ce:label><ce:textfn>MALSAT Research Group, Liverpool School of Tropical Medicine, Pembroke place, Liverpool, UK L35QA</ce:textfn></ce:affiliation><ce:affiliation id="AFF2"><ce:label>b</ce:label><ce:textfn>DFID Malaria Knowledge Programme, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK L35QA</ce:textfn></ce:affiliation></ce:author-group><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>Current efforts to predict malaria epidemics focus on the role weather anomalies can play in epidemic prediction. Alongside weather monitoring and seasonal climate forecasts, epidemiological, social and environmental factors can also play a role in predicting the timing and severity of malaria epidemics. Such factors can be incorporated into a framework for malaria early warning.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="keyword"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>malaria control</ce:text></ce:keyword><ce:keyword><ce:text>epidemic prediction/prevention</ce:text></ce:keyword><ce:keyword><ce:text>mortality</ce:text></ce:keyword><ce:keyword><ce:text>morbidity</ce:text></ce:keyword><ce:keyword><ce:text>Malaria early warning systems</ce:text></ce:keyword></ce:keywords><ce:keywords class="idt"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Parasitology</ce:text></ce:keyword><ce:keyword><ce:text>Techniques & Methods</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>The development of Malaria Early Warning Systems for Africa</title></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>The development of Malaria Early Warning Systems for Africa</title></titleInfo><name type="personal"><namePart type="given">Madeleine C</namePart><namePart type="family">Thomson</namePart><affiliation>MALSAT Research Group, Liverpool School of Tropical Medicine, Pembroke place, Liverpool, UK L35QA</affiliation><affiliation>E-mail: mthomson@liv.ac.uk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Stephen J</namePart><namePart type="family">Connor</namePart><affiliation>DFID Malaria Knowledge Programme, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK L35QA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="review-article" displayLabel="Review article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-L5L7X3NF-P">review-article</genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">2001</dateIssued><copyrightDate encoding="w3cdtf">2001</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><abstract lang="en">Abstract: Current efforts to predict malaria epidemics focus on the role weather anomalies can play in epidemic prediction. Alongside weather monitoring and seasonal climate forecasts, epidemiological, social and environmental factors can also play a role in predicting the timing and severity of malaria epidemics. Such factors can be incorporated into a framework for malaria early warning.</abstract><note type="content">Section title: Review</note><note type="content">Fig. 1: Sporozoite rates in Anopheles gambiae (determined by ELISA) and malaria prevalence rates in children under five years of age from Gambian non-treated villages involved in the national impregnated bednet programme in 1991 (green dots) and 1992 (orange dots) (R2 = 0.8016, p <0.001). The number of mosquitoes sampled ranged from 142 to 5041 per village, and 30 to 60 children were tested for malaria in each village. The results demonstrated a positive relationship between malaria prevalence and sporozoites rates in the children studied 32,60.</note><subject><genre>article-category</genre><topic>Review</topic></subject><subject lang="en"><genre>Keywords</genre><topic>malaria control</topic><topic>epidemic prediction/prevention</topic><topic>mortality</topic><topic>morbidity</topic><topic>Malaria early warning systems</topic></subject><subject lang="en"><genre>Keywords</genre><topic>Parasitology</topic><topic>Techniques & Methods</topic></subject><relatedItem type="host"><titleInfo><title>Trends in Parasitology</title></titleInfo><titleInfo type="abbreviated"><title>TREPAR</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">20010901</dateIssued></originInfo><identifier type="ISSN">1471-4922</identifier><identifier type="PII">S1471-4922(00)X0008-6</identifier><part><date>20010901</date><detail type="volume"><number>17</number><caption>vol.</caption></detail><detail type="issue"><number>9</number><caption>no.</caption></detail><extent unit="issue-pages"><start>401</start><end>456</end></extent><extent unit="pages"><start>438</start><end>445</end></extent></part></relatedItem><identifier type="istex">F61BA0EBAD22496A217F28C225C8DC3142643A24</identifier><identifier type="ark">ark:/67375/6H6-74V03XH7-L</identifier><identifier type="DOI">10.1016/S1471-4922(01)02077-3</identifier><identifier type="PII">S1471-4922(01)02077-3</identifier><accessCondition type="use and reproduction" contentType="copyright">©2001 Elsevier Science Ltd</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-HKKZVM7B-M">elsevier</recordContentSource><recordOrigin>Elsevier Science Ltd, ©2001</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/document/F61BA0EBAD22496A217F28C225C8DC3142643A24/metadata/json</uri></json:item></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Sante/explor/SidaSubSaharaV1/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 005004 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 005004 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Sante
|area= SidaSubSaharaV1
|flux= Istex
|étape= Corpus
|type= RBID
|clé= ISTEX:F61BA0EBAD22496A217F28C225C8DC3142643A24
|texte= The development of Malaria Early Warning Systems for Africa
}}
| This area was generated with Dilib version V0.6.32. |  |