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  1. AU="Lucero, D E"
  2. AU="Isik, C"
  3. AU="Lange, Lana"
  4. AU="Morris, Ray"
  5. AU="Sun, Xiankai"
  6. AU=Jeggo Penny A.
  7. AU="Kanthamneni, Naveen"
  8. AU="Di Lorenzo, Raffaele"
  9. AU="Tiraboschi, Juan M"
  10. AU="Xiang, Jinzhu"
  11. AU="Diehl, Kyra"
  12. AU="Aparicio-Yuste, Raul"
  13. AU="Jiang, Gengbo"
  14. AU=Murrell Dedee F AU=Murrell Dedee F
  15. AU=Gupta Riya
  16. AU="Elmasry, Dalia M A" AU="Elmasry, Dalia M A"
  17. AU=Rosa Rafael Fabiano Machado
  18. AU="Bhatia, Vishwas"
  19. AU="Buchwitz, Michael"
  20. AU="Sadrozinski, H-F W."
  21. AU="Allan, Rachel"
  22. AU="Ma, Jiele"
  23. AU="Bizjak, Isabella"
  24. AU="Pelucchi, Paride"
  25. AU="Krug, Anne Barbara"
  26. AU="Pikridas, M"
  27. AU="Adams, Jonathan D"
  28. AU="Esquivel-Muelbert, A."
  29. AU="Khan, Meraj Alam"
  30. AU="Bullard, Stevan"
  31. AU="Wang, Peter H"
  32. AU="Preto, Jordane"
  33. AU="Pierce, Shaketha"
  34. AU="Sankar, Jishnu"
  35. AU="Yahagi, Naohisa"
  36. AU=Pinho Juliana
  37. AU="Brennan, Anna"
  38. AU="Lee, Theresa M"
  39. AU="Chunqing Ou"
  40. AU="Gwynn, Simon"
  41. AU="Holper, Sarah"
  42. AU="Haider, Farag Ibrahim"
  43. AU="Rice, Jordin L"
  44. AU="Gong, Xingguo"
  45. AU=Rother Magdalena B.
  46. AU="Petrov, Ksenia"
  47. AU="Rijneveld, R"
  48. AU=Lopez-Martinez Briceida
  49. AU=Astone Pia
  50. AU="Amaral, V"

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  1. Artikel ; Online: Spatiotemporal Co-occurrence of Flanders and West Nile Viruses Within Culex Populations in Shelby County, Tennessee.

    Lucero, D E / Carlson, T C / Delisle, J / Poindexter, S / Jones, T F / Moncayo, A C

    Journal of medical entomology

    2016  Band 53, Heft 3, Seite(n) 526–532

    Abstract: West Nile virus (WNV) and Flanders virus (FLAV) can cocirculate in Culex mosquitoes in parts of North America. A large dataset of mosquito pools tested for WNV and FLAV was queried to understand the spatiotemporal relationship between these two viruses ... ...

    Abstract West Nile virus (WNV) and Flanders virus (FLAV) can cocirculate in Culex mosquitoes in parts of North America. A large dataset of mosquito pools tested for WNV and FLAV was queried to understand the spatiotemporal relationship between these two viruses in Shelby County, TN. We found strong evidence of global clustering (i.e., spatial autocorrelation) and overlapping of local clustering (i.e., Hot Spots based on Getis Ord Gi*) of maximum likelihood estimates (MLE) of infection rates (IR) during 2008-2013. Temporally, FLAV emerges and peaks on average 10.2 wk prior to WNV based on IR. Higher levels of WNV IR were detected within 3,000 m of FLAV-positive pool buffers than outside these buffers.
    Mesh-Begriff(e) Animal Distribution ; Animals ; Culex/growth & development ; Culex/virology ; Female ; Insect Vectors/growth & development ; Insect Vectors/virology ; Male ; Rhabdoviridae/genetics ; Rhabdoviridae/isolation & purification ; Rhabdoviridae/physiology ; Seasons ; Tennessee ; West Nile virus/genetics ; West Nile virus/isolation & purification ; West Nile virus/physiology
    Sprache Englisch
    Erscheinungsdatum 2016
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, P.H.S.
    ZDB-ID 410635-0
    ISSN 1938-2928 ; 0022-2585
    ISSN (online) 1938-2928
    ISSN 0022-2585
    DOI 10.1093/jme/tjw011
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Geospatial cluster analyses of pneumonia-associated hospitalisations among adults in New York City, 2010-2014.

    Kache, P A / Julien, T / Corrado, R E / Vora, N M / Daskalakis, D C / Varma, J K / Lucero, D E

    Epidemiology and infection

    2018  Band 147, Seite(n) e51

    Abstract: Pneumonia is a leading cause of death in New York City (NYC). We identified spatial clusters of pneumonia-associated hospitalisation for persons residing in NYC, aged ⩾18 years during 2010-2014. We detected pneumonia-associated hospitalisations using an ... ...

    Abstract Pneumonia is a leading cause of death in New York City (NYC). We identified spatial clusters of pneumonia-associated hospitalisation for persons residing in NYC, aged ⩾18 years during 2010-2014. We detected pneumonia-associated hospitalisations using an all-payer inpatient dataset. Using geostatistical semivariogram modelling, local Moran's I cluster analyses and χ2 tests, we characterised differences between 'hot spots' and 'cold spots' for pneumonia-associated hospitalisations. During 2010-2014, there were 141 730 pneumonia-associated hospitalisations across 188 NYC neighbourhoods, of which 43.5% (N = 61 712) were sub-classified as severe. Hot spots of pneumonia-associated hospitalisation spanned 26 neighbourhoods in the Bronx, Manhattan and Staten Island, whereas cold spots were found in lower Manhattan and northeastern Queens. We identified hot spots of severe pneumonia-associated hospitalisation in the northern Bronx and the northern tip of Staten Island. For severe pneumonia-associated hospitalisations, hot-spot patients were of lower mean age and a greater proportion identified as non-Hispanic Black compared with cold spot patients; additionally, hot-spot patients had a longer hospital stay and a greater proportion experienced in-hospital death compared with cold-spot patients. Pneumonia prevention efforts within NYC should consider examining the reasons for higher rates in hot-spot neighbourhoods, and focus interventions towards the Bronx, northern Manhattan and Staten Island.
    Sprache Englisch
    Erscheinungsdatum 2018-11-19
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 632982-2
    ISSN 1469-4409 ; 0950-2688
    ISSN (online) 1469-4409
    ISSN 0950-2688
    DOI 10.1017/S0950268818003060
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel: Household model of Chagas disease vectors (Hemiptera: Reduviidae) considering domestic, peridomestic, and sylvatic vector populations.

    Stevens, L / Rizzo, D M / Lucero, D E / Pizarro, J C

    Journal of medical entomology

    2013  Band 50, Heft 4, Seite(n) 907–915

    Abstract: ABSTRACT Disease transmission is difficult to model because most vectors and hosts have different generation times. Chagas disease is such a situation, where insect vectors have 1-2 generations annually and mammalian hosts, including humans, can live for ...

    Abstract ABSTRACT Disease transmission is difficult to model because most vectors and hosts have different generation times. Chagas disease is such a situation, where insect vectors have 1-2 generations annually and mammalian hosts, including humans, can live for decades. The hemataphagous triatominae vectors (Hemiptera: Reduviidae) of the causative parasite Trypanosoma cruzi (Kinetoplastida: Trypanosomatidae) usually feed on sleeping hosts, making vector infestation of houses, peridomestic areas, and wild animal burrows a central factor in transmission. Because of difficulties with different generation times, we developed a model considering the dwelling as the unit of infection, changing the dynamics from an indirect to a direct transmission model. In some regions, vectors only infest houses; in others, they infest corrals; and in some regions, they also infest wild animal burrows. We examined the effect of sylvatic and peridomestic vector populations on household infestation rates. Both sylvatic and peridomestic vectors increase house infestation rates, sylvatic much more than peridomestic, as measured by the reproductive number R0. The efficacy of manipulating parameters in the model to control vector populations was examined. When R0 > 1, the number of infested houses increases. The presence of sylvatic vectors increases R0 by at least an order of magnitude. When there are no sylvatic vectors, spraying rate is the most influential parameter. Spraying rate is relatively unimportant when there are sylvatic vectors; in this case, community size, especially the ratio of houses to sylvatic burrows, is most important. The application of this modeling approach to other parasites and enhancements of the model are discussed.
    Mesh-Begriff(e) Animal Distribution ; Animals ; Bolivia ; Chagas Disease/epidemiology ; Chagas Disease/prevention & control ; Ecosystem ; Humans ; Insect Control/methods ; Insect Vectors/physiology ; Models, Biological ; Sensitivity and Specificity ; Triatominae/physiology ; Trypanosoma cruzi/physiology
    Sprache Englisch
    Erscheinungsdatum 2013-07-22
    Erscheinungsland England
    Dokumenttyp Evaluation Studies ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 410635-0
    ISSN 0022-2585
    ISSN 0022-2585
    DOI 10.1603/me12096
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Verfügbar in ZB MED Köln/Königswinter

    Zs.A 303: Hefte anzeigen Standort:
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  4. Artikel: Household Model of Chagas Disease Vectors (Hemiptera: Reduviidae) Considering Domestic, Peridomestic, and Sylvatic Vector Populations

    Stevens, L. / Rizzo, D. M. / Lucero, D. E. / Pizarro, J. C.

    Journal of medical entomology

    Band v. 50,, Heft no. 4

    Abstract: Disease transmission is difficult to model because most vectors and hosts have different generation times. Chagas disease is such a situation, where insect vectors have 1–2 generations annually and mammalian hosts, including humans, can live for decades. ...

    Abstract Disease transmission is difficult to model because most vectors and hosts have different generation times. Chagas disease is such a situation, where insect vectors have 1–2 generations annually and mammalian hosts, including humans, can live for decades. The hemataphagous triatominae vectors (Hemiptera: Reduviidae) of the causative parasite Trypanosoma cruzi (Kinetoplastida: Trypanosomatidae) usually feed on sleeping hosts, making vector infestation of houses, peridomestic areas, and wild animal burrows a central factor in transmission. Because of difficulties with different generation times, we developed a model considering the dwelling as the unit of infection, changing the dynamics from an indirect to a direct transmission model. In some regions, vectors only infest houses; in others, they infest corrals; and in some regions, they also infest wild animal burrows. We examined the effect of sylvatic and peridomestic vector populations on household infestation rates. Both sylvatic and peridomestic vectors increase house infestation rates, sylvatic much more than peridomestic, as measured by the reproductive number R₀. The efficacy of manipulating parameters in the model to control vector populations was examined. When R₀ > 1, the number of infested houses increases. The presence of sylvatic vectors increases R₀ by at least an order of magnitude. When there are no sylvatic vectors, spraying rate is the most influential parameter. Spraying rate is relatively unimportant when there are sylvatic vectors; in this case, community size, especially the ratio of houses to sylvatic burrows, is most important. The application of this modeling approach to other parasites and enhancements of the model are discussed.
    Schlagwörter Chagas disease ; Trypanosoma cruzi ; parasites ; disease transmission ; humans ; Reduviidae ; Hemiptera ; insect vectors ; models ; spraying ; wild animals ; burrows ; hosts ; vector control ; population
    Sprache Englisch
    Dokumenttyp Artikel
    ISSN 0022-2585
    Datenquelle AGRIS - International Information System for the Agricultural Sciences and Technology

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