Výsledky vyhledávání - "Mbolatiana Raza-Fanomezanjanahary"

Akademický článek

Applying a zero-corrected, gravity model estimator reduces bias due to heterogeneity in healthcare utilization in community-scale, passive surveillance datasets of endemic diseases

Autor: Michelle V. Evans, Felana A. Ihantamalala, Mauricianot Randriamihaja, Andritiana Tsirinomen’ny Aina, Matthew H. Bonds, Karen E. Finnegan, Rado J. L. Rakotonanahary, Mbolatiana Raza-Fanomezanjanahary, Bénédicte Razafinjato, Oméga Raobela, Sahondraritera Herimamy Raholiarimanana, Tiana Harimisa Randrianavalona, Andres Garchitorena

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)

Abstract Data on population health are vital to evidence-based decision making but are rarely adequately localized or updated in continuous time. They also suffer from low ascertainment rates, particularly in rural areas where barriers to healthcare

Externí odkaz: https://doaj.org/article/c5373dd293494722a4b02758bda3030b

Zobrazit plný text záznamu

Plný text ve formátu HTML

The Zero-Corrected, Gravity-Model Multiplier (ZERO-G): A novel method to estimate disease dynamics at the community-scale from passive surveillance data

Autor: Michelle V Evans, Felana A Ihantamalala, Mauricianot Randriamihaja, Andritiana Tsirinomen’ny Aina, Matthew H Bonds, Karen E Finnegan, Rado JL Rakotonanahary, Mbolatiana Raza-Fanomezanjanahary, Bénédicte Razafinjato, Oméga Raobela, Sahondraritera Herimamy Raholiarimanana, Tiana Harimisa Randrianavalona, Andres Garchitorena

Data on population health are vital to evidence-based decision making by public health officials, but are rarely adequately localized, particularly in rural areas where barriers to healthcare can result in extremely low ascertainment of cases by the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f8569f4f66e9266742423fe08801bf66
https://doi.org/10.1101/2023.03.13.23287196

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání