Idah Orowe
Biographic Data
| ID | 8337488 |
|---|---|
| NAME | Idah Orowe |
| GIVEN NAMES | Idah |
| FAMILY NAME | Orowe |
| SIGNATURE | OROWE I |
| AFFILIATIONS | University of Nairobi |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
A collaborative approach to advancing research and training in Public Health Data Science—challenges, opportunities, and lessons learnt
The unprecedented availability of increasingly complex, voluminous, and multi-dimensional data as well as the emergence of data science as an evolving field provide ideal opportunities to address the multi-faceted public health challenges faced by low and middle income countries (LMIC), especially those in sub-Saharan Africa. However, there is a severe lack of well-trained data scientists and home-grown educational programs to enable context-spec…
Sensitivity Analysis of a Transmission Interruption Model for the Soil-Transmitted Helminth Infections in Kenya
As the world rallies toward the endgame of soil-transmitted helminths (STH) elimination by the year 2030, there is a need for efficient and robust mathematical models that would enable STH programme managers to target the scarce resources and interventions, increase treatment coverage among specific sub-groups of the population, and develop reliable surveillance systems that meet sensitivity and specificity requirements for the endgame of STH eli…
No prominent works on this page.
Sensitivity Analysis of a Transmission Interruption Model for the Soil-Transmitted Helminth Infections in Kenya
As the world rallies toward the endgame of soil-transmitted helminths (STH) elimination by the year 2030, there is a need for efficient and robust mathematical models that would enable STH programme managers to target the scarce resources and interventions, increase treatment coverage among specific sub-groups of the population, and develop reliable surveillance systems that meet sensitivity and specificity requirements for the endgame of STH eli…
A collaborative approach to advancing research and training in Public Health Data Science—challenges, opportunities, and lessons learnt
The unprecedented availability of increasingly complex, voluminous, and multi-dimensional data as well as the emergence of data science as an evolving field provide ideal opportunities to address the multi-faceted public health challenges faced by low and middle income countries (LMIC), especially those in sub-Saharan Africa. However, there is a severe lack of well-trained data scientists and home-grown educational programs to enable context-spec…
Computer Science (2 works) · Medicine (2 works) · Capacity building (1 works) · Conceptualization (1 works) · Econometrics (1 works) · Engineering (1 works) · Environmental health (1 works) · Ethics in Clinical Research (1 works) · General partnership (1 works) · Genetics, Bioinformatics, and Biomedical Research (1 works)