Wisdom M Dlamini
Biographic Data
| ID | 6476390 |
|---|---|
| NAME | Wisdom M Dlamini |
| GIVEN NAMES | Wisdom M |
| FAMILY NAME | Dlamini |
| SIGNATURE | DLAMINI W M |
| AFFILIATIONS | University of Eswatini |
| ORCID | 0000-0002-0397-4404 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Spatial assessment and monitoring of household electricity access and use using nighttime lights and ancillary spatial data
This study develops models of electricity access and use in Eswatini (formerly Swaziland) using census data on household use of electricity for cooking and lighting. Nighttime light (NTL) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band sensor is combined with ancillary spatial datasets though random forest models to predict electricity access and use. The findings reveal geographic disparities in electricity access.…
Predicting Covid-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method
COVID-19 country spikes have been reported at varying temporal scales as a result of differences in the disease-driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between socio-economic, weather, demographic and health variables with the reported cases of COVID-19 in Eswatini using the maximum likelihood estimation method for count data. A generalized Poisson…
Spatial risk assessment of an emerging pandemic under data scarcity
Application of Bayesian networks for fire risk mapping using GIS and remote sensing data
No prominent works on this page.
Application of Bayesian networks for fire risk mapping using GIS and remote sensing data
Spatial risk assessment of an emerging pandemic under data scarcity
Spatial assessment and monitoring of household electricity access and use using nighttime lights and ancillary spatial data
This study develops models of electricity access and use in Eswatini (formerly Swaziland) using census data on household use of electricity for cooking and lighting. Nighttime light (NTL) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band sensor is combined with ancillary spatial datasets though random forest models to predict electricity access and use. The findings reveal geographic disparities in electricity access.…
Predicting Covid-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method
COVID-19 country spikes have been reported at varying temporal scales as a result of differences in the disease-driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between socio-economic, weather, demographic and health variables with the reported cases of COVID-19 in Eswatini using the maximum likelihood estimation method for count data. A generalized Poisson…
Geography (3 works) · Computer Science (2 works) · COVID-19 epidemiological studies (2 works) · COVID-19 Pandemic Impacts (2 works) · Disease (2 works) · Economics (2 works) · Engineering (2 works) · Environmental health (2 works) · Environmental Science (2 works) · Medicine (2 works)