Publications
1.
Muchiri, Samuel K.; Muthee, Rose; Kiarie, Hellen; Sitienei, Joseph; Agweyu, Ambrose; Atkinson, Peter M.; Utazi, C. Edson; Tatem, Andrew J.; Alegana, Victor A.
Unmet need for COVID-19 vaccination coverage in Kenya Journal Article
In: Vaccine, vol. 40, no. 13, 2022, ISSN: 0264-410X.
Abstract | Links | BibTeX | Tags: Africa, covid-19, Kenya, travel time, vaccination
@article{nokey,
title = {Unmet need for COVID-19 vaccination coverage in Kenya},
author = {Samuel K. Muchiri and Rose Muthee and Hellen Kiarie and Joseph Sitienei and Ambrose Agweyu and Peter M. Atkinson and C. {Edson Utazi} and Andrew J. Tatem and Victor A. Alegana},
doi = {https://doi.org/10.1016/j.vaccine.2022.02.035},
issn = {0264-410X},
year = {2022},
date = {2022-02-14},
urldate = {2022-02-14},
journal = {Vaccine},
volume = {40},
number = {13},
abstract = {COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 – 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 – 16.74) – 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 – 36.96) – 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.},
keywords = {Africa, covid-19, Kenya, travel time, vaccination},
pubstate = {published},
tppubtype = {article}
}
COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 – 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 – 16.74) – 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 – 36.96) – 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.
2.
Hay, S. I.; Noor, A. M.; Nelson, A.; Tatem, A. J.
The accuracy of human population maps for public health application Journal Article
In: Tropical Medicine & International Health, vol. 10, no. 10, pp. 1073-1086, 2005.
Abstract | Links | BibTeX | Tags: areal weighting, census, dasymetric mapping, demography, Kenya, pycnophylactic interpolation, smart interpolation
@article{https://doi.org/10.1111/j.1365-3156.2005.01487.x,
title = {The accuracy of human population maps for public health application},
author = {S. I. Hay and A. M. Noor and A. Nelson and A. J. Tatem},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-3156.2005.01487.x},
doi = {https://doi.org/10.1111/j.1365-3156.2005.01487.x},
year = {2005},
date = {2005-01-01},
journal = {Tropical Medicine & International Health},
volume = {10},
number = {10},
pages = {1073-1086},
abstract = {Summary Objectives Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used. Methods The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved. Results We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best. Conclusions Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving per capita health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.},
keywords = {areal weighting, census, dasymetric mapping, demography, Kenya, pycnophylactic interpolation, smart interpolation},
pubstate = {published},
tppubtype = {article}
}
Summary Objectives Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used. Methods The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved. Results We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best. Conclusions Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving per capita health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.
3.
Tatem, Andrew J.; Noor, Abdisalan M.; Hay, Simon I.
Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery Journal Article
In: Remote Sensing of Environment, vol. 93, no. 1, pp. 42-52, 2004, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: JERS-1 SAR, Kenya, Landsat TM, Neural network, Population, Public health, Settlement mapping, Texture
@article{TATEM200442,
title = {Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery},
author = {Andrew J. Tatem and Abdisalan M. Noor and Simon I. Hay},
url = {https://www.sciencedirect.com/science/article/pii/S0034425704001944},
doi = {https://doi.org/10.1016/j.rse.2004.06.014},
issn = {0034-4257},
year = {2004},
date = {2004-01-01},
journal = {Remote Sensing of Environment},
volume = {93},
number = {1},
pages = {42-52},
abstract = {This paper presents an appraisal of satellite imagery types and texture measures for identifying and delineating settlements in four Districts of Kenya chosen to represent the variation in human ecology across the country. Landsat Thematic Mapper (TM) and Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) imagery of the four districts were obtained and supervised per-pixel classifications of image combinations tested for their efficacy at settlement delineation. Additional data layers including human population census data, land cover, and locations of medical facilities, villages, schools and market centres were used for training site identification and validation. For each district, the most accurate approach was determined through the best correspondence with known settlement and non-settlement pixels. The resulting settlement maps will be used in combination with census data to produce medium spatial resolution population maps for improved public health planning in Kenya.},
keywords = {JERS-1 SAR, Kenya, Landsat TM, Neural network, Population, Public health, Settlement mapping, Texture},
pubstate = {published},
tppubtype = {article}
}
This paper presents an appraisal of satellite imagery types and texture measures for identifying and delineating settlements in four Districts of Kenya chosen to represent the variation in human ecology across the country. Landsat Thematic Mapper (TM) and Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) imagery of the four districts were obtained and supervised per-pixel classifications of image combinations tested for their efficacy at settlement delineation. Additional data layers including human population census data, land cover, and locations of medical facilities, villages, schools and market centres were used for training site identification and validation. For each district, the most accurate approach was determined through the best correspondence with known settlement and non-settlement pixels. The resulting settlement maps will be used in combination with census data to produce medium spatial resolution population maps for improved public health planning in Kenya.
