Evidence map›Paper›PMID 41973751›Full record

ArticlePloS one2026

Spatial prediction of dog population distribution in Kenya.

Moumita Das, Maria Sol Perez Aguirreburualde, Shepelo Getrude Peter, Anima Sirma, Andres Perez

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Moumita DasCenter for Animal Health and Food Safety, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America.ORCID https://orcid.org/0000-0001-5848-9054
Maria Sol Perez AguirreburualdeCenter for Animal Health and Food Safety, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America.
Shepelo Getrude PeterKenya Women Veterinary Association and Department of Clinical Studies, Faculty of Veterinary Medicine, University of Nairobi, Nairobi, Kenya.
Anima SirmaDirectorate of Veterinary Services, Kenya Ministry of Agriculture and Livestock Development, Nairobi, Kenya.
Andres PerezCenter for Animal Health and Food Safety, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Free-roaming dogs pose a significant public health concern due to their role in disease transmission. Rabies has been endemic in Kenya for over a century, yet a sustainable and standardized method for estimating dog populations remains unestablished. To address this gap, we applied kriging and co-kriging spatial interpolation techniques to predict the distribution of free-roaming domestic dogs across different Kenyan counties. To improve the model's accuracy, we incorporated environmental and demographic predictors such as daily temperature, the Normalized Difference Vegetation Index (NDVI), and human density. Dog population data at the village level were collected from 34 counties through an online survey of veterinary professionals in both the public and private sectors. A spherical model was used to construct the semivariogram, integrating temperature, NDVI, and human density to refine spatial predictions. Kenya's total free-roaming dog population was estimated to be 7.46 million. The density of dogs per square kilometer varied across counties, corresponding to a median national density of 12.13 dogs per square kilometer. Compared to models incorporating no or only single or multiple covariates, the co-kriging model incorporating human density provided the best fit, with the minimum estimated difference between observed and predicted values. The spatial distribution map highlights arid and sparsely populated pastoral counties having lower dog densities, whereas peri-urban, densely populated, and agricultural counties exhibit higher dog numbers. This study provides a spatial framework for estimating free-roaming dog populations, which can inform the design and implementation of rabies control programs and public health interventions in Kenya and other infected countries.

Indexed as

RabiesAnimalsDogsHumansKenyaPopulation Density

Identifiers

PMID41973751
PMCPMC13075693

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.