Using fine-scale satellite imagery and GIS data to help predict disease spread
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1
One Health Institute, School of Veterinary Medicine, University of California, Davis, United States
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2
University of California, Davis, United States
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3
Department of Statistics, University of California, Davis, United States
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4
Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, United States
Background and objectives: Emerging infectious disease outbreaks present a major public health challenge and require innovative tools for modeling their spread and impact. To address these challenges, we developed a geospatial model leveraging fine-scale satellite imagery, GIS datasets, and population mobility models to simulate disease spread via road transportation
Methods: High-resolution satellite data on urban areas (~12m) and population data were used to build a geospatial network connected by roads. Mobility patterns across the resultant network were calculated with the radiation model, which used the population attributes of urban areas and the road distances between them to determine commuting rates between the road-connected urban areas. We demonstrated the above process for Rwanda and simulate the spread of 2009 pandemic Influenza A H1N1 over the created network. The results of our simulations were compared with the observed spread of the disease in 2009-10. We also determined the effects of vaccination campaigns on outbreak spread and impact across the network.
Results: Our results were comparable to data collected during the actual outbreak of pandemic influenza in Rwanda with respect to outbreak length and order of urban areas infected. The probability of outbreak occurrence reduced when areas of infection origin were vaccinated, especially with increasing vaccination coverages and efficacies. At the same time, outbreak impact, defined by the number of individuals infected with influenza, was lower when larger urban areas were vaccinated.
Conclusions: Our modeling approach, taking into account human mobility between urban settlements across road networks, can be valuable for future planning and control purposes in real-time disease situations. The model highlights the effectiveness of controlling outbreaks by targeting mitigation efforts at their points of origin, a process that can be made possible by increased surveillance and more rapid outbreak response times. This modeling approach can readily be applied to other infectious diseases as well, such as Ebola in West Africa.
Keywords:
Satellite Imagery,
outbreak spread,
Roads,
networks,
Fine-scale,
mobility
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Student senior oral presentation
Topic:
Spatio-temporal surveillance and modeling approaches
Citation:
Randhawa
N,
Mailhot
H,
Lang
DT,
Martínez-López
B,
Gilardi
K and
Mazet
JA
(2019). Using fine-scale satellite imagery and GIS data to help predict disease spread.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00042
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Received:
09 Jun 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Dr. Nistara Randhawa, One Health Institute, School of Veterinary Medicine, University of California, Davis, Davis, California, 95616, United States, nrandhawa@ucdavis.edu
Dr. Beatriz Martínez-López, Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, Davis, United States, beamartinezlopez@ucdavis.edu