Dengue is a vector-borne disease transmitted by the Aedes genus
mosquito. It causes financial burdens on public health systems and
considerable morbidity and mortality. Tropical regions in the Americas
and Asia are the areas most affected by the virus. Fortaleza is a city
with approximately 2.6 million inhabitants in northeastern Brazil that,
during the recent decades, has been suffering from endemic dengue
transmission, interspersed with larger epidemics. The objective of this
paper is to study the impact of human mobility in urban areas on the
spread of the dengue virus, and to test whether human mobility data can
be used to improve predictions of dengue virus transmission at the
neighbourhood level. We present two distinct forecasting systems for
dengue transmission in Fortaleza: the first using artificial neural
network methods and the second developed using a mechanistic model of
disease transmission. We then present enhanced versions of the two
forecasting systems that incorporate bus transportation data cataloguing
movement among 119 neighbourhoods in Fortaleza. Each forecasting system
was used to perform retrospective forecasts for historical dengue
outbreaks from 2007 to 2015. Results show that both artificial neural
networks and mechanistic models can accurately forecast dengue cases,
and that the inclusion of human mobility data substantially improves the
performance of both forecasting systems. While the mechanistic models
perform better in capturing seasons with large-scale outbreaks, the
neural networks more accurately forecast outbreak peak timing, peak
intensity and annual dengue time series. These results have two
practical implications: they support the creation of public policies
from the use of the models created here to combat the disease and help
to understand the impact of urban mobility on the epidemic in large
cities.
%0 Journal Article
%1 WOS:000586513400001
%A Bomfim, Rafael
%A Pei, Sen
%A Shaman, Jeffrey
%A Yamana, Teresa
%A Makse, Hernan A
%A Jr., Jose S Andrade
%A Neto, Antonio Lima S
%A Furtado, Vasco
%C 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND
%D 2020
%I ROYAL SOC
%J JOURNAL OF THE ROYAL SOCIETY INTERFACE
%K forecasting; human mobility} network; neural {dengue;
%N 171
%R 10.1098/rsif.2020.0691
%T Predicting dengue outbreaks at neighbourhood level using human mobility
in urban areas
%V 17
%X Dengue is a vector-borne disease transmitted by the Aedes genus
mosquito. It causes financial burdens on public health systems and
considerable morbidity and mortality. Tropical regions in the Americas
and Asia are the areas most affected by the virus. Fortaleza is a city
with approximately 2.6 million inhabitants in northeastern Brazil that,
during the recent decades, has been suffering from endemic dengue
transmission, interspersed with larger epidemics. The objective of this
paper is to study the impact of human mobility in urban areas on the
spread of the dengue virus, and to test whether human mobility data can
be used to improve predictions of dengue virus transmission at the
neighbourhood level. We present two distinct forecasting systems for
dengue transmission in Fortaleza: the first using artificial neural
network methods and the second developed using a mechanistic model of
disease transmission. We then present enhanced versions of the two
forecasting systems that incorporate bus transportation data cataloguing
movement among 119 neighbourhoods in Fortaleza. Each forecasting system
was used to perform retrospective forecasts for historical dengue
outbreaks from 2007 to 2015. Results show that both artificial neural
networks and mechanistic models can accurately forecast dengue cases,
and that the inclusion of human mobility data substantially improves the
performance of both forecasting systems. While the mechanistic models
perform better in capturing seasons with large-scale outbreaks, the
neural networks more accurately forecast outbreak peak timing, peak
intensity and annual dengue time series. These results have two
practical implications: they support the creation of public policies
from the use of the models created here to combat the disease and help
to understand the impact of urban mobility on the epidemic in large
cities.
@article{WOS:000586513400001,
abstract = {Dengue is a vector-borne disease transmitted by the Aedes genus
mosquito. It causes financial burdens on public health systems and
considerable morbidity and mortality. Tropical regions in the Americas
and Asia are the areas most affected by the virus. Fortaleza is a city
with approximately 2.6 million inhabitants in northeastern Brazil that,
during the recent decades, has been suffering from endemic dengue
transmission, interspersed with larger epidemics. The objective of this
paper is to study the impact of human mobility in urban areas on the
spread of the dengue virus, and to test whether human mobility data can
be used to improve predictions of dengue virus transmission at the
neighbourhood level. We present two distinct forecasting systems for
dengue transmission in Fortaleza: the first using artificial neural
network methods and the second developed using a mechanistic model of
disease transmission. We then present enhanced versions of the two
forecasting systems that incorporate bus transportation data cataloguing
movement among 119 neighbourhoods in Fortaleza. Each forecasting system
was used to perform retrospective forecasts for historical dengue
outbreaks from 2007 to 2015. Results show that both artificial neural
networks and mechanistic models can accurately forecast dengue cases,
and that the inclusion of human mobility data substantially improves the
performance of both forecasting systems. While the mechanistic models
perform better in capturing seasons with large-scale outbreaks, the
neural networks more accurately forecast outbreak peak timing, peak
intensity and annual dengue time series. These results have two
practical implications: they support the creation of public policies
from the use of the models created here to combat the disease and help
to understand the impact of urban mobility on the epidemic in large
cities.},
added-at = {2022-05-23T20:00:14.000+0200},
address = {6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND},
author = {Bomfim, Rafael and Pei, Sen and Shaman, Jeffrey and Yamana, Teresa and Makse, Hernan A and Jr., Jose S Andrade and Neto, Antonio Lima S and Furtado, Vasco},
biburl = {https://www.bibsonomy.org/bibtex/25432ca4917bdbc20d5580f900d6204cf/ppgfis_ufc_br},
doi = {10.1098/rsif.2020.0691},
interhash = {6f0b79582661217cfd4d3c5a53cfc8d4},
intrahash = {5432ca4917bdbc20d5580f900d6204cf},
issn = {1742-5689},
journal = {JOURNAL OF THE ROYAL SOCIETY INTERFACE},
keywords = {forecasting; human mobility} network; neural {dengue;},
number = 171,
publisher = {ROYAL SOC},
pubstate = {published},
timestamp = {2022-05-23T20:00:14.000+0200},
title = {Predicting dengue outbreaks at neighbourhood level using human mobility
in urban areas},
tppubtype = {article},
volume = 17,
year = 2020
}