%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 }