Modeling the relative role of human mobility, land-use and climate factors on dengue outbreak emergence in Sri Lanka

Abstract

Background More than 80,000 dengue cases including 215 deaths were reported nationally in less than 7 months between 2016 and 2017, a fourfold increase in the number of reported cases compared to the average number over 2010–2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission. Methods We present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016–2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km × 1 km spatial resolution and a weekly temporal resolution. Results Our results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions. Conclusions Our study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.

Effect of social isolation in dengue cases in the state of Sao Paulo, Brazil: An analysis during the COVID-19 pandemic

“…Such variables allowed to take into account seasonality and the presence of autocorrelation frequently observed in daily data, providing more robust estimates. Several recent studies have shown the contribution of human mobility in the location and spatio-temporal evolution of the occurrence of dengue cases [ 2 , 13 , [31] , [32] , [33] ], either locally through long distance travel or migration. However, in general, the implications of spreading diseases are complex because contact between individuals is structured according to human activities, rather than assuming a random character.…”

Impacts of El Niño Southern Oscillation on the dengue transmission dynamics in the Metropolitan Region of Recife, Brazil

“…Similar results have been reported in Southern Taiwan, where the epidemic has spread from large cities to smaller cities 39,53 . Associated with atmospheric conditions, adjacent processes such as the spatial hierarchy of population structure 54 and human mobility [55][56][57][58] could play an important role in the spread of the epidemic. When this synchrony is regionally strong, it could have important consequences: a health system collapse 58 and an increase in disability-adjusted lost life Years-DALY 59 .…”

Spatio-temporal distribution pattern of COVID-19 in the Northern Italy during the first-wave scenario: The role of the highway network

“…However, the proposed model can be applied to any other study areas to identify the relationship between the high-speed road network and the disease transmission in different contexts and time periods ( Cheshmehzangi et al, 2021 ). Indeed, in the pre-COVID-19 the literature demonstrated different transport modes ( Findlater and Bogoch, 2018 ) as a direct factor affecting infectious diseases transmission ( Zhang et al, 2020a , Zhang et al, 2020b , Peak et al, 2018 , Arthur et al, 2017 , López-Quílez, 2019 ). The recent COVID-19 literature focused on built environment factors ( Ma et al, 2021 ), urban attributes ( Barak et al, 2021 ), and living conditions ( Sahasranaman and Jensen, 2021 ) to investigate the income on cases growth.…”

“…Some studies proposed temporal and spatial analysis of COVID-19 spreading focusing on air flows and dispersion of aerosolized virus in polluted areas ( Zheng et al, 2021 ), meteorological variables ( Wang et al, 2021 ), traffic volumes ( Mu et al, 2020 ). Network analyses have been carried out on cross-country pandemic air connectedness ( Chu et al, 2021 ), maritime transport ( Wang et al, 2022 ), high-speed railway ( Zhang et al, 2020a , Zhang et al, 2020b ): the attention was paid on mass public transport. Conversely, in this study the authors investigated how the highway transportation network in the Northern Italy exacerbated the large-scale spreading of COVID-19 during the first wave.…”