RESUMO
An improved understanding of global Urban Exposure to Flooding (UEF) is essential for developing risk-reduction strategies for sustainable urban development. This study is the first to assess the long-term historical global UEF at a fine spatial resolution (i.e., 30 m) and annual temporal frequency, with consideration of smaller urban areas in the exposure assessment compared to those using coarse resolution data. We assessed the UEF by investigating the spatially explicit urban expansion in the 100-year floodplain extents. The global UEF increased more than four-fold from 16,443 km2 in 1985 to 92,233 km2 in 2018 with accelerated temporal trends. The most notable growth in UEF occurred in Asia (74.1%), followed by Europe (11.6%), Northern America (8.7%), Africa (2.9%), Southern America (2.2%), and Australia (0.5%). Notably, China and US were the two countries with the largest UEF, accounting for about 61.5% of global growth in UEF. In addition, only 1.2% of global floodplains were occupied by urban expansion by 2018, whereas this percentage reached 20% in the basins of Western Europe, Eastern Asia, and Northeastern US. Moreover, although the floodplains only accounted for 5.5% of the global land areas, 12.6% of the urban expansion occurred in the floodplains from 1985 to 2018, with the most rapid increases in the basins in Southeastern and Eastern China. Our findings highlight that the trends of accelerated increasing urban exposure to flooding have been occurring for at least the past three decades.
Assuntos
Inundações , Ásia , China , Europa (Continente) , América do SulRESUMO
BACKGROUND: A common challenge to the study of several infectious diseases consists in combining limited cross-sectional survey data, collected with a more sensitive detection method, with a more extensive (but biased) syndromic sentinel surveillance data, collected with a less sensitive method. Our article describes a novel modeling framework that overcomes this challenge, resulting in enhanced understanding of malaria in the Western Brazilian Amazon. METHODOLOGY/PRINCIPAL FINDINGS: A cohort of 486 individuals was monitored using four cross-sectional surveys, where all participants were sampled regardless of symptoms (aggressive-active case detection), resulting in 1,383 microscopy and 1,400 polymerase chain reaction tests. Data on the same individuals were also obtained from the local surveillance facility (i.e., passive and active case detection), totaling 1,694 microscopy tests. Our model accommodates these multiple pathogen and case detection methods. This model is shown to outperform logistic regression in terms of interpretability of its parameters, ability to recover the true parameter values, and predictive performance. We reveal that the main infection determinant was the extent of forest, particularly during the rainy season and in close proximity to water bodies, and participation on forest activities. We find that time residing in Acrelandia (as a proxy for past malaria exposure) decreases infection risk but surprisingly increases the likelihood of reporting symptoms once infected, possibly because non-naïve settlers are only susceptible to more virulent Plasmodium strains. We suggest that the search for asymptomatic carriers should focus on those at greater risk of being infected but lower risk of reporting symptoms once infected. CONCLUSIONS/SIGNIFICANCE: The modeling framework presented here combines cross-sectional survey data and syndromic sentinel surveillance data to shed light on several aspects of malaria that are critical for public health policy. This framework can be adapted to enhance inference on infectious diseases whenever asymptomatic carriers are important and multiple datasets are available.