The Potential to Address Disease Vectors in Favelas in Brazil Using Sustainable Drainage Systems: Zika, Drainage and Greywater Management

Abstract

Residents of informal settlements, the world over, suffer consequences due to the lack of drainage and greywater management, impacting human and environmental health. In Brazil, the presence of the Aedes aegypti mosquito in urban areas promotes infections of the Zika virus as well as companion viruses, such as dengue, chikungunya and yellow fever. By using observation and interviews with the community, this paper shows how a simple sustainable drainage system approach could prevent the accumulation of on-street standing water, and thus reduce opportunities for the mosquito to breed and reduce infection rates. During the interview phase, it became apparent that underlying misinformation and misunderstandings prevail related to existing environmental conditions in favelas and the role of the mosquito in infecting residents. This inhibits recommendations made by professionals to reduce breeding opportunities for the disease vector. Whilst unrest is an issue in favelas, it is not the only issue preventing the human right to reliable, safe sanitation, including drainage. In “pacified” favelas which may be considered safe(r), the infrastructure is still poor and is not connected to the city-wide sanitation/treatment networks.

Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil

“…The Federal District of Brazil is the smallest federative unit without municipalities, and intense urban land expansion and population growth make dengue a major public health issue in recent years [ 40 , 41 , 42 ]. Moreover, Fortaleza is the fifth most densely populated city in Brazil and exhibits great socioeconomic inequality where intense dengue epidemics occurred in recent decades due to population concentration and urban inequality [ 25 , 41 , 43 , 44 ]. The experiments of city-level forecasting of weekly dengue cases were implemented as follows: (1) generating the time series of weekly dengue cases; (2) delineating the dengue transmission areas and generating the time series of risk predictors based on the GEE platform; (3) constructing, training and evaluating multi-date ahead and multi-scenario models.…”

Democratizing Public Health: Participatory Policymaking Institutions, Mosquito Control, and Zika in the Americas

“…The emergence and rapid spread of the Zika virus in the Americas represented a complex public health challenge at the time and a contemporary opportunity to learn about combating other pandemics through hindsight. From 2015 to 2018, over one million people tested positive for Zika infection in the Americas, with many more infections likely un-reported; Zika cases are often asymptomatic, diagnosis is difficult and easily confused with other diseases with similar symptoms, and access to testing is limited across the tropical regions of the Americas, where Zika is most prevalent [ 1 , 2 , 3 ]. Zika’s spread and its potential impact on newborns sparked a public health crisis and brief panic.…”

“…Response capacity to the Zika pandemic varied considerably across countries. Lower-income countries and lower-income communities within those countries were disproportionately affected by the virus due to lack of resources for mosquito abatement, for prophylactic measures, such as screens or air conditioning, and for education [ 3 , 36 , 37 ].…”

“…Zika is a confounding virus from a conceptual standpoint as well: the virus is both transmitted by mosquitos and sexually transmitted. Like Dengue or Chikungunya, outbreaks are much likelier in dense, poor urban areas [ 3 , 36 , 37 , 38 ]. However, four out of five Zika cases may be asymptomatic, and the disease is rarely fatal for adults, which makes the disease less visible in literal and figurative senses for public health [ 39 , 40 , 41 ].…”

Fire Risk Assessments of Informal Settlements Based on Fire Risk Index and Bayesian Network

“…When it comes to fire risk, David Rush et al have examined fire risks in IS in New Delhi, Cape Town and Lebanon, based on literature, statistical data and qualitative interviews [ 9 ]; Natalia Flores Quiroz et al have obtained fire risk perception of the IS complex inhabitants with surveys to comprehend why the fire started [ 10 ]; Richard et al have qualitatively presented an appraisal of various interventions and strategies to improve fire safety in IS in South Africa [ 11 ]; Isabela et al have assessed the fire exposure and risks in IS in Tanzania, mainly based on interviews [ 12 ]; Morrissey and Taylor have analyzed the factors influencing the fire risk in the IS in Cape Town qualitatively [ 13 ]. In addition to fires, considering other disasters, such as flooding or earthquake, some studies have also analyzed the comprehensive risks in IS [ 14 , 15 , 16 , 17 ].…”