Smart pooling: AI-powered COVID-19 informative group testing.

Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.

Is the built-environment at origin, on route, and at destination associated with bicycle commuting? A gender-informed approach.

There is limited evidence on the gender differences and location-specific built-environment factors associated with bicycling in Latin American cities. This study aimed to assess commuting in Bogotá by (1) analyzing the gender-specific trend of the standardized number of bicycle commuters during 2005-2017; and (2) assessing the socio-demographic, community, built-environment and natural factors associated with bicycle commuting stratified by gender. This secondary-data analysis included data from the Household Travel Surveys and Multipurpose Surveys to calculate the number of bicycle commuters per habitant from 2005 to 2017 by gender. We assessed the socio-demographic and built-environment factors fitting generalized additive models stratified by gender using the 2015 Household Travel Survey. Although both women and men increased the standardized number of bicycle commuters, male commuters show a steeper trend than women, evidencing the widening gender gap in bicycle commuting over time. Bicycle commuting was negatively associated with household motor vehicle ownership, steeper terrain slope, longer commute distance, and scarce low-stress roads at trip origin and route. Among women, the availability of bike paths at the trip destination was positively associated with bicycling, while age and being a student were negatively associated with bicycling. Among men, living in areas with the lowest socio-economic status was positively associated with bicycling, while having a driver's license and living close to bus rapid transit stations were negatively associated with bicycling. In conclusion, bicycle and transport infrastructure play different roles in commuting by bicycle by gender and trip stages (origin - route - destination).

Level of traffic stress-based classification: A clustering approach for Bogotá, Colombia.

The Level of Traffic Stress (LTS) is an indicator that quantifies the stress experienced by a cyclist on the segments of a road network. We propose an LTS-based classification with two components: a clustering component and an interpretative component. Our methodology is comprised of four steps: (i) compilation of a set of variables for road segments, (ii) generation of clusters of segments within a subset of the road network, (iii) classification of all segments of the road network into these clusters using a predictive model, and (iv) assignment of an LTS category to each cluster. At the core of the methodology, we couple a classifier (unsupervised clustering algorithm) with a predictive model (multinomial logistic regression) to make our approach scalable to massive data sets. Our methodology is a useful tool for policy-making, as it identifies suitable areas for interventions; and can estimate their impact on the LTS classification, according to probable changes to the input variables (e.g., traffic density). We applied our methodology on the road network of Bogotá, Colombia, a city with a history of implementing innovative policies to promote biking. To classify road segments, we combined government data with open-access repositories using geographic information systems (GIS). Comparing our LTS classification with city reports, we found that the number of bicyclists' fatal and non-fatal collisions per kilometer is positively correlated with higher LTS. Finally, to support policy making, we developed a web-enabled dashboard to visualize and analyze the LTS classification and its underlying variables.

Bicycle safety in Bogotá: A seven-year analysis of bicyclists' collisions and fatalities.

Road safety research in low- and middle-income countries is limited, even though ninety percent of global road traffic fatalities are concentrated in these locations. In Colombia, road traffic injuries are the second leading source of mortality by external causes and constitute a significant public health concern in the city of Bogotá. Bogotá is among the top 10 most bike-friendly cities in the world. However, bicyclists are one of the most vulnerable road-users in the city. Therefore, assessing the pattern of mortality and understanding the variables affecting the outcome of bicyclists' collisions in Bogotá is crucial to guide policies aimed at improving safety conditions. This study aims to determine the spatiotemporal trends in fatal and nonfatal collision rates and to identify the individual and contextual factors associated with fatal outcomes. We use confidence intervals, geo-statistics, and generalized additive mixed models (GAMM) corrected for spatial correlation. The collisions' records were taken from Bogotá's Secretariat of Mobility, complemented with records provided by non-governmental organizations (NGO). Our findings indicate that from 2011 to 2017, the fatal bicycling collision rates per bicyclists' population have remained constant for females while decreasing 53 % for males. Additionally, we identified high-risk areas located in the west, southwest, and southeast of the city, where the rate of occurrence of fatal events is higher than what occurs in other parts of the city. Finally, our results show associated risk factors that differ by sex. Overall, we find that fatal collisions are positively associated with factors including collisions with large vehicles, the absence of dedicated infrastructure, steep terrain, and nighttime occurrence. Our findings support policy-making and planning efforts to monitor, prioritize, and implement targeted interventions aimed at improving bicycling safety conditions while accounting for gender differences.

Urban Transformations and Health: Methods for TrUST-a Natural Experiment Evaluating the Impacts of a Mass Transit Cable Car in Bogotá, Colombia.

Background: Cable cars provide urban mobility benefits for vulnerable populations. However, no evaluation has assessed cable cars' impact from a health perspective. TransMiCable in Bogotá, Colombia, provides a unique opportunity to (1) assess the effects of its implementation on the environmental and social determinants of health (microenvironment pollution, transport accessibility, physical environment, employment, social capital, and leisure time), physical activity, and health outcomes (health-related quality of life, respiratory diseases, and homicides); and (2) use citizen science methods to identify, prioritize, and communicate the most salient negative and positive features impacting health and quality of life in TransMiCable's area, as well as facilitate a consensus and advocacy-building change process among community members, policymakers, and academic researchers. Methods: TrUST (In Spanish: Transformaciones Urbanas y Salud: el caso de TransMiCable en Bogotá) is a quasi-experimental study using a mixed-methods approach. The intervention group includes adults from Ciudad Bolívar, the area of influence of TransMiCable. The control group includes adults from San Cristóbal, an area of future expansion for TransMiCable. A conceptual framework was developed through group-model building. Outcomes related to environmental and social determinants of health as well as health outcomes are assessed using questionnaires (health outcomes, physical activity, and perceptions), secondary data (crime and respiratory outcomes) use of portable devices (air pollution exposure and accelerometry), mobility tracking apps (for transport trajectories), and direct observation (parks). The Stanford Healthy Neighborhood Discovery Tool is being used to capture residents' perceptions of their physical and social environments as part of the citizen science component of the investigation. Discussion: TrUST is innovative in its use of a mixed-methods, and interdisciplinary research approach, and in its systematic engagement of citizens and policymakers throughout the design and evaluation process. This study will help to understand better how to maximize health benefits and minimize unintended negative consequences of TransMiCable.

On the preventive management of sediment-related sewer blockages: a combined maintenance and routing optimization approach.

In this work we tackle the problem of planning and scheduling preventive maintenance (PM) of sediment-related sewer blockages in a set of geographically distributed sites that are subject to non-deterministic failures. To solve the problem, we extend a combined maintenance and routing (CMR) optimization approach which is a procedure based on two components: (a) first a maintenance model is used to determine the optimal time to perform PM operations for each site and second (b) a mixed integer program-based split procedure is proposed to route a set of crews (e.g., sewer cleaners, vehicles equipped with winches or rods and dump trucks) in order to perform PM operations at a near-optimal minimum expected cost. We applied the proposed CMR optimization approach to two (out of five) operative zones in the city of Bogotá (Colombia), where more than 100 maintenance operations per zone must be scheduled on a weekly basis. Comparing the CMR against the current maintenance plan, we obtained more than 50% of cost savings in 90% of the sites.