Dataset for detecting motorcyclists in pedestrian areas.

This paper presents a semi-automated, scalable, and homologous methodology towards IoT implemented in Python for extracting and integrating images in pedestrian and motorcyclist areas on the road for constructing a multiclass object classifier. It consists of two stages. The first stage deals with creating a non-debugged data set by acquiring images related to the semantic context previously mentioned, using an embedded device connected 24/7 via Wi-Fi to a free and public CCTV service in Medellin, Colombia. Through artificial vision techniques, and automatically performs a comparative chronological analysis to download the images observed by 80 cameras that report data asynchronously. The second stage proposes two algorithms focused on debugging the previously obtained data set. The first one facilitates the user in labeling the data set not debugged through Regions of Interest (ROI) and hotkeys. It decomposes the information in the nth image of the data set in the same dictionary to store it in a binary Pickle file. The second one is nothing more than an observer of the classification performed by the user through the first algorithm to allow the user to verify if the information contained in the Pickle file built is correct.

Cost-Effectiveness Analysis of Strategies of COVID-19 Vaccination in Colombia: Comparison of High-Risk Prioritization and No Prioritization Strategies With the Absence of a Vaccination Plan.

OBJECTIVES: Our study compares two national COVID-19 vaccination plan strategies-high-risk prioritization and no prioritization-and estimates their cost-effectiveness compared with no vaccination, to generate possible recommendations for future vaccination plans. METHODS: We developed a Markov discrete-time, compartmental, deterministic model stratified by Colombian departments, healthcare workers, comorbidities, and age groups and calibrated to seroprevalence, cases, and deaths. The model simulates three scenarios: no vaccination, no prioritization of vaccination, and prioritization of high-risk population. The study presents the perspective of the health system of Colombia, including the direct health costs financed by the government and the direct health outcomes related to the infection. We measured symptomatic cases, deaths, and costs for each of the three scenarios from the start of the vaccination rollout to February 20, 2023. RESULTS: Both for the base-case and across multiple sensitivity analyses, the high-risk prioritization proves to be the most cost-effective of the considered strategies. An increment of US$255 million results in an incremental cost-effectiveness ratio of US$3339 per disability-adjusted life-year avoided. The simulations show that prioritization of high-risk population reduces symptomatic cases by 3.4% and deaths by 20.1% compared with no vaccination. The no-prioritization strategy is still cost-effective, with an incremental cost-effectiveness ratio of US$5223.66, but the sensitivity analysis the show potential risks of losing cost-effectiveness under the cost-effectiveness threshold (one gross domestic product per averted disability-adjusted life-year). CONCLUSIONS: The high-risk prioritization strategy is consistently more cost-effective than the no-prioritization strategy across multiple scenarios. High-risk prioritization is the recommended strategy in low-resource settings to reduce the burden of disease.