The adoption of non-pharmaceutical interventions and the role of digital infrastructure during the COVID-19 Pandemic in Colombia, Ecuador, and El Salvador

• URL: http://arxiv.org/abs/2202.12088v1
• Date: Thu, 24 Feb 2022 13:15:17 GMT
• Title: The adoption of non-pharmaceutical interventions and the role of digital infrastructure during the COVID-19 Pandemic in Colombia, Ecuador, and El Salvador
• Authors: Nicol\`o Gozzi, Niccol\`o Comini, Nicola Perra
• Abstract summary: We study the determinants of NPIs adherence during the first wave of the COVID-19 Pandemic in Colombia, Ecuador, and El Salvador. We find a significant correlation between mobility drops and digital infrastructure quality. The link between mobility drops and digital infrastructure quality is stronger at the peak of NPIs stringency.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adherence to the non-pharmaceutical interventions (NPIs) put in place to mitigate the spreading of infectious diseases is a multifaceted problem. Socio-demographic, socio-economic, and epidemiological factors can influence the perceived susceptibility and risk which are known to affect behavior. Furthermore, the adoption of NPIs is dependent upon the barriers, real or perceived, associated with their implementation. We study the determinants of NPIs adherence during the first wave of the COVID-19 Pandemic in Colombia, Ecuador, and El Salvador. Analyses are performed at the level of municipalities and include socio-economic, socio-demographic, and epidemiological indicators. Furthermore, by leveraging a unique dataset comprising tens of millions of internet Speedtest measurements from Ookla, we investigate the quality of the digital infrastructure as a possible barrier to adoption. We use publicly available data provided by Meta capturing aggregated mobility changes as a proxy of adherence to NPIs. Across the three countries considered, we find a significant correlation between mobility drops and digital infrastructure quality. The relationship remains significant after controlling for several factors including socio-economic status, population size, and reported COVID-19 cases. This finding suggests that municipalities with better connectivity were able to afford higher mobility reductions. The link between mobility drops and digital infrastructure quality is stronger at the peak of NPIs stringency. We also find that mobility reductions were more pronounced in larger, denser, and wealthier municipalities. Our work provides new insights on the significance of access to digital tools as an additional factor influencing the ability to follow social distancing guidelines during a health emergency

Related papers

• First 100 days of pandemic; an interplay of pharmaceutical, behavioral and digital interventions -- A study using agent based modeling [14.192977334409104]
  We simulate realistic pharmaceutical, behavioral, and digital interventions that mirror challenges in real-world policy adoption. Our analysis reveals the pivotal role of the initial 100 days in dictating a pandemic's course.
  arXiv  Detail & Related papers  (2024-01-09T19:38:59Z)
• Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures [65.62256987706128]
  Compartmental epidemiological models categorize individuals based on their disease status. We propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens. We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coruna, Spain.
  arXiv  Detail & Related papers  (2023-07-28T08:01:05Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures [0.0]
  We examined the impact of shifting the distribution of mobility on COVID-19 case rates from June 1 - November 14, 2020. Ten mobility indices were selected to capture several aspects of behavior expected to influence and be influenced by COVID-19 case rates.
  arXiv  Detail & Related papers  (2021-10-24T21:17:47Z)
• Adaptive Epidemic Forecasting and Community Risk Evaluation of COVID-19 [9.11149442423076]
  We present a flexible end-to-end solution that seamlessly integrates public health data with tertiary client data to accurately estimate the risk of reopening a community. At its core lies a state-of-the-art prediction model that auto-captures changing trends in transmission and mobility.
  arXiv  Detail & Related papers  (2021-06-03T19:26:37Z)
• Impact of Interventional Policies Including Vaccine on Covid-19 Propagation and Socio-Economic Factors [0.7874708385247353]
  This study aims to provide a predictive analytics framework to model, predict and simulate COVID-19 propagation and socio-economic impact. We have leveraged a recently launched open-source COVID-19 big data platform and used published research to find potentially relevant variables. An advanced machine learning pipeline has been developed armed with a self-evolving model, deployed on a modern machine learning architecture.
  arXiv  Detail & Related papers  (2021-01-11T15:08:07Z)
• Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19 [75.99038202534628]
  We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
  arXiv  Detail & Related papers  (2020-09-23T22:35:43Z)
• Epidemic mitigation by statistical inference from contact tracing data [61.04165571425021]
  We develop Bayesian inference methods to estimate the risk that an individual is infected. We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact.
  arXiv  Detail & Related papers  (2020-09-20T12:24:45Z)
• Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
  We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
  arXiv  Detail & Related papers  (2020-06-23T03:36:19Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.