CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics

• URL: http://arxiv.org/abs/2005.12783v2
• Date: Fri, 26 Jun 2020 12:18:50 GMT
• Title: CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics
• Authors: Oluwasegun Ojo, Augusto Garc\'ia-Agundez, Benjamin Girault, Harold Hern\'andez, Elisa Cabana, Amanda Garc\'ia-Garc\'ia, Payman Arabshahi, Carlos Baquero, Paolo Casari, Ednaldo Jos\'e Ferreira, Davide Frey, Chryssis Georgiou, Mathieu Goessens, Anna Ishchenko, Ernesto Jim\'enez, Oleksiy Kebkal, Rosa Lillo, Raquel Menezes, Nicolas Nicolaou, Antonio Ortega, Paul Patras, Julian C Roberts, Efstathios Stavrakis, Yuichi Tanaka, Antonio Fern\'andez Anta
• Abstract summary: We propose a technique based on (anonymous) surveys in which participants report on the health status of their contacts. This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts. Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool for LMICs.
• Score: 29.03294669532478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. National governments have problems evaluating the reach of the epidemic, due to having limited resources and tests at their disposal. This problem is especially acute in low and middle-income countries (LMICs). Hence, any simple, cheap and flexible means of evaluating the incidence and evolution of the epidemic in a given country with a reasonable level of accuracy is useful. In this paper, we propose a technique based on (anonymous) surveys in which participants report on the health status of their contacts. This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts, and collects information from a larger fraction of the population (as compared to individual surveys). This technique has been deployed in the CoronaSurveys project, which has been collecting reports for the COVID-19 pandemic for more than two months. Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool for LMICs.

Related papers

• Predicting COVID-19 and pneumonia complications from admission texts [101.28793285063904]
  We present a novel approach to risk assessment for patients hospitalized with pneumonia or COVID-19 based on their admission reports. We applied a Longformer neural network to admission reports and other textual data available shortly after admission to compute risk scores for the patients.
  arXiv  Detail & Related papers  (2023-05-05T16:28:44Z)
• Exploring the Scope and Potential of Local Newspaper-based Dengue Surveillance in Bangladesh [1.181206257787103]
  Dengue fever has been considered to be one of the global public health problems of the twenty-first century. It is so prevalent in such regions that enforcing a granular level of surveillance is quite impossible. It is crucial to explore an alternative cost-effective solution that can provide updates of the ongoing situation in a timely manner.
  arXiv  Detail & Related papers  (2021-07-07T18:48:15Z)
• Misinformation, Believability, and Vaccine Acceptance Over 40 Countries: Takeaways From the Initial Phase of The COVID-19 Infodemic [11.737540072863405]
  This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic. We find a strong association between perceived believability of misinformation and vaccination hesitancy. We discuss implications of our findings on public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic.
  arXiv  Detail & Related papers  (2021-04-22T05:09:25Z)
• COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms [12.864257751458712]
  We use self-reported symptoms survey data to understand trends in the spread of COVID-19. From our studies, we try to predict the likely % of the population that tested positive for COVID-19 based on self-reported symptoms. We forecast that % of the population having COVID-19-like illness (CLI) and those tested positive as 0.15% and 1.14% absolute error respectively.
  arXiv  Detail & Related papers  (2020-12-21T00:37:24Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• 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)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• 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)
• Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazi [0.0]
  This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil. Several Brazilian states are in a state of lock-down. This work considers the impact that such a termination would have on how the virus evolves locally.
  arXiv  Detail & Related papers  (2020-04-15T07:37:08Z)
• A County-level Dataset for Informing the United States' Response to COVID-19 [5.682299443164938]
  We present a dataset that aggregates relevant data from governmental, journalistic, and academic sources on the U.S. county level. Our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and income, climate, transit, scores, and healthcare system-related metrics.
  arXiv  Detail & Related papers  (2020-04-01T05:07:27Z)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

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.