How well can social scientists predict societal change, and what processes underlie their
predi... more How well can social scientists predict societal change, and what processes underlie their
predictions? To answer these questions, we ran two forecasting tournaments testing accuracy
of predictions of societal change in domains commonly studied in the social sciences: ideological
preferences, political polarization, life satisfaction, sentiment on social media, and gender-career
and racial bias. Following provision of historical trend data on the domain, social scientists
submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams/359
forecasts), with an opportunity to update forecasts based on new data six months later
(Tournament 2; N = 120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that
social scientists’ forecasts were on average no more accurate than simple statistical models
(historical means, random walk, or linear regressions) or the aggregate forecasts of a sample
from the general public (N = 802). However, scientists were more accurate if they had scientific
expertise in a prediction domain, were interdisciplinary, used simpler models, and based
predictions on prior data.
Motivation Outbreak investigations use data from interviews, healthcare providers, laboratories a... more Motivation Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions.
Results We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. Using publicly available HIV sequences and other data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020.
Availability and Implementation MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully-operational without an internet connection. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. The source code is available at https://github.com/cdcgov/microbetrace.
How well can social scientists predict societal change, and what processes underlie their
predi... more How well can social scientists predict societal change, and what processes underlie their
predictions? To answer these questions, we ran two forecasting tournaments testing accuracy
of predictions of societal change in domains commonly studied in the social sciences: ideological
preferences, political polarization, life satisfaction, sentiment on social media, and gender-career
and racial bias. Following provision of historical trend data on the domain, social scientists
submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams/359
forecasts), with an opportunity to update forecasts based on new data six months later
(Tournament 2; N = 120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that
social scientists’ forecasts were on average no more accurate than simple statistical models
(historical means, random walk, or linear regressions) or the aggregate forecasts of a sample
from the general public (N = 802). However, scientists were more accurate if they had scientific
expertise in a prediction domain, were interdisciplinary, used simpler models, and based
predictions on prior data.
Motivation Outbreak investigations use data from interviews, healthcare providers, laboratories a... more Motivation Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions.
Results We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. Using publicly available HIV sequences and other data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020.
Availability and Implementation MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully-operational without an internet connection. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. The source code is available at https://github.com/cdcgov/microbetrace.
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Papers by Tony Boyles
predictions? To answer these questions, we ran two forecasting tournaments testing accuracy
of predictions of societal change in domains commonly studied in the social sciences: ideological
preferences, political polarization, life satisfaction, sentiment on social media, and gender-career
and racial bias. Following provision of historical trend data on the domain, social scientists
submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams/359
forecasts), with an opportunity to update forecasts based on new data six months later
(Tournament 2; N = 120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that
social scientists’ forecasts were on average no more accurate than simple statistical models
(historical means, random walk, or linear regressions) or the aggregate forecasts of a sample
from the general public (N = 802). However, scientists were more accurate if they had scientific
expertise in a prediction domain, were interdisciplinary, used simpler models, and based
predictions on prior data.
Drafts by Tony Boyles
Results We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. Using publicly available HIV sequences and other data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020.
Availability and Implementation MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully-operational without an internet connection. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. The source code is available at https://github.com/cdcgov/microbetrace.
predictions? To answer these questions, we ran two forecasting tournaments testing accuracy
of predictions of societal change in domains commonly studied in the social sciences: ideological
preferences, political polarization, life satisfaction, sentiment on social media, and gender-career
and racial bias. Following provision of historical trend data on the domain, social scientists
submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams/359
forecasts), with an opportunity to update forecasts based on new data six months later
(Tournament 2; N = 120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that
social scientists’ forecasts were on average no more accurate than simple statistical models
(historical means, random walk, or linear regressions) or the aggregate forecasts of a sample
from the general public (N = 802). However, scientists were more accurate if they had scientific
expertise in a prediction domain, were interdisciplinary, used simpler models, and based
predictions on prior data.
Results We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. Using publicly available HIV sequences and other data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020.
Availability and Implementation MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully-operational without an internet connection. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. The source code is available at https://github.com/cdcgov/microbetrace.