TRACE‐Omicron: Policy Counterfactuals to Inform Mitigation of COVID‐19 Spread in the United States DOI Creative Commons
David O’Gara, Samuel F. Rosenblatt, Laurent Hébert‐Dufresne

et al.

Advanced Theory and Simulations, Journal Year: 2023, Volume and Issue: 6(7)

Published: April 28, 2023

The Omicron wave was the largest of COVID-19 pandemic to date, more than doubling any other in terms cases and hospitalizations United States. In this paper, we present a large-scale agent-based model policy interventions that could have been implemented mitigate wave. Our takes into account behaviors individuals their interactions with one another within nationally representative population, as well efficacy various such social distancing, mask wearing, testing, tracing, vaccination. We use simulate impact different scenarios evaluate potential effectiveness controlling spread virus. results suggest substantially curtailed via combination comparable extreme unpopular singular measures widespread closure schools workplaces, highlight importance early decisive action.

Language: Английский

The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination DOI Creative Commons
Joshua A. Salomon, Alex Reinhart, Alyssa Bilinski

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2021, Volume and Issue: 118(51)

Published: Dec. 13, 2021

Significance The US COVID-19 Trends and Impact Survey (CTIS) has operated continuously since April 6, 2020, collecting over 20 million responses. As the largest public health survey conducted in United States to date, CTIS was designed facilitate detailed demographic geographic analyses, track trends time, accommodate rapid revision address emerging priorities. Using examples of results illuminating symptoms, risks, mitigating behaviors, testing, vaccination relation evolving high-priority policy questions 12 mo pandemic, we illustrate value online surveys for tracking patterns COVID outcomes as an adjunct official reporting, showcase unique insights that would not be visible through traditional reporting.

Language: Английский

Citations

139
Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency DOI Creative Commons
Mario Coccia

AIMS Public Health, Journal Year: 2023, Volume and Issue: 10(1), P. 145 - 168

Published: Jan. 1, 2023

<abstract> <p>Scholars and experts argue that future pandemics and/or epidemics are inevitable events, the problem is not whether they will occur, but when a new health emergency emerge. In this uncertain scenario, one of most important questions an accurate prevention, preparedness prediction for next pandemic. The main goal study twofold: first, clarification sources factors may trigger pandemic threats; second, examination models on-going pandemics, showing pros cons. Results, based on in-depth systematic review, show vital role environmental in spread Coronavirus Disease 2019 (COVID-19), many limitations epidemiologic because complex interactions between viral agent SARS-CoV-2, environment society have generated variants sub-variants with rapid transmission. insights here are, whenever possible, to clarify these aspects associated public order provide lessons learned policy reduce risks emergence diffusion having negative societal impact.</p> </abstract>

Language: Английский

Citations

90
The United States COVID-19 Forecast Hub dataset DOI Creative Commons
Estee Y. Cramer, Yuxin Huang, Yijin Wang

et al.

Scientific Data, Journal Year: 2022, Volume and Issue: 9(1)

Published: Aug. 1, 2022

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, United States Centers for Disease Control Prevention (CDC) partnered with academic research lab University of Massachusetts Amherst to create US Forecast Hub. Launched in April 2020, Hub is a dataset point probabilistic incident cases, hospitalizations, deaths, cumulative deaths due county, state, national, levels States. Included represent variety modeling approaches, data sources, assumptions regarding spread COVID-19. The goal this establish standardized comparable set short-term from teams. These can be used develop ensemble models, communicate public, visualizations, compare inform policies mitigation. open-source are available via download GitHub, through online API, R packages.

Language: Английский

Citations

87
Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years DOI Creative Commons
Amanda C. Perofsky, C. Hansen, Roy Burstein

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 16, 2024

Abstract Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population on transmission 17 viruses in Seattle over a 4-year period, 2018-2022. Before 2020, visits schools daycares, within-city mixing, visitor inflow preceded or coincided with seasonal outbreaks viruses. Pathogen circulation dropped substantially after initiation COVID-19 stay-at-home orders March 2020. During this was positive, leading indicator all lagging negatively correlated activity. Mobility briefly predictive when restrictions relaxed but associations weakened subsequent waves. The rebound heterogeneously timed exhibited stronger, longer-lasting than SARS-CoV-2. Overall, is most virus during periods dramatic behavioral change at beginning epidemic

Language: Английский

Citations

16
Collaborative Hubs: Making the Most of Predictive Epidemic Modeling DOI
Nicholas G Reich, Justin Lessler, Sebastian Funk

et al.

American Journal of Public Health, Journal Year: 2022, Volume and Issue: 112(6), P. 839 - 842

Published: April 14, 2022

Language: Английский

Citations

59
Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction? DOI Creative Commons
Daniel J. McDonald,

Jacob Bien,

Alden Green

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2021, Volume and Issue: 118(51)

Published: Dec. 13, 2021

Significance Validated forecasting methodology should be a vital element in the public health response to any fast-moving epidemic or pandemic. A widely used model for predicting future spread of temporal process is an autoregressive (AR) model. While basic, such AR (properly trained) already competitive with top models operational use COVID-19 forecasting. In this paper, we exhibit five auxiliary indicators—based on deidentified medical insurance claims, self-reported symptoms via online surveys, and COVID-related Google searches—that further improve predictive accuracy The most substantial gains appear quiescent times; but search indicator appears also offer improvements during upswings pandemic activity.

Language: Английский

Citations

41
An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation DOI Creative Commons

Kristen Nixon,

Sonia Jindal,

Felix Parker

et al.

The Lancet Digital Health, Journal Year: 2022, Volume and Issue: 4(10), P. e738 - e747

Published: Sept. 20, 2022

Infectious disease modelling can serve as a powerful tool for situational awareness and decision support policy makers. However, COVID-19 efforts faced many challenges, from poor data quality to changing human behaviour. To extract practical insight the large body of literature available, we provide narrative review with systematic approach that quantitatively assessed prospective, data-driven studies in USA. We analysed 136 papers, focused on aspects models are essential have documented forecasting window, methodology, prediction target, datasets used, geographical resolution each study. also found fraction papers did not evaluate performance (25%), express uncertainty (50%), or state limitations (36%). remedy some these identified gaps, recommend adoption EPIFORGE 2020 model reporting guidelines creating an information-sharing system is suitable fast-paced infectious outbreak science.

Language: Английский

Citations

37
An analysis of COVID-19 vaccine hesitancy in the U.S. DOI Creative Commons
Hieu Bui, Sandra D. Ekşioğlu, Rubén A. Proaño

et al.

IISE Transactions, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 15

Published: Jan. 5, 2024

Reluctance or refusal to get vaccinated, commonly known as Vaccine Hesitancy (VH), poses a significant challenge COVID-19 vaccination campaigns. Understanding the factors contributing VH is essential for shaping effective public health strategies. This study proposes novel framework combining machine learning with publicly available data generate proxy metric that evaluates dynamics of faster than currently used survey methods. The input descriptive classification models analyze wide array data, aiming identify key associated at county level in U.S. during pandemic (i.e., January October 2021). Both static and dynamic are considered. We use Random Forest classifier identifies political affiliation Google search trends most influencing behavior. model categorizes counties into five distinct clusters based on Cluster 1, low VH, consists mainly Democratic-leaning residents who, have longest life expectancy, college degree, highest income per capita, live metropolitan areas. 5, high predominantly Republican-leaning individuals non-metropolitan Individuals 1 more responsive policies.

Language: Английский

Citations

5
COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina DOI Creative Commons
Erik Rosenstrom, Julie S. Ivy, María E. Mayorga

et al.

Epidemics, Journal Year: 2024, Volume and Issue: 46, P. 100752 - 100752

Published: Feb. 23, 2024

We document the evolution and use of stochastic agent-based COVID-19 SIMu-lation model (COVSIM) to study impact population behaviors public health policy on disease spread within age, race/ethnicity, urbanicity subpopulations in North Carolina. detail methodologies used complexities COVID-19, including multiple agent attributes (i.e., high-risk medical status), census tract-level interaction network, state behavior masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), variants. describe its uses outside Scenario Modeling Hub (CSMH), which has focused interplay nonpharmaceutical interventions, equitability vaccine distribution, supporting local county decision-makers This work led publications meetings with a variety stakeholders. When COVSIM joined CSMH January 2022, we found it was sustainable way support new challenges allowed group focus broader scientific questions. The informed adaptions our modeling approach, redesigning high-performance computing implementation.

Language: Английский

Citations

5
Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS) DOI Creative Commons
Youqi Yang, Walter Dempsey,

Peisong Han

et al.

Science Advances, Journal Year: 2024, Volume and Issue: 10(22)

Published: May 31, 2024

Selection bias poses a substantial challenge to valid statistical inference in nonprobability samples. This study compared estimates of the first-dose COVID-19 vaccination rates among Indian adults 2021 from large sample, Trends and Impact Survey (CTIS), small probability survey, Center for Voting Options Election Research (CVoter), against national benchmark data COVID Vaccine Intelligence Network. Notably, CTIS exhibits larger estimation error on average (0.37) CVoter (0.14). Additionally, we explored accuracy (regarding mean squared error) estimating successive differences (over time) subgroup (for females versus males) vaccine uptakes. Compared overall rates, targeting these alternative estimands comparing or relative two means increased effective sample size. These results suggest that Big Data Paradox can manifest countries beyond United States may not apply equally every estimand interest.

Language: Английский

Citations

4