scholarly journals Globally Local: Hyper-local Modeling for Accurate Forecast of COVID-19

2020 ◽  
Author(s):  
Vishrawas Gopalakrishnan ◽  
Sayali Pethe ◽  
Sarah Kefayati ◽  
Raman Srinivasan ◽  
Paul Hake ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
State Level ◽  
Ground Truth ◽  
Prediction Errors ◽  
Public Health Response ◽  
Underlying Assumption ◽  
Ground Truth Data ◽  
Local Modeling ◽  
Trade Offs

AbstractMultiple efforts to model the epidemiology of SARS-CoV-2 have recently been launched in support of public health response at the national, state, and county levels. While the pandemic is global, the dynamics of this infectious disease varies with geography, local policies, and local variations in demographics. An underlying assumption of most infectious disease compartment modeling is that of a well mixed population at the resolution of the areas being modeled. The implicit need to model at fine spatial resolution is impeded by the quality of ground truth data for fine scale administrative subdivisions. To understand the trade-offs and benefits of such modeling as a function of scale, we compare the predictive performance of a SARS-CoV-2 modeling at the county, county cluster, and state level for the entire United States. Our results demonstrate that accurate prediction at the county level requires hyper-local modeling with county resolution. State level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. As an important use case, leveraging high resolution modeling with public health data and admissions data from Hillsborough County Florida, we performed weekly forecasts of both hospital admission and ICU bed demand for the county. The repeated forecasts between March and August 2020 were used to develop accurate resource allocation plans for Tampa General Hospital.2010 MSC92-D30, 91-C20

scholarly journals Ethics of instantaneous contact tracing using mobile phone apps in the control of the COVID-19 pandemic

2020 ◽  
Vol 46 (7) ◽  
pp. 427-431 ◽  
Author(s):  
Michael J Parker ◽  
Christophe Fraser ◽  
Lucie Abeler-Dörner ◽  
David Bonsall
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Mobile Phone ◽  
Public Health Practice ◽  
Disease Outbreaks ◽  
Contact Tracing ◽  
Mobile Phone Data ◽  
Public Health Response ◽  
Infectious Disease Outbreaks ◽  
Ethical Implications

In this paper we discuss ethical implications of the use of mobile phone apps in the control of the COVID-19 pandemic. Contact tracing is a well-established feature of public health practice during infectious disease outbreaks and epidemics. However, the high proportion of pre-symptomatic transmission in COVID-19 means that standard contact tracing methods are too slow to stop the progression of infection through the population. To address this problem, many countries around the world have deployed or are developing mobile phone apps capable of supporting instantaneous contact tracing. Informed by the on-going mapping of ‘proximity events’ these apps are intended both to inform public health policy and to provide alerts to individuals who have been in contact with a person with the infection. The proposed use of mobile phone data for ‘intelligent physical distancing’ in such contexts raises a number of important ethical questions. In our paper, we outline some ethical considerations that need to be addressed in any deployment of this kind of approach as part of a multidimensional public health response. We also, briefly, explore the implications for its use in future infectious disease outbreaks.

scholarly journals Leveraging Health Department Capacities, Partnerships, and Health Insurance for Infectious Disease Response in Massachusetts, 2014-2018

2020 ◽  
Vol 135 (1_suppl) ◽  
pp. 75S-81S
Author(s):  
H. Dawn Fukuda ◽  
Liisa M. Randall ◽  
Thera Meehan ◽  
Kevin Cranston
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Health Insurance ◽  
Viral Hepatitis ◽  
Public Health Laboratory ◽  
Health Programs ◽  
Public Health Response ◽  
Public Health Programs ◽  
The Public ◽  
Health Response

Policies facilitating integration of public health programs can improve the public health response, but the literature on approaches to integration across multiple system levels is limited. We describe the efforts of the Massachusetts Department of Public Health to integrate its HIV, viral hepatitis, sexually transmitted infection (STI), and tuberculosis response through policies that mandated contracted organizations to submit specimens for testing to the Massachusetts State Public Health Laboratory; co-test blood specimens for HIV, hepatitis C virus (HCV), and syphilis; integrate HIV, viral hepatitis, and STI disease surveillance and case management in a single data system; and implement an integrated infectious disease drug assistance program. From 2014 through 2018, the number of tests performed by the Massachusetts State Public Health Laboratory increased from 16 321 to 33 674 for HIV, from 11 054 to 33 670 for HCV, and from 19 169 to 30 830 for syphilis. Service contracts enabled rapid response to outbreaks of HIV, hepatitis A, and hepatitis B. Key challenges included lack of a billing infrastructure at the Massachusetts State Public Health Laboratory; the need to complete negotiations with insurers and to establish a retained revenue account to receive health insurance reimbursements for testing services; and time to train testing providers in phlebotomy for required testing. Investing in laboratory infrastructure; creating billing mechanisms to maximize health insurance reimbursement; proactively engaging providers, community members, and other stakeholders; and building capacity to transform practices are needed. Using multilevel policy approaches to integrate the public health response to HIV, STI, viral hepatitis, and tuberculosis is feasible and adaptable to other public health programs.

scholarly journals A Product Feature Inference Model for Mining Implicit Customer Preferences Within Large Scale Social Media Networks

2015 ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Ground Truth ◽  
Inference Model ◽  
Underlying Assumption ◽  
Ground Truth Data ◽  
Social Media Networks ◽  
Customer Preferences ◽  
Product Features ◽  
Online Sources

The acquisition and mining of product feature data from online sources such as customer review websites and large scale social media networks is an emerging area of research. In many existing design methodologies that acquire product feature preferences form online sources, the underlying assumption is that product features expressed by customers are explicitly stated and readily observable to be mined using product feature extraction tools. In many scenarios however, product feature preferences expressed by customers are implicit in nature and do not directly map to engineering design targets. For example, a customer may implicitly state “wow I have to squint to read this on the screen”, when the explicit product feature may be a larger screen. The authors of this work propose an inference model that automatically assigns the most probable explicit product feature desired by a customer, given an implicit preference expressed. The algorithm iteratively refines its inference model by presenting a hypothesis and using ground truth data, determining its statistical validity. A case study involving smartphone product features expressed through Twitter networks is presented to demonstrate the effectiveness of the proposed methodology.

scholarly journals Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

10.2196/14986 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e14986 ◽  
Author(s):  
Ashlynn R Daughton ◽  
Rumi Chunara ◽  
Michael J Paul
Keyword(s):  
Infectious Disease ◽  
Social Media ◽  
Random Sample ◽  
Topic Model ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Social Media Data ◽  
Individual Level ◽  
Small Effect Size ◽  
Media Data

Background Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. Objective This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. Methods This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Results Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). Conclusions To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

scholarly journals Re-emerging Infectious Disease (RED) Alert tool

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Maneesha Chitanvis ◽  
Ashlynn Daughton ◽  
Forest M Altherr ◽  
Geoffery Fairchild ◽  
William Rosenberger ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Incidence ◽  
Emerging Infectious Disease ◽  
Control Measures ◽  
Public Health Response ◽  
Incidence Trends ◽  
Effective Public Health ◽  
Emergence Event

Objective: Although relying on verbal definitions of "re-emergence", descriptions that classify a “re-emergence” event as any significant recurrence of a disease that had previously been under public health control, and subjective interpretations of these events is currently the conventional practice, this has the potential to hinder effective public health responses. Defining re-emergence in this manner offers limited ability for ad hoc analysis of prevention and control measures and facilitates non-reproducible assessments of public health events of potentially high consequence. Re-emerging infectious disease alert (RED Alert) is a decision-support tool designed to address this issue by enhancing situational awareness by providing spatiotemporal context through disease incidence pattern analysis following an event that may represent a local (country-level) re-emergence. The tool’s analytics also provide users with the associated causes (socioeconomic indicators) related to the event, and guide hypothesis-generation regarding the global scenario.Introduction: Definitions of “re-emerging infectious diseases” typically encompass any disease occurrence that was a historic public health threat, declined dramatically, and has since presented itself again as a significant health problem. Examples include antimicrobial resistance leading to resurgence of tuberculosis, or measles re-appearing in previously protected communities. While the language of this verbal definition of “re-emergence” is sensitive enough to capture most epidemiologically relevant resurgences, its qualitative nature obfuscates the ability to quantitatively classify disease re-emergence events as such.Methods: Our tool automatically computes historic disease incidence and performs trend analyses to help elucidate events which a user may considered a true re-emergence in a subset of pertinent infectious diseases (measles, cholera, yellow fever, and dengue). The tool outputs data visualizations that illustrate incidence trends in diverse and informative ways. Additionally, we categorize location and incidence-specific indicators for re-emergence to provide users with associated indicators as well as justifications and documentation to guide users’ next steps. Additionally, the tool also houses interactive maps to facilitate global hypothesis-generation.Results: These outputs provide historic trend pattern analyses as well as contextualization of the user’s situation with similar locations. The tool also broadens users' understanding of the given situation by providing related indicators of the likely re-emergence, as well as the ability to investigate re-emergence factors of global relevance through spatial analysis and data visualization.Conclusions: The inability to categorically name a re-emergence event as such is due to lack of standardization and/or availability of reproducible, data-based evidence, and hinders timely and effective public health response and planning. While the tool will not explicitly call out a user scenario as categorically re-emergent or not, by providing users with context in both time and space, RED Alert aims to empower users with data and analytics in order to substantially enhance their contextual awareness; thus, better enabling them to formulate plans of action regarding re-emerging infectious disease threats at both the country and global level.

Use and effectiveness of mHealth technologies in tracking the spread of infectious disease outbreaks and their implications for the COVID-19 pandemic: a systematic review protocol (Preprint)

2020 ◽  
Author(s):  
Madison Milne-Ives ◽  
Simon Rowland ◽  
Alison McGregor ◽  
J Edward Fitzgerald ◽  
Edward Meinert
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Systematic Review ◽  
Public Health Practice ◽  
Disease Outbreaks ◽  
Digital Technologies ◽  
World Health ◽  
Health Practice ◽  
Public Health Response ◽  
Infectious Disease Outbreaks

BACKGROUND The World Health Organisation (WHO) defines mHealth as medical and public health practice supported by mobile devices. A number of mHealth devices, primarily apps designed to support contact tracing, have been utilised as part of the public health response to the Covid-19 pandemic. The value of mHealth devices in augmenting public health practice is however yet to be defined. OBJECTIVE The study aims to address three research questions: (1) What digital technologies are being used to track the symptoms and spread of infectious disease outbreaks and what strategies do they use to do so? (2) How effective and cost-effective are digital technologies at tracking the spread of infectious disease outbreaks and what are their strengths and limitations? (3) What are the user perspectives on the usability and effectiveness of these technologies? METHODS The PICOS template and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) will be followed for this systematic review. The review will be composed of a literature search, article selection, data extraction, quality appraisal, data analysis, and a discussion of the implications of the data for the current COVID-19 pandemic. RESULTS N/A CONCLUSIONS This systematic review will summarise the available evidence for use of mHealth devices for tracking the spread of infectious disease outbreaks. These results are potentially valuable for informing public health policy during infectious disease outbreaks such as the current Covid-19 pandemic.

Real-Time Infectious Disease Modeling to Inform Emergency Public Health Decision Making

2022 ◽  
Vol 43 (1) ◽  
Author(s):  
Anna Bershteyn ◽  
Hae-Young Kim ◽  
R. Scott Braithwaite
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Real Time ◽  
Disease Modeling ◽  
Disease Process ◽  
Annual Review ◽  
Publication Date ◽  
Public Health Response ◽  
Response Options ◽  
Infectious Disease Modeling

Infectious disease transmission is a nonlinear process with complex, sometimes unintuitive dynamics. Modeling can transform information about a disease process and its parameters into quantitative projections that help decision makers compare public health response options. However, modelers face methodologic challenges, data challenges, and communication challenges, which are exacerbated under the time constraints of a public health emergency. We review methods, applications, challenges and opportunities for real-time infectious disease modeling during public health emergencies, with examples drawn from the two deadliest pandemics in recent history: HIV/AIDS and coronavirus disease 2019 (COVID-19). Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Data Processing Techniques for Real-Time Traveler Information: Use of Dedicated Short-Range Communications Probes on Suburban Arterial

2020 ◽  
Vol 14 (1) ◽  
pp. 99-108
Author(s):  
Jinhwan Jang
Keyword(s):  
Data Processing ◽  
Travel Time ◽  
Real Time ◽  
Sequence Data ◽  
Time Lag ◽  
Ground Truth ◽  
Prediction Errors ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Ground Truth Data

Background: As wireless communication technologies evolve, probe-based travel-time collection systems are becoming popular around the globe. However, two problems generally arise in probe-based systems: one is the outlier and the other is time lag. To resolve the problems, methods for outlier removal and travel-time prediction need to be applied. Methods: In this study, data processing methods for addressing the two issues are proposed. After investigating the characteristic of the travel times on the test section, the modified z-score was suggested for censoring outliers contained in probe travel times. To mitigate the time-lag phenomenon, a recurrent neural network, a class of deep learning where temporal sequence data are normally treated, was applied to predict travel times. Results: As a result of evaluation with ground-truth data obtained through test-car runs, the proposed methods showed enhanced performances with prediction errors lower than 13% on average compared to current practices. Conclusion: The suggested methods can make drivers to better arrange their trip schedules with real-time travel-time information with improved accuracy.

scholarly journals Early COVID-19 Interventions Failed to Replicate 1918 St. Louis vs. Philadelphia Outcomes in the United States

2020 ◽  
Author(s):  
Aliea M. Jalali ◽  
Brent M. Peterson ◽  
Thushara Galbadage
Keyword(s):  
Public Health ◽  
United States ◽  
State Level ◽  
The United States ◽  
The State ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Public Health Response ◽  
Health Response ◽  
The Us

The Coronavirus disease 2019 (COVID-19) pandemic has elicited an abrupt pause in the United States in multiple sectors of commerce and social activity. As the US faces this health crisis, the magnitude, and rigor of their initial public health response was unprecedented. As a response, the entire nation shutdown at the state-level for the duration of approximately one to three months. These public health interventions, however, were not arbitrarily decided, but rather, implemented as a result of evidence-based practices. These practices were a result of lessons learned during the 1918 influenza pandemic and the city-level non-pharmaceutical interventions (NPIs) taken across the US. During the 1918 pandemic, two model cities, St. Louis, MO, and Philadelphia, PA, carried out two different approaches to address the spreading disease, which resulted in two distinctly different outcomes. Our group has evaluated the state-level public health response adopted by states across the US, with a focus on New York, California, Florida, and Texas, and compared the effectiveness of reducing the spread of COVID-19. Our assessments show that while the states mentioned above benefited from the implementations of early preventative measures, they inadequately replicated the desired outcomes observed in St. Louis during the 1918 crisis. Our study indicates that there are other factors, including health disparities that may influence the effectiveness of public health interventions applied. Identifying more specific health determinants may help implement targeted interventions aimed at preventing the spread of COVID-19 and improving health equity.

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