Modeling COVID-19 latent prevalence to assess a public health intervention at a state and regional scale

AbstractsBackgroundEmergence of COVID-19 caught the world off-guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their healthcare systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policymakers to make informed decisions during a rapidly evolving pandemic.ObjectiveThe goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte metropolitan region and to incorporate the effect of a public health intervention to reduce disease spread, while accounting for unique regional features and imperfect detection.MethodsThree SIR models were fit to prevalence data from the state and the greater Charlotte region and then rigorously compared. One of these models (SIR-Int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases.ResultsPresently, the COVID-19 outbreak is rapidly decelerating in NC and the Charlotte region. Infection curves are flattening at both the state and regional level. Relatively speaking, the greater Charlotte region has responded more favorably to the stay-at-home intervention than NC as a whole. While an initial basic SIR model served the purpose of informing decision making in the early days of the pandemic, its forecast increasingly did not fit the data over time. However, as the pandemic and local conditions evolved, the SIR-Int model provided a good fit to the data.ConclusionsUsing local data and continuous attention to model adaptation, our findings have enabled policymakers, public health officials and health systems to do capacity planning and evaluate the impact of a public health intervention. Our SIR-Int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated the efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

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Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study (Preprint)

10.2196/preprints.19353 ◽

2020 ◽

Cited By ~ 1

Author(s):

Philip J Turk ◽

Shih-Hsiung Chou ◽

Marc A Kowalkowski ◽

Pooja P Palmer ◽

Jennifer S Priem ◽

...

Keyword(s):

Public Health ◽

North Carolina ◽

Public Health Intervention ◽

Sir Model ◽

Health Intervention ◽

Disease Spread ◽

Local Context ◽

Imperfect Detection ◽

Policy Makers ◽

Spatial Differences

BACKGROUND Emergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. OBJECTIVE The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. METHODS Three SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics such as the basic reproduction number. RESULTS Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences, this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention, defined as 1 – the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. CONCLUSIONS Using local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

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Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study

JMIR Public Health and Surveillance ◽

10.2196/19353 ◽

2020 ◽

Vol 6 (2) ◽

pp. e19353

Author(s):

Philip J Turk ◽

Shih-Hsiung Chou ◽

Marc A Kowalkowski ◽

Pooja P Palmer ◽

Jennifer S Priem ◽

...

Keyword(s):

Public Health ◽

North Carolina ◽

Public Health Intervention ◽

Sir Model ◽

Health Intervention ◽

Disease Spread ◽

Local Context ◽

Imperfect Detection ◽

Policy Makers ◽

Spatial Differences

Background Emergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. Objective The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. Methods Three SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics such as the basic reproduction number. Results Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences, this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention, defined as 1 – the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. Conclusions Using local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

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New Insights into an Epidemic SIR Model for Control and Public Health Intervention

10.1109/ccta48906.2021.9659144 ◽

2021 ◽

Author(s):

Enrique Barbieri ◽

William E. Fitzgibbon ◽

Jeff Morgan

Keyword(s):

Public Health ◽

Public Health Intervention ◽

Sir Model ◽

Health Intervention

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At-home testing to mitigate community transmission of SARS-CoV-2: protocol for a public health intervention with a nested prospective cohort study

BMC Public Health ◽

10.1186/s12889-021-12007-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Emily J. Ciccone ◽

Donaldson F. Conserve ◽

Gaurav Dave ◽

Christoph P. Hornik ◽

Marlena L. Kuhn ◽

...

Keyword(s):

Public Health ◽

Community Engagement ◽

Ecological Study ◽

Underserved Populations ◽

Public Health Intervention ◽

Health Intervention ◽

Home Testing ◽

Self Testing ◽

Community Transmission ◽

At Home

Abstract Background The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve as a global health crisis. Although highly effective vaccines have been developed, non-pharmaceutical interventions remain critical to controlling disease transmission. One such intervention—rapid, at-home antigen self-testing—can ease the burden associated with facility-based testing programs and improve testing access in high-risk communities. However, its impact on SARS-CoV-2 community transmission has yet to be definitively evaluated, and the socio-behavioral aspects of testing in underserved populations remain unknown. Methods As part of the Rapid Acceleration of Diagnostics–Underserved Populations (RADx-UP) program funded by the National Institutes of Health, we are implementing a public health intervention titled “Say Yes! COVID Test” (SYCT) involving at-home self-testing using a SARS-CoV-2 rapid antigen assay in North Carolina (Greenville, Pitt County) and Tennessee (Chattanooga City, Hamilton County). The intervention is supported by a multifaceted communication and community engagement strategy to ensure widespread awareness and uptake, particularly in marginalized communities. Participants receive test kits either through online orders or via local community distribution partners. To assess the impact of this intervention on SARS-CoV-2 transmission, we will conduct a non-randomized, ecological study using community-level outcomes. Specifically, we will evaluate trends in SARS-CoV-2 cases and hospitalizations, SARS-CoV-2 viral load in wastewater, and population mobility in each community before, during, and after the SYCT intervention. Individuals who choose to participate in SYCT will also have the option to enroll in an embedded prospective cohort substudy gathering participant-level data to evaluate behavioral determinants of at-home self-testing and socio-behavioral mechanisms of SARS-CoV-2 community transmission. Discussion This is the first large-scale, public health intervention implementing rapid, at-home SARS-CoV-2 self-testing in the United States. The program consists of a novel combination of an at-home testing program, a broad communications and community engagement strategy, an ecological study to assess impact, and a research substudy of the behavioral aspects of testing. The findings from the SYCT project will provide insights into innovative methods to mitigate viral transmission, advance the science of public health communications and community engagement, and evaluate emerging, novel assessments of community transmission of disease.

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The Affective Politics of Karma among Buddhist Cancer Charities in Vietnam

Journal of Vietnamese Studies ◽

10.1525/vs.2020.15.4.33 ◽

2020 ◽

Vol 15 (4) ◽

pp. 33-62

Author(s):

Sara Swenson

Keyword(s):

Public Health ◽

Global Existence ◽

Food Production ◽

Public Health Intervention ◽

Health Intervention ◽

Moral Failure ◽

Cancer Rates ◽

Charity Work

In this article, I explore how Buddhist charity workers in Vietnam interpret rising cancer rates through understandings of karma. Rather than framing cancer as a primarily physical or medical phenomenon, volunteers state that cancer is a product of collective moral failure. Corruption in public food production is both caused by and perpetuates bad karma, which negatively impacts global existence. Conversely, charity work creates merit, which can improve collective karma and benefit all living beings. I argue that through such interpretations of karma, Buddhist volunteers understand their charity at cancer hospitals as an affective and ethical form of public health intervention.

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Comparison of the second and third waves of the COVID-19 pandemic in South Korea: Importance of early public health intervention

International Journal of Infectious Diseases ◽

10.1016/j.ijid.2021.02.004 ◽

2021 ◽

Vol 104 ◽

pp. 742-745

Author(s):

Hye Seong ◽

Hak Jun Hyun ◽

Jin Gu Yun ◽

Ji Yun Noh ◽

Hee Jin Cheong ◽

...

Keyword(s):

Public Health ◽

South Korea ◽

Public Health Intervention ◽

Health Intervention

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Measuring the Efficacy of a Pilot Public Health Intervention for Engaging Communities of Puerto Rico to Rapidly Write Hurricane Protection Plans

Prehospital and Disaster Medicine ◽

10.1017/s1049023x20001429 ◽

2020 ◽

pp. 1-10

Author(s):

Mark E. Keim ◽

Laura A. Runnels ◽

Alexander P. Lovallo ◽

Margarita Pagan Medina ◽

Eduardo Roman Rosa ◽

...

Keyword(s):

Public Health ◽

Puerto Rico ◽

User Satisfaction ◽

National Guard ◽

Rapid Development ◽

Public Health Intervention ◽

Health Intervention ◽

Project Work ◽

Community Members ◽

Community Based

Abstract Objective: The efficacy is measured for a public health intervention related to community-based planning for population protection measures (PPMs; ie, shelter-in-place and evacuation). Design: This is a mixed (qualitative and quantitative) prospective study of intervention efficacy, measured in terms of usability related to effectiveness, efficiency, satisfaction, and degree of community engagement. Setting: Two municipalities in the Commonwealth of Puerto Rico are included. Participants: Community members consisting of individuals; traditional leaders; federal, territorial, and municipal emergency managers; municipal mayors; National Guard; territorial departments of education, health, housing, public works, and transportation; health care; police; Emergency Medical Services; faith-based organizations; nongovernmental organizations (NGOs); and the private sector. Intervention: The intervention included four community convenings: one for risk communication; two for plan-writing; and one tabletop exercise (TTX). This study analyzed data collected from the project work plan; participant rosters; participant surveys; workshop outputs; and focus group interviews. Main Outcome Measures: Efficacy was measured in terms of ISO 9241-11, an international standard for usability that includes effectiveness, efficiency, user satisfaction, and “freedom from risk” among users. Degree of engagement was considered an indicator of “freedom from risk,” measurable through workshop attendance. Results: Two separate communities drafted and exercised ~60-page-long population protection plans, each within 14.5 hours. Plan-writing workshops completed 100% of plan objectives and activities. Efficiency rates were nearly the same in both communities. Interviews and surveys indicated high degrees of community satisfaction. Engagement was consistent among community members and variable among governmental officials. Conclusions: Frontline communities have successfully demonstrated the ability to understand the environmental health hazards in their own community; rapidly write consensus-based plans for PPMs; participate in an objective-based TTX; and perform these activities in a bi-lingual setting. This intervention appears to be efficacious for public use in the rapid development of community-based PPMs.

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