Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study
| Autor: | Yhenneko J. Taylor, Marc A. Kowalkowski, Pooja P. Palmer, Andrew McWilliams, Melanie D. Spencer, Shih-Hsiung Chou, Jennifer S. Priem, Philip J. Turk |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
020205 medical informatics Pneumonia Viral Health Informatics Context (language use) forecasting 02 engineering and technology 03 medical and health sciences latent prevalence 0302 clinical medicine Public health surveillance Pandemic Health care 0202 electrical engineering electronic engineering information engineering medicine North Carolina Prevalence Humans 030212 general & internal medicine Cities Pandemics Retrospective Studies Original Paper Models Statistical business.industry Public health pandemic Hazard ratio Public Health Environmental and Occupational Health novel coronavirus 2019 Outbreak COVID-19 public health surveillance Geography detection probability Public aspects of medicine RA1-1270 SIR model business Coronavirus Infections Basic reproduction number Demography |
| Zdroj: | JMIR Public Health and Surveillance JMIR Public Health and Surveillance, Vol 6, Iss 2, p e19353 (2020) |
| ISSN: | 2369-2960 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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