Combining predictive models with future change scenarios can produce credible forecasts of COVID-19 futures

Newcomb, Ken; Bilal, Shakir; Michael, Edwin

doi:10.1371/journal.pone.0277521

Cited by 4 publications

(4 citation statements)

References 57 publications

(85 reference statements)

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“…In conclusion, data from telehealth services help model COVID-19 spread and may be helpful in other health situations. Telehealth data and digital health technologies for monitoring disease spread may be especially useful considering the resurgence of new SARS-CoV-2 variants [ 36 ]. Data collected from primary health care systems can be used for monitoring the dynamics of COVID-19 cases and the geographic localization of cases.…”

Section: Discussionmentioning

confidence: 99%

Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil

Boaventura¹,

Grave²,

Cerqueira‐Silva³

et al. 2023

JMIR Public Health Surveill

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Background Telehealth has been widely used for new case detection and telemonitoring during the COVID-19 pandemic. It safely provides access to health care services and expands assistance to remote, rural areas and underserved communities in situations of shortage of specialized health professionals. Qualified data are systematically collected by health care workers containing information on suspected cases and can be used as a proxy of disease spread for surveillance purposes. However, the use of this approach for syndromic surveillance has yet to be explored. Besides, the mathematical modeling of epidemics is a well-established field that has been successfully used for tracking the spread of SARS-CoV-2 infection, supporting the decision-making process on diverse aspects of public health response to the COVID-19 pandemic. The response of the current models depends on the quality of input data, particularly the transmission rate, initial conditions, and other parameters present in compartmental models. Telehealth systems may feed numerical models developed to model virus spread in a specific region. Objective Herein, we evaluated whether a high-quality data set obtained from a state-based telehealth service could be used to forecast the geographical spread of new cases of COVID-19 and to feed computational models of disease spread. Methods We analyzed structured data obtained from a statewide toll-free telehealth service during 4 months following the first notification of COVID-19 in the Bahia state, Brazil. Structured data were collected during teletriage by a health team of medical students supervised by physicians. Data were registered in a responsive web application for planning and surveillance purposes. The data set was designed to quickly identify users, city, residence neighborhood, date, sex, age, and COVID-19–like symptoms. We performed a temporal-spatial comparison of calls reporting COVID-19–like symptoms and notification of COVID-19 cases. The number of calls was used as a proxy of exposed individuals to feed a mathematical model called “susceptible, exposed, infected, recovered, deceased.” Results For 181 (43%) out of 417 municipalities of Bahia, the first call to the telehealth service reporting COVID-19–like symptoms preceded the first notification of the disease. The calls preceded, on average, 30 days of the notification of COVID-19 in the municipalities of the state of Bahia, Brazil. Additionally, data obtained by the telehealth service were used to effectively reproduce the spread of COVID-19 in Salvador, the capital of the state, using the “susceptible, exposed, infected, recovered, deceased” model to simulate the spatiotemporal spread of the disease. Conclusions Data from telehealth services confer high effectiveness in anticipating new waves of COVID-19 and may help understand the epidemic dynamics.

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Section: Discussionmentioning

confidence: 99%

Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil

Boaventura¹,

Grave²,

Cerqueira‐Silva³

et al. 2023

JMIR Public Health Surveill

View full text Add to dashboard Cite

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“…Vaccine efficacy is implemented according to dosage viz. first dose is 75% efficient, while second dose has a 95% efficacy and booster has a 99% efficacy, and a waning individual has a reduced efficacy of 90% between second dose and subsequent booster [67]. The total number of vaccines on a given day were distributed by age and that distribution changed over time according to vaccination guidelines [62, 63, 68, 69], see Table S4 in the supplementary document.…”

Section: Framework and Methodologymentioning

confidence: 99%

CitySEIRCast: An Agent-Based City Digital Twin for Pandemic Analysis and Simulation

Bilal,

Zaatour,

Otano

et al. 2023

Preprint

Self Cite

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The COVID-19 pandemic has dramatically highlighted the importance of developing simulation systems for quickly characterizing and providing spatio-temporal forecasts of infection spread dynamics that take specific accounts of the population and spatial heterogeneities that govern pathogen transmission in real-world communities. Developing such computational systems must also overcome the cold start problem related to the inevitable scarce early data and extant knowledge regarding a novel pathogen’s transmissibility and virulence, while addressing changing population behavior and policy options as a pandemic evolves. Here, we describe how we have coupled advances in the construction of digital or virtual models of real-world cities with an agile, modular, agent-based model of viral transmission and data from navigation and social media interactions, to overcome these challenges in order to provide a new simulation tool, CitySEIRCast, that can model viral spread at the sub-national level. Our data pipelines and workflows are designed purposefully to be flexible and scalable so that we can implement the system on hybrid cloud/cluster systems and be agile enough to address different population settings and indeed, diseases. Our simulation results demonstrate that CitySEIRCast can provide the timely high resolution spatio-temporal epidemic predictions required for supporting situational awareness of the state of a pandemic as well as for facilitating assessments of vulnerable sub-populations and locations and evaluations of the impacts of implemented interventions, inclusive of the effects of population behavioral response to fluctuations in case incidence. This work arose in response to requests from county agencies to support their work on COVID-19 monitoring, risk assessment, and planning, and using the described workflows, we were able to provide uninterrupted bi-weekly simulations to guide their efforts for over a year from late 2021 to 2023. We discuss future work that can significantly improve the scalability and real-time application of this digital city-based epidemic modelling system, such that validated predictions and forecasts of the paths that may followed by a contagion both over time and space can be used to anticipate the spread dynamics, risky groups and regions, and options for responding effectively to a complex epidemic.INDEX TERMSAgent-based Modeling, city-scale digital twins, disease transmission, epidemiology, geospatial modeling, healthcare interventions, lockdowns, microsimulation, model validation, synthetic populations, risk groups, vaccinations, visual analytics.

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“…As we cautiously navigate towards a post-pandemic world, the transmission dynamics of COVID-19 continue to shape our approach. Anticipating challenges in the post-vaccination era, monitoring emerging variants, and maintaining vigilance in public health practices will be crucial, ongoing research remains instrumental in refining our strategies and adapting to the evolving landscape (Newcomb et al, 2022).…”

Section: Transmission Dynamics and Spread Of Covid-19mentioning

confidence: 99%

Pandemic Epidemiology: A Comprehensive Review of Covid-19 Lessons and Future Healthcare Preparedness

Olufunke Omotayo,

Muridzo Muonde,

Tolulope O Olorunsogo

et al. 2024

imsrj

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This comprehensive review examines the epidemiology of the COVID-19 pandemic, shedding light on pivotal lessons learned and their implications for future healthcare preparedness. Analyzing the global landscape of the virus, the review delves into transmission dynamics, risk factors influencing severe outcomes, and the effectiveness of diverse public health interventions. It provides a nuanced exploration of the challenges encountered by healthcare systems worldwide, elucidating strengths and vulnerabilities brought to the forefront by the pandemic. The study underscores the need for a multifaceted approach to healthcare preparedness, emphasizing the critical importance of integrated surveillance systems, agile response mechanisms, and the implementation of evidence-based strategies. Drawing on experiences from diverse regions, the review identifies key lessons in pandemic management, showcasing successful strategies and innovative interventions that have proven effective in curbing the virus's spread. Moreover, the review examines the role of vaccination campaigns in shaping the trajectory of the pandemic and emphasizes the vital contribution of technology in bolstering healthcare resilience. As the world grapples with the long-term effects of COVID-19, this review aims to distill essential insights that can inform future pandemic preparedness efforts. By synthesizing knowledge gleaned from the challenges posed by COVID-19, this comprehensive analysis offers a foundation for evidence-based strategies, guiding global healthcare systems towards enhanced readiness to confront and mitigate the impact of future infectious threats. Keywords: Pandemic, Epidemiology, COVID-19, Healthcare, Review.

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Combining predictive models with future change scenarios can produce credible forecasts of COVID-19 futures

Cited by 4 publications

References 57 publications

Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil

Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil

CitySEIRCast: An Agent-Based City Digital Twin for Pandemic Analysis and Simulation

Pandemic Epidemiology: A Comprehensive Review of Covid-19 Lessons and Future Healthcare Preparedness

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