Modeling and predicting the spread of COVID-19

Ojokoh, Bolanle Adefowoke; Sarumi, Oluwafemi A.; Salako, Kolawolé Valère; Gabriel, Arome Junior; Taiwo, Akin; Johnson, Oliver; Adegun, Iyanu Pelumi; Babalola, O.T.

doi:10.1016/b978-0-323-90769-9.00039-6

Cited by 6 publications

(4 citation statements)

References 52 publications

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Section: Resultsmentioning

confidence: 99%

“…In addition, the claims of the models vary, as some of them have prognostic ambitions to some degree. Finally, it should be mentioned that the relevance of the single models to policy making varied from country to country during the pandemic [13]. The most common and also the most popular simulation models during the pandemic were the so-called compartmental models.…”

Section: Resultsmentioning

confidence: 99%

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Social and Ethical Implications of Digital Crisis Technologies: Case Study of Pandemic Simulation Models During the COVID-19 Pandemic

Bartl

2024

J Med Internet Res

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Background Responses to public health crises are increasingly technological in nature, as the prominence of COVID-19–related statistics and simulations amply demonstrates. However, the use of technologies is preconditional and has various implications. These implications can not only affect acceptance but also challenge the acceptability of these technologies with regard to the ethical and normative dimension. Objective This study focuses on pandemic simulation models as algorithmic governance tools that played a central role in political decision-making during the COVID-19 pandemic. To assess the social implications of pandemic simulation models, the premises of data collection, sorting, and evaluation must be disclosed and reflected upon. Consequently, the social construction principles of digital health technologies must be revealed and examined for their effects with regard to social, ethical, and ultimately political issues. Methods This case study starts with a systematization of different simulation approaches to create a typology of pandemic simulation models. On the basis of this, various properties, functions, and challenges of these simulation models are revealed and discussed in detail from a socioscientific point of view. Results The typology of pandemic simulation methods reveals the diversity of model-driven handling of pandemic threats. However, it is reasonable to assume that the use of simulation models could increasingly shift toward agent-based or artificial intelligence models in the future, thus promoting the logic of algorithmic decision-making in response to public health crises. As algorithmic decision-making focuses more on predicting future dynamics than statistical practices of assessing pandemic events, this study discusses this development in detail, resulting in an operationalized overview of the key social and ethical issues related to pandemic crisis technologies. Conclusions This study identifies 3 major recommendations for the future of pandemic crisis technologies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Social and Ethical Implications of Digital Crisis Technologies: Case Study of Pandemic Simulation Models During the COVID-19 Pandemic

Bartl

2024

J Med Internet Res

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“…The two most commonly used methods for this are equation-based modeling, which uses differential equations to describe population-level dynamics [6], and agent-based modeling [7], which simulates individual interactions [8]. A review of modeling papers found most models were compartmental epidemiological models [9], such as susceptible-infectious-recover models [10], or a modi ed version, susceptible-exposed-infected-recovered models, which focus on a human-to-human transmission pathway [11]. By establishing models that re ect the process, laws, and trends of infectious disease spread, this approach allows for a thorough analysis of dynamic characteristics, providing a solid foundation for understanding the causes and key factors of disease transmission and devising optimal prevention and control strategies [12].…”

Section: Introductionmentioning

confidence: 99%

“…While machine learning has been used to model COVID-19 [13], this approach is used far less frequently than mathematical modeling [9]. Machine learning can enhance traditional mathematical epidemic models by leveraging extensive datasets, such as epidemic, genetic, demographic, geospatial, and mobility data [14].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Insights into Regional Dynamics and Prevalence of COVID-19 Variants in US Health and Human Services Regions

Hu,

Zhang,

D’Souza

2024

Preprint

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Background The COVID-19 pandemic arising from the emergence of SARS-CoV-2 in late 2019 has led to global devastation with millions of lives lost by January 2024. Despite the WHO's declaration of the end of the global health emergency in May 2023, the virus persists, propelled by mutations. Variants continue to challenge vaccination efforts, underscoring the necessity for ongoing vigilance. This study aimed at contributing to a more data-driven approach to pandemic management by employing random forest regression to analyze regional variant prevalence. Methods This study utilized data from various sources including National COVID Cohort Collaborative database, Bureau of Transportation Statistics, World Weather Online, EPA, and US Census. Key variables include pollution, weather, travel patterns, and demographics. Preprocessing steps involved merging and normalization of datasets. Training data spanned from January 2021 to February 2023. The Random Forest Regressor was chosen for its accuracy in modeling. To prevent data leakage, time series splits were employed. Model performance was evaluated using metrics such as MSE and R-squared. Results The Alpha variant was predominant in the Southeast, with less than 80% share even at its peak. Delta surged initially in Kansas City and maintained dominance there for over 5 months. Omicron subvariant BA.5 spread nationwide, becoming predominant across all Health and Human Services regions simultaneously, with New York seeing the earliest and fastest decline in its share. Variant XBB.1.5 concentrated more in the Northeast, but limited data hindered full analysis. Using RF regressor, key features affecting spread patterns were identified, with high predictive accuracy. Each variant showed specific environmental correlations; for instance, Alpha with air quality index and temperature, Delta with ozone density, BA.5 with UV index, and XBB.1.5 with location, land area, and income. Correlation analysis further highlighted variant-specific associations. Conclusions This research provides a comprehensive analysis of the regional distribution of COVID-19 variants, offering critical insights for devising targeted public health strategies. By utilizing machine learning, the study uncovers the complex factors contributing to variant spread and reveals how specific factors contribute to variant prevalence, offering insights crucial for pandemic management.

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Cross fertilisation of Public Health and Translational Research

2022

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Public health is defined as the science of protecting the safety and improving the health of communities through education, policy-making and research for the prevention of disease (Gatseva and Argirova in J Public Health 19(3):205–6, 2011, 10.1007/s10389-011-0412-8; Winslow in Mod Med 2(1306):183–91, 1920. 10.1126/science.51.1306.23; What is public health. Centers for Disease Control Foundation. Centers for Disease Control, Atlanta, https://www.cdcfoundation.org/what-public-health ; What is the WHO definition of health? from the Preamble to the Constitution of WHO as adopted by the International Health Conference, New York, On 7 April 1948. The definition has not been amended since. 22 July 1946; signed by the representatives of 61 States (Official Records of WHO, no. 2, p. 100) and entered into force, 19 June;1948. https://web.archive.org/web/20190307113324/https:/www.who.int/about/who-we-are/frequently-asked-questions ). Translational research in healthcare is not only useful and satisfying for the researchers to bring their work to market but it would also support public health by bringing affordable, attainable and scalable solutions to the community at large. This is of high significance because instead of increasing the GDP spent in public health, we should focus on the increasing the translational research spending, as this would lead to improved solutions. Hence, the public health offering would reach a larger community at an improved cost. The COVID-19 pandemic and the huge number of lives it claimed exposes challenges in the public health. The pandemic has caused economic and social disruption to millions of people around the world, with many falling into extreme poverty. In early 2021, it was estimated nearly 690 million people are undernourished and by end of 2021 to increase further by 132 million (Joint statement by ILO, FAO, IFAD and WHO. Impact of COVID-19 on people's livelihoods, their health and our food systems https://www.who.int/news/item/13-10-2020-impact-of-covid-19-on-people's-livelihoods-their-health-and-our-food-systems ). The spending for public health has increased many folds during the pandemic and this is where translational research in healthcare can play a transformative role to reduce the burden on government healthcare budget (Covid-19 and its impact on Indian society. https://timesofindia.indiatimes.com/readersblog/covid-19-and-its-impact-on-india/covid-19-and-its-impact-on-indian-society-27565/ ). Over the past decade, public health research has started playing a major role in Indian academic settings. COVID-19 pandemic has further highlighted the role of public health. However, the potential of using technological advancement has not been fully utilised. This is where translational research and public health can play a role to tap the full potential of technology. This review paper explores the public health practices to understand the different practices to examine how both publ...

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Modeling and predicting the spread of COVID-19

Cited by 6 publications

References 52 publications

Social and Ethical Implications of Digital Crisis Technologies: Case Study of Pandemic Simulation Models During the COVID-19 Pandemic

Social and Ethical Implications of Digital Crisis Technologies: Case Study of Pandemic Simulation Models During the COVID-19 Pandemic

Machine Learning Insights into Regional Dynamics and Prevalence of COVID-19 Variants in US Health and Human Services Regions

Cross fertilisation of Public Health and Translational Research

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