Predicting the Size and Duration of the COVID-19 Pandemic

Lewis, Ted G.

doi:10.3389/fams.2020.611854

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Although there is no method yet developed to show accurate results, the SML method can suggest a solution to bring an outbreak under control, which can help us to act beforehand. One good part of ML is learning the ability to predict the magnitude of an epidemic such as COVID-19 [ 43 ]. The SML algorithm helps in imaging or predicting the speed of disease to different sectors by analyzing various structured, semi-structured, and unstructured data from multiple open source and social network data.…”

Section: Growing Research Areasmentioning

confidence: 99%

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Roy¹,

Meena²,

Lim

2022

Diagnostics

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The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.

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Section: Growing Research Areasmentioning

confidence: 99%

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Roy¹,

Meena²,

Lim

2022

Diagnostics

View full text Add to dashboard Cite

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“…Coronavirus disease 2019 non-pharmaceutical policy responses have largely depended on epidemiological parameters of the pandemic estimated from mathematical and statistical COVID-19 modeling.The models have included epidemiological growth models ( 5 – 8 ), and the Susceptible, Infected, and Recovered (SIR) type models ( 9 – 15 ). In other epidemiological, healthcare, and surveillance indicators, the estimates have provided relevant policymakers with scientifically driven strategies for appropriately imposing and lifting COVID-19 related restrictions.…”

Section: Introductionmentioning

confidence: 99%

Inter-Country COVID-19 Contagiousness Variation in Eight African Countries

Singini

Manda

2022

Front. Public Health

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The estimates of contiguousness parameters of an epidemic have been used for health-related policy and control measures such as non-pharmaceutical control interventions (NPIs). The estimates have varied by demographics, epidemic phase, and geographical region. Our aim was to estimate four contagiousness parameters: basic reproduction number (R0), contact rate, removal rate, and infectious period of coronavirus disease 2019 (COVID-19) among eight African countries, namely Angola, Botswana, Egypt, Ethiopia, Malawi, Nigeria, South Africa, and Tunisia using Susceptible, Infectious, or Recovered (SIR) epidemic models for the period 1 January 2020 to 31 December 2021. For reference, we also estimated these parameters for three of COVID-19's most severely affected countries: Brazil, India, and the USA. The basic reproduction number, contact and remove rates, and infectious period ranged from 1.11 to 1.59, 0.53 to 1.0, 0.39 to 0.81; and 1.23 to 2.59 for the eight African countries. For the USA, Brazil, and India these were 1.94, 0.66, 0.34, and 2.94; 1.62, 0.62, 0.38, and 2.62, and 1.55, 0.61, 0.39, and 2.55, respectively. The average COVID-19 related case fatality rate for 8 African countries in this study was estimated to be 2.86%. Contact and removal rates among an affected African population were positively and significantly associated with COVID-19 related deaths (p-value < 0.003). The larger than one estimates of the basic reproductive number in the studies of African countries indicate that COVID-19 was still being transmitted exponentially by the 31 December 2021, though at different rates. The spread was even higher for the three countries with substantial COVID-19 outbreaks. The lower removal rates in the USA, Brazil, and India could be indicative of lower death rates (a proxy for good health systems). Our findings of variation in the estimate of COVID-19 contagiousness parameters imply that countries in the region may implement differential COVID-19 containment measures.

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A Multi-factor Model of COVID-19 Epidemic in California

Lewis

2023

Preprint

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We describe a multi-factor model of the spread of COVID-19 across the 58 counties of California from March 2020 to June 2023. The model provides estimates of cumulative cases and duration of the epidemic versus 5 independent variables. The independent variables are the following correlated factors: population, population density, family income, Gini coefficient, and land area (size) of each county. The correlation coefficients of these factors are used to reduce the error in models of cumulative cases and duration. The model produces linear equations – one for cumulative cases and the other for duration of infection. The cumulative case estimate is highly correlated with population, but the estimate is improved by considering all 5 factors. The duration of infection1 estimate is improved by considering population and income level. We also find that infection rate (per capita cases) varies highly and roughly obeys a normal distribution, suggesting randomness, rather than correlation with one or more of the 5 factors. We also observe the vast differences between high and low cumulative cases across the 58 counties. Using the same model but with different values of correlation of the same factors, we obtain very accurate models of both bottom-half and top-half counties. i.e., counties with 49,000 or fewer cumulative cases versus counties with 50,000 – 3.5 million cases. This suggests that multi-factor models are a suitable alternative to traditional diffusion models especially if an analysis of causal factors is preferred versus estimates of cumulative cases versus time. An attempt to predict the cumulative cases at the end of the pandemic (1191 days) at one- and two-year intervals reveals the challenge of prediction. The model improves as more data is accumulated but lacks predictive power. We speculate that the model might be extended by noting the convergence rate of successive approximations and then extrapolating. This was left as future work.

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Predicting the Size and Duration of the COVID-19 Pandemic

Cited by 3 publications

References 15 publications

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Inter-Country COVID-19 Contagiousness Variation in Eight African Countries

A Multi-factor Model of COVID-19 Epidemic in California

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