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End of the COVID-19 Era: Models, Predictions and Projections

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A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Coronaviruses (CoV) and COVID-19 Pandemic".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 12944

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Prof. Dr. Marco Roccetti

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Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
Interests: artificial intelligence; health care; human–computer interaction; machine learning; social networking (online); causality; medical computing; medical diagnostic computing; medical information systems

Special Issue Information

Dear Colleagues,

After two winters of COVID-19, in September 2022, an increase in cases has been recognized in data from the main countries in Western Europe, including the UK, Germany, France and Italy. With the number of cases already rising, the question in Europe, the US and the Western world is if this wave is likely to be better than the last wave or if these regions should prepare for another bad winter.

The situation in Africa or Asia is similar. For example, in recent days, Indonesia has recorded some of the highest numbers of COVID-19 cases in Southeast Asia, especially in young people aged 18 or younger. In Singapore, the reinfection rate is climbing due to a new Omicron sub-variant which is increasingly causing most of the daily new infections.

In general, worldwide, the waning immunity, increased vaccine hesitancy, decline in testing, masking fatigue and the return of children, students and workers to school, universities and jobs are all factors which, depending on the specific region, could increase infection rates, leading many citizens feeling like they are trapped in a never-ending loop.

The role played by new variants is also relevant. While Omicron has widely dominated in Europe and the US since the winter of 2021, also causing an unexpected summer wave due to its BA.5 sub-variant, new variants are growing fast, such as BA4.6, BF.7, BA.2.75.2 and BQ.1.1, for example.

At the moment, we have no clear indications of the effect that these sub-variants will have on the disease severity. For example, we still do not know whether the number of COVID-19-related deaths will remain relatively low, based on the consideration that the high population immunity achieved with vaccination and past infections will cause the fatality rate to continue to decline, stabilizing in a predictable, endemic state, or whether new deaths will occur due to a new infection wave. Obviously, we are not only concerned with deaths, because even a wave of medium intensity could have the potential to put massive additional pressure on health services, if coupled with other seasonal respiratory viruses.

For these reasons, we introduce this Special Issue, the purpose of which is to highlight problems and solutions related to the analysis of this multifaceted situation, predicting its outcomes and responding to the question posed in the title. Needless to say, we are looking for approaches based on scientific methods, that have the right balance between observation, logical reasoning and mathematical modelling. Attention will be primarily paid to papers employing an experimental design and making use of data and statistics, aiming to achieve the construction and use of models suitable for predictions and projections on the future developments of the COVID-19 pandemic and its impact on health services.

Prof. Dr. Marco Roccetti
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

COVID-19 pandemic: current and future state analysis
models for analysis and predictions
mathematical, computational, cognitive modelling for COVID-19
COVID-19: data and surveillance
epidemiology and infodemiology data for COVID-19
computational epidemiology
bioinformatics and immunoinformatics for COVID-19
AI for COVID-19
simulation for COVID-19

Published Papers (10 papers)

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Research

14 pages, 1115 KiB

Open AccessArticle

Individualized Prediction of SARS-CoV-2 Infection in Mexico City Municipality during the First Six Waves of the Pandemic

by Mariel Victorino-Aguilar, Abel Lerma, Humberto Badillo-Alonso, Víctor Manuel Ramos-Lojero, Luis Israel Ledesma-Amaya, Silvia Ruiz-Velasco Acosta and Claudia Lerma

Healthcare 2024, 12(7), 764; https://doi.org/10.3390/healthcare12070764 - 31 Mar 2024

Viewed by 613

Abstract

After COVID-19 emerged, alternative methods to laboratory tests for the individualized prediction of SARS-CoV-2 were developed in several world regions. The objective of this investigation was to develop models for the individualized prediction of SARS-CoV-2 infection in a large municipality of Mexico. The study included data from 36,949 patients with suspected SARS-CoV-2 infection who received a diagnostic tested at health centers of the Alvaro Obregon Jurisdiction in Mexico City registered in the Epidemiological Surveillance System for Viral Respiratory Diseases (SISVER-SINAVE). The variables that were different between a positive test and a negative test were used to generate multivariate binary logistic regression models. There was a large variation in the prediction variables for the models of different pandemic waves. The models obtained an overall accuracy of 73% (63–82%), sensitivity of 52% (18–71%), and specificity of 84% (71–92%). In conclusion, the individualized prediction models of a positive COVID-19 test based on SISVER-SINAVE data had good performance. The large variation in the prediction variables for the models of different pandemic waves highlights the continuous change in the factors that influence the spread of COVID-19. These prediction models could be applied in early case identification strategies, especially in vulnerable populations. Full article

(This article belongs to the Special Issue End of the COVID-19 Era: Models, Predictions and Projections)

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11 pages, 771 KiB

Open AccessArticle

A Low-Cost Early Warning Method for Infectious Diseases with Asymptomatic Carriers

by Mauro Gaspari

Healthcare 2024, 12(4), 469; https://doi.org/10.3390/healthcare12040469 - 13 Feb 2024

Viewed by 632

Abstract

At the beginning of 2023, the Italian former prime minister, the former health minister and 17 others including the current president of the Lombardy region were placed under investigation on suspicion of aggravated culpable epidemic in connection with the government’s response at the start of the COVID-19 pandemic. The charges revolve around the failure by authorities to take adequate measures to prevent the spread of the virus in the Bergamo area, which experienced a significant excess of deaths during the initial outbreak. The aim of this paper is to analyse the pandemic data of Italy and the Lombardy region in the first 10 days of the pandemic, spanning from the 24th of February 2020 to the 4th of March 2020. The objective is to determine whether the use of early warning indicators could have facilitated the identification of a critical increase in infections. This identification, in turn, would have enabled the timely formulation of strategies for pandemic containment, thereby reducing the number of deaths. In conclusion, to translate our findings into practical guidelines, we propose a low-cost early warning method for infectious respiratory diseases with asymptomatic carriers. Full article

(This article belongs to the Special Issue End of the COVID-19 Era: Models, Predictions and Projections)

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12 pages, 922 KiB

Open AccessArticle

Hand Trauma and Reconstructive Microsurgery during the COVID-19 Emergency in the Marche Region (Italy): What Has Changed?

by Francesco De Francesco, Massimo Berdini, Pasquale Gravina, Pier Paolo Pangrazi, Giuseppe Signoriello and Michele Riccio

Healthcare 2023, 11(23), 3006; https://doi.org/10.3390/healthcare11233006 - 21 Nov 2023

Viewed by 643

Abstract

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing COVID-19, has spread across the globe. To limit the spread of COVID-19, the Italian government imposed various restrictions (lockdowns). These restrictions had an impact on the flow of patients accessing hospital care. Our aim in this study was to analyze the impact of lockdowns on the epidemiology of patients suffering from hand trauma. Our work analyzed the variation in the number and characteristics of hand trauma patients during the lockdown and half-lockdowns in 2020 compared to the same periods in the previous and subsequent years. In 2020, during the lockdown period, 107 patients were treated by our department for hand trauma, amounting to a 2% increase compared to the average number of patients treated in the pre-pandemic period. In 2020, during the half-lockdown period, 158 patients were treated, amounting to a 6.8% increase in comparison to the pre-pandemic period. During the lockdown period in Italy, the flow of patients suffering from hand trauma referred to our hub center remained stable. Given the restrictions imposed by the lockdown, we expected a consequent reduction in the number of work-related injuries, which did occur, while there was a surprising increase in the number of traffic-related injuries. The number of domestic accidents remained stable. Full article

(This article belongs to the Special Issue End of the COVID-19 Era: Models, Predictions and Projections)

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10 pages, 1616 KiB

Open AccessArticle

Modified Early Warning Score: Clinical Deterioration of Mexican Patients Hospitalized with COVID-19 and Chronic Disease

by Nicolás Santiago González, María de Lourdes García-Hernández, Patricia Cruz-Bello, Lorena Chaparro-Díaz, María de Lourdes Rico-González and Yolanda Hernández-Ortega

Healthcare 2023, 11(19), 2654; https://doi.org/10.3390/healthcare11192654 - 29 Sep 2023

Viewed by 1277

Abstract

The objective was to evaluate the Modified Early Warning Score in patients hospitalized for COVID-19 plus chronic disease. Methods: Retrospective observational study, 430 hospitalized patients with COVID-19 and chronic disease. Instrument, Modified Early Warning Score (MEWS). Data analysis, with Cox and logistic regression, to predict survival and risk. Results: Of 430 patients, 58.6% survived, and 41.4% did not. The risk was: low 53.5%, medium 23.7%, and high 22.8%. The MEWS score was similar between survivors 3.02, p 0.373 (95% CI: −0.225–0.597) and non-survivors 3.20 (95% CI: −0.224–0.597). There is a linear relationship between MEWS and mortality risk R 0.920, ANOVA 0.000, constant 4.713, and coefficient 4.406. The Cox Regression p 0.011, with a risk of deterioration of 0.325, with a positive coefficient, the higher the risk, the higher the mortality, while the invasive mechanical ventilation coefficient was negative −0.757. By providing oxygen and ventilation, mortality is lower. Conclusions: The predictive value of the modified early warning score in patients hospitalized for COVID-19 and chronic disease is not predictive with the MEWS scale. Additional assessment is required to prevent complications, especially when patients are assessed as low-risk. Full article

(This article belongs to the Special Issue End of the COVID-19 Era: Models, Predictions and Projections)

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14 pages, 606 KiB

Open AccessArticle

Preparing Cities for Future Pandemics: Unraveling the Influence of Urban and Housing Variables on COVID-19 Incidence in Santiago de Chile

by Katherina Kuschel, Raúl Carrasco, Byron J. Idrovo-Aguirre, Claudia Duran and Javier E. Contreras-Reyes

Healthcare 2023, 11(16), 2259; https://doi.org/10.3390/healthcare11162259 - 11 Aug 2023

Cited by 2 | Viewed by 1065

Abstract

In this study, we analyzed how urban, housing, and socioeconomic variables are related to COVID-19 incidence. As such, we have analyzed these variables along with demographic, education, employment, and COVID-19 data from 32 communes in Santiago de Chile between March and August of 2020, before the release of the vaccines. The results of our Principal Component Analysis (PCA) confirmed that those communes with more economic, social, organizational, and infrastructural resources were overall less affected by COVID-19. As the dimensions affecting COVID-19 are based on structural variables, this study discusses to what extent our cities can be prepared for the next pandemic. Recommendations for local decision-makers in controlling illegal immigration and investing in housing and urban parks are drawn. Full article

(This article belongs to the Special Issue End of the COVID-19 Era: Models, Predictions and Projections)

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14 pages, 2323 KiB

Open AccessArticle

A Multi-SCALE Community Network-Based SEIQR Model to Evaluate the Dynamic NPIs of COVID-19

by Cheng-Chieh Liu, Shengjie Zhao and Hao Deng

Healthcare 2023, 11(10), 1467; https://doi.org/10.3390/healthcare11101467 - 18 May 2023

Cited by 1 | Viewed by 1021

Abstract

Regarding the problem of epidemic outbreak prevention and control, infectious disease dynamics models cannot support urban managers in reducing urban-scale healthcare costs through community-scale control measures, as they usually have difficulty meeting the requirements for simulation at different scales. In this paper, we [...] Read more.

R_{t}

through the number of cases at the beginning of the outbreak, and evaluate the effectiveness of dynamic non-pharmaceutical interventions (NPIs) adopted at different time periods in Shanghai using our proposed approach. In particular, our proposed contact network is a three-layer multi-scale network that is used to distinguish social interactions occurring in areas of different sizes, as well as to distinguish between intensive and non-intensive population contacts. This susceptible–exposure–infection–quarantine–recovery (SEIQR) epidemic model constructed based on a multi-scale network can more effectively assess the feasibility of small-scale control measures, such as assessing community quarantine measures and mobility restrictions at different moments and phases of an epidemic. Our experimental results show that this model can meet the simulation needs at different scales, and our further discussion and analysis show that the spread of the epidemic in Shanghai from March to July 2022 can be successfully controlled by implementing a strict long-term dynamic NPI strategy. Full article

(This article belongs to the Special Issue End of the COVID-19 Era: Models, Predictions and Projections)

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7 pages, 228 KiB

Open AccessArticle

Correlations between SARS-CoV-2 Infections and the Number of COVID-19 Vaccine Doses Administered in Three Italian Provinces

by Alberto Modenese

Healthcare 2023, 11(3), 358; https://doi.org/10.3390/healthcare11030358 - 27 Jan 2023

Cited by 1 | Viewed by 945

Abstract

The aim of this ecological study is to evaluate correlations between the number of COVID-19 vaccine doses administered in three Italian provinces—one in the south, one in the center and one in the north of the country—and the registered numbers of COVID-19 cases in the same areas. The period of January 2021–September 2022 was considered, with specific analysis for fractions of times corresponding to the spread in Italy of the different SARS-CoV-2 variants. The results confirm the reduction of the effectiveness of the vaccines in preventing new COVID-19 cases in Italy, regardless of latitude, after the appearance of the first omicron variants. The new variants omicron 4 and 5 showed an extremely high spread during the Italian summer months; fortunately, the effects of the vaccinations in preventing new cases was improved compared to the previous omicron variants, showing a negative correlation between the new COVID-19 cases and the number of vaccine doses administered. Full article

(This article belongs to the Special Issue End of the COVID-19 Era: Models, Predictions and Projections)

21 pages, 3538 KiB

Open AccessArticle

Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach

by Ateekh Ur Rehman, Syed Hammad Mian, Yusuf Siraj Usmani, Mustufa Haider Abidi and Muneer Khan Mohammed

Healthcare 2023, 11(2), 260; https://doi.org/10.3390/healthcare11020260 - 13 Jan 2023

Cited by 2 | Viewed by 2594

Abstract

In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19’s exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible–exposed–infected–recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia. Full article

(This article belongs to the Special Issue End of the COVID-19 Era: Models, Predictions and Projections)

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12 pages, 2123 KiB

Open AccessArticle

COVID-19 Risk Compensation? Examining Vaccination Uptake among Recovered and Classification of Breakthrough Cases

by Arielle Kaim, Gal Zeevy and Mor Saban

Healthcare 2023, 11(1), 58; https://doi.org/10.3390/healthcare11010058 - 26 Dec 2022

Cited by 2 | Viewed by 1843

Abstract

The study has two primary aims: the first is to examine the uptake of COVID-19 vaccination patterns among those previously infected, and the second is an evaluation of the period elapsed between the patient’s latest dose of the vaccine and the infection itself by demographic group. A retrospective study was conducted from 1 March 2020, to 31 May 2022, in Israel. The study found that among Israelis, vaccination uptake following infection is relatively low. When examining gender, one sees that the immunization rate among recovering females is higher than among men. Similarly, differences in uptake exist between age groups. When examining the interval between vaccine dose and infection according to age groups, the most significant breakthrough infection rate is among the ages of 20–59 (1–6 days—0.3%; 7–13 days—0.48%; two to three weeks—0.3%, p < 0.001). This study reveals potential reservoir groups of virus spread. Among previously infected, low vaccination uptake levels are observed (first dose—30–40%, second dose—16–27%, third dose—9% and fourth dose—2%, p < 0.001), despite findings that indicate surging reinfection rates. Among vaccinated, two critical groups (0–19; 20–59) exhibit highest levels of breakthrough cases varying per vaccine doses, with statistically significant findings (p < 0.001). These population groups may be subject to a false sense of security as a result of perceived acquired long-term immunity prompting low perceived risk of the virus and non-vigilance with protective behavior. The findings point to the possibility that individuals engage in more risky health behavior, per the Peltzman effect. Full article

(This article belongs to the Special Issue End of the COVID-19 Era: Models, Predictions and Projections)

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11 pages, 1711 KiB

Open AccessArticle

Women Are More Infected and Seek Care Faster but Are Less Severely Ill: Gender Gaps in COVID-19 Morbidity and Mortality during Two Years of a Pandemic in Israel

by Arielle Kaim, Shani Ben Shetrit and Mor Saban

Healthcare 2022, 10(12), 2355; https://doi.org/10.3390/healthcare10122355 - 23 Nov 2022

Cited by 2 | Viewed by 1172

Abstract

In the context of COVID-19 outcomes, global data have deduced a gender bias towards severe disease among males. The aim is to compare morbidity and mortality during two years of the COVID-19 pandemic in female and male patients with COVID-19, as well as to assess length of stay, time of health-seeking behavior after positive diagnosis, and vaccination differences. A retrospective-archive study was conducted in Israel from 1 March 2020 to 1 March 2022 (two consecutive years). Data were obtained from the Israeli Ministry of Health’s (MOH) open COVID-19 database. The findings indicate female infections are 1.12 times more likely, across almost all age groups, apart from the youngest (0–19) age groups. Despite this, the relative risk of severe illness, intubation and mortality is higher among men. In addition, our findings indicate that the mean number of days taken by unvaccinated men from positive diagnosis to hospital admission was greater than among unvaccinated women among the deceased population. The findings of this study reveal lessons learned from the COVID-19 global pandemic. Specifically, the study shows how human biological sex may have played a role in COVID-19 transmission, illness, and death in Israel. The conclusions of this study indicate that targeted approaches, which take into consideration sex and gender and the intersecting factors are necessary to engage in the fight against COVID-19 and ensure the most effective and equitable pandemic response. Full article

(This article belongs to the Special Issue End of the COVID-19 Era: Models, Predictions and Projections)

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