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Understanding the Transmission Dynamics, Control Strategies, and Epidemiological Characteristics of the COVID-19 Pandemic

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (1 March 2024) | Viewed by 22614

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Dr. Salihu S. Musa

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Guest Editor

Department of Mathematics, University of Maryland, College Park, Maryland, 20742, USA
Interests: COVID-19 epidemiology; infectious diseases modelling; COVID-19 during pregnancy; SARS-CoV-2 vertical transmission; systematic review and meta-analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Daihai He

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Guest Editor

Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
Interests: COVID-19 pandemic; parameter estimation; infectious diseases; mathematical epidemiology; statistical inference and stochastic process

Dr. Shi Zhao

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Guest Editor

JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
Interests: infectious diseases epidemiology; biostatistics

Special Issue Information

Dear Colleagues,

In December 2019, public health officials in China alerted the World Health Organization about the emergence of pneumonia of unknown etiology. The disease, named COVID-19, spread rapidly globally and became a pandemic. COVID-19, which SARS-CoV-2 causes, has become the most dangerous global pandemic that has deployed a severe problem to humanity in the past decades. Many etiological features of SARS-CoV-2 are yet to be uncovered, ranging from epidemiological and clinical characteristics. Such as the impact of pharmaceutical and non-pharmaceutical intervention measures on the COVID-19 dynamics, the effects of SARS-CoV-2 on maternal and neonatal outcomes and breastfeeding, etc. Thus, global effort is required from researchers and public health to suppress the impact of the COVID-19 pandemic.

This special issue of the International Journal of Environmental Research and Public Health (IJERPH) focuses on research articles addressing key epidemiological problems related to COVID-19 to help contain the pandemic. These include (but are not limited to) articles under the general scope of infectious diseases, epidemiological modelling, virological characteristics, preventive and control, and public health. This special issue offers the opportunity to publish original full research articles, systematic reviews and meta-analyses, and short communications, which are expected to add appropriate information to the current knowledge.

Dr. Salihu S. Musa
Dr. Daihai He
Dr. Shi Zhao
Guest Editors

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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.

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Keywords

COVID-19
epidemiology
disease prevention and control
SAR-CoV-2 vertical transmission
breastfeeding and COVID-19
SARS-CoV-2 infection during pregnancy
vaccination
parameter estimation
infectious disease modelling

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Published Papers (9 papers)

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Research

13 pages, 881 KiB

Open AccessArticle

Effect of Vitamin D in Long COVID Patients

by Ramsen Ghasan Hikmet, Christian Wejse and Jane Agergaard

Int. J. Environ. Res. Public Health 2023, 20(22), 7058; https://doi.org/10.3390/ijerph20227058 - 13 Nov 2023

Cited by 3 | Viewed by 2491

Abstract

Vitamin D deficiency has been studied in the context of acute coronavirus disease 2019 (COVID-19), revealing associations with increased severity and mortality. Yet, the influence of vitamin D on long COVID symptoms remains unknown. The purpose of this study is to examine the effect of vitamin D on long COVID symptoms. Over the study period, 50,432 individuals within the catchment area of the outpatient COVID-19 clinic tested positive for SARS-CoV-2 via PCR, with 547 patients subsequently referred to a specialized Post-COVID Clinic, and 447 of them enrolled in the study. Patient-reported symptoms and paraclinical measures including vitamin D were evaluated in 442 patients. The majority of participants were female (72%, n = 320/442). The consumption of alcohol and number of current smokers were low. Low vitamin D was observed in 26% (n = 115/442) of the patients, most commonly in male participants (odds ratio (OR) = 1.77, 95% confidence interval (CI) (1.12, 2.79), p = 0.014). Additionally, low vitamin D was correlated with a younger mean age of 41 years (standard deviation (SD) = 12) as opposed to 48 years (SD = 13) in patients with normal vitamin D levels (OR = 0.96, 95% CI (0.94, 0.97), p < 0.001). While our study population indicated a potentially higher prevalence of vitamin D insufficiency in this population compared to the general population, no significant differences in prevalence of symptom or symptom severity scores were observed between the low and normal vitamin D groups. In patients in a Post-COVID Clinic, we found no association between vitamin D levels and long COVID symptoms. Full article

(This article belongs to the Special Issue Understanding the Transmission Dynamics, Control Strategies, and Epidemiological Characteristics of the COVID-19 Pandemic)

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14 pages, 1634 KiB

Open AccessArticle

The Effectiveness of Mobility Restrictions on Controlling the Spread of COVID-19 in a Resistant Population

by Dina Albassam, Mariam Nouh and Anette Hosoi

Int. J. Environ. Res. Public Health 2023, 20(7), 5343; https://doi.org/10.3390/ijerph20075343 - 31 Mar 2023

Cited by 3 | Viewed by 2141

Abstract

Human mobility plays an important role in the spread of COVID-19. Given this knowledge, countries implemented mobility-restricting policies. Concomitantly, as the pandemic progressed, population resistance to the virus increased via natural immunity and vaccination. We address the question: “What is the impact of mobility-restricting measures on a resistant population?” We consider two factors: different types of points of interest (POIs)—including transit stations, groceries and pharmacies, retail and recreation, workplaces, and parks—and the emergence of the Delta variant. We studied a group of 14 countries and estimated COVID-19 transmission based on the type of POI, the fraction of population resistance, and the presence of the Delta variant using a Pearson correlation between mobility and the growth rate of cases. We find that retail and recreation venues, transit stations, and workplaces are the POIs that benefit the most from mobility restrictions, mainly if the fraction of the population with resistance is below 25–30%. Groceries and pharmacies may benefit from mobility restrictions when the population resistance fraction is low, whereas in parks, there is little advantage to mobility-restricting measures. These results are consistent for both the original strain and the Delta variant; Omicron data were not included in this work. Full article

(This article belongs to the Special Issue Understanding the Transmission Dynamics, Control Strategies, and Epidemiological Characteristics of the COVID-19 Pandemic)

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16 pages, 3210 KiB

Open AccessArticle

Trajectories of Seroprevalence and Neutralizing Activity of Antibodies against SARS-CoV-2 in Southern Switzerland between July 2020 and July 2021: An Ongoing, Prospective Population-Based Cohort Study

by Rebecca Amati, Giovanni Piumatti, Giovanni Franscella, Peter Buttaroni, Anne-Linda Camerini, Laurie Corna, Sara Levati, Marta Fadda, Maddalena Fiordelli, Anna Maria Annoni, Kleona Bezani, Antonio Amendola, Cristina Fragoso Corti, Serena Sabatini, Marco Kaufmann, Anja Frei, Milo Alan Puhan, Luca Crivelli, Emiliano Albanese and on behalf of the Corona Immunitas Ticino Study Group

Int. J. Environ. Res. Public Health 2023, 20(4), 3703; https://doi.org/10.3390/ijerph20043703 - 19 Feb 2023

Cited by 4 | Viewed by 2215

Abstract

Objectives: The COVID-19 pandemic continues, and evidence on infection- and vaccine-induced immunity is key. We assessed COVID-19 immunity and the neutralizing antibody response to virus variants across age groups in the Swiss population. Study Design: We conducted a cohort study in representative community-dwelling residents aged five years or older in southern Switzerland (total population 353,343), and we collected blood samples in July 2020 (in adults only, N = 646), November–December 2020 (N = 1457), and June–July 2021 (N = 885). Methods: We used a previously validated Luminex assay to measure antibodies targeting the spike (S) and the nucleocapsid (N) proteins of the virus and a high-throughput cell-free neutralization assay optimized for multiple spike protein variants. We calculated seroprevalence with a Bayesian logistic regression model accounting for the population’s sociodemographic structure and the test performance, and we compared the neutralizing activity between vaccinated and convalescent participants across virus variants. Results: The overall seroprevalence was 7.8% (95% CI: 5.4–10.4) by July 2020 and 20.2% (16.4–24.4) by December 2020. By July 2021, the overall seroprevalence increased substantially to 72.5% (69.1–76.4), with the highest estimates of 95.6% (92.8–97.8) among older adults, who developed up to 10.3 more antibodies via vaccination than after infection compared to 3.7 times more in adults. The neutralizing activity was significantly higher for vaccine-induced than infection-induced antibodies for all virus variants (all p values < 0.037). Conclusions: Vaccination chiefly contributed to the reduction in immunonaive individuals, particularly those in older age groups. Our findings on the greater neutralizing activity of vaccine-induced antibodies than infection-induced antibodies are greatly informative for future vaccination campaigns. Full article

(This article belongs to the Special Issue Understanding the Transmission Dynamics, Control Strategies, and Epidemiological Characteristics of the COVID-19 Pandemic)

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25 pages, 2403 KiB

Open AccessArticle

Municipal Ethnic Composition and Disparities in COVID-19 Infections in New Jersey: A Blinder–Oaxaca Decomposition Analysis

by Yuqi Wang, Laurent Reyes, Emily A. Greenfield and Sarah R. Allred

Int. J. Environ. Res. Public Health 2022, 19(21), 13963; https://doi.org/10.3390/ijerph192113963 - 27 Oct 2022

Cited by 1 | Viewed by 2305

Abstract

COVID-19 has disproportionally impacted Latinx and Black communities in the US. Our study aimed to extend the understanding of ethnic disparities in COVID-19 case rates by using a unique dataset of municipal case rates across New Jersey (NJ) during the first 17 months of the pandemic. We examined the extent to which there were municipal-level ethnic disparities in COVID-19 infection rates during three distinct spikes in case rates over this period. Furthermore, we used the Blinder–Oaxaca decomposition analysis to identify municipal-level exposure and vulnerability factors that contributed to ethnic disparities and how the contributions of these factors changed across the three initial waves of infection. Two clear results emerged. First, in NJ, the COVID-19 infection risk disproportionally affected Latinx communities across all three waves during the first 17 months of the pandemic. Second, the exposure and vulnerability factors that most strongly contributed to higher rates of infection in Latinx and Black communities changed over time as the virus, alongside medical and societal responses to it, also changed. These findings suggest that understanding and addressing ethnicity-based COVID-19 disparities will require sustained attention to the systemic and structural factors that disproportionately place historically marginalized ethnic communities at greater risk of contracting COVID-19. Full article

(This article belongs to the Special Issue Understanding the Transmission Dynamics, Control Strategies, and Epidemiological Characteristics of the COVID-19 Pandemic)

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14 pages, 9360 KiB

Open AccessArticle

Effectiveness of COVID-19 Vaccination on Reduction of Hospitalizations and Deaths in Elderly Patients in Rio Grande do Norte, Brazil

by Ana Isabela L. Sales-Moioli, Leonardo J. Galvão-Lima, Talita K. B. Pinto, Pablo H. Cardoso, Rodrigo D. Silva, Felipe Fernandes, Ingridy M. P. Barbalho, Fernando L. O. Farias, Nicolas V. R. Veras, Gustavo F. Souza, Agnaldo S. Cruz, Ion G. M. Andrade, Lúcio Gama and Ricardo A. M. Valentim

Int. J. Environ. Res. Public Health 2022, 19(21), 13902; https://doi.org/10.3390/ijerph192113902 - 26 Oct 2022

Cited by 8 | Viewed by 3544

Abstract

Since the COVID-19 pandemic emerged, vaccination has been the core strategy to mitigate the spread of SARS-CoV-2 in humans. This paper analyzes the impact of COVID-19 vaccination on hospitalizations and deaths in the state of Rio Grande do Norte, Brazil. We analyzed data from 23,516 hospitalized COVID-19 patients diagnosed between April 2020 and August 2021. We excluded the data from patients hospitalized through direct occupancy, unknown outcomes, and unconfirmed COVID-19 cases, resulting in data from 12,635 patients cross-referenced with the immunization status during hospitalization. Our results indicated that administering at least one dose of the immunizers was sufficient to significantly reduce the occurrence of moderate and severe COVID-19 cases among patients under 59 years. Considering the partially or fully immunized patients, the mean age is similar between the analyzed groups, despite the occurrence of comorbidities and higher than that observed among not immunized patients. Thus, immunized patients present lower Unified Score for Prioritization (USP) levels when diagnosed with COVID-19. Our data suggest that COVID-19 vaccination significantly reduced the hospitalization and death of elderly patients (60+ years) after administration of at least one dose. Comorbidities do not change the mean age of moderate/severe COVID-19 cases and the days required for the hospitalization of these patients. Full article

(This article belongs to the Special Issue Understanding the Transmission Dynamics, Control Strategies, and Epidemiological Characteristics of the COVID-19 Pandemic)

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12 pages, 1902 KiB

Open AccessArticle

Changes in Social and Clinical Determinants of COVID-19 Outcomes Achieved by the Vaccination Program: A Nationwide Cohort Study

by Oliver Ibarrondo, Maíra Aguiar, Nico Stollenwerk, Rubén Blasco-Aguado, Igor Larrañaga, Joseba Bidaurrazaga, Carlo Delfin S. Estadilla and Javier Mar

Int. J. Environ. Res. Public Health 2022, 19(19), 12746; https://doi.org/10.3390/ijerph191912746 - 5 Oct 2022

Cited by 2 | Viewed by 1688

Abstract

Background: The objective of this study was to assess changes in social and clinical determinants of COVID-19 outcomes associated with the first year of COVID-19 vaccination rollout in the Basque population. Methods: A retrospective study was performed using the complete database of the Basque Health Service (n = 2,343,858). We analyzed data on age, sex, socioeconomic status, the Charlson comorbidity index (CCI), hospitalization and intensive care unit (ICU) admission, and COVID-19 infection by Cox regression models and Kaplan–Meier curves. Results: Women had a higher hazard ratio (HR) of infection (1.1) and a much lower rate of hospitalization (0.7). With older age, the risk of infection fell, but the risks of hospitalization and ICU admission increased. The higher the CCI, the higher the risks of infection and hospitalization. The risk of infection was higher in high-income individuals in all periods (HR = 1.2–1.4) while their risk of hospitalization was lower in the post-vaccination period (HR = 0.451). Conclusion: Despite the lifting of many control measures during the second half of 2021, restoring human mobility patterns, the situation could not be defined as syndemic, clinical determinants seeming to have more influence than social ones on COVID-19 outcomes, both before and after vaccination program implementation. Full article

(This article belongs to the Special Issue Understanding the Transmission Dynamics, Control Strategies, and Epidemiological Characteristics of the COVID-19 Pandemic)

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19 pages, 2819 KiB

Open AccessArticle

Agent Simulation Model of COVID-19 Epidemic Agent-Based on GIS: A Case Study of Huangpu District, Shanghai

by Tao Dong, Wen Dong and Quanli Xu

Int. J. Environ. Res. Public Health 2022, 19(16), 10242; https://doi.org/10.3390/ijerph191610242 - 18 Aug 2022

Cited by 7 | Viewed by 2124

Abstract

Since the COVID-19 outbreak was detected and reported at the end of 2019, the pandemic continues worldwide, with public health authorities and the general public in each country struggling to balance safety and normal travel activities. However, the complex public health environment and the complexity of human behaviors, as well as the constant mutation of the COVID-19 virus, requires the development of theoretical and simulation tools to accurately model all segments of society. In this paper, an agent-based model is proposed, the model constructs the real geographical environment of Shanghai Huangpu District based on the building statistics data of Shanghai Huangpu District, and the real population data of Shanghai Huangpu District based on the data of China’s seventh Population census in 2020. After incorporating the detailed elements of COVID-19 transmission and the real data of WHO, the model forms various impact parameters. Finally, the model was validated according to the COVID-19 data reported by the official, and the model is applied to a hypothetical scenario. Shanghai is one of the places hardest hit by the current outbreak, Huangpu District is the “heart, window and name card” of Shanghai, and its importance to Shanghai is self-evident. so we used one-to-one population modeling to simulate the spread of COVID-19 in Huangpu District of Shanghai, In addition to the conventional functions of crowd movement, detection and treatment, the model also takes into account the burden of nucleic acid detection on the model caused by diseases similar to COVID-19, such as seasonal cold. The model validation results show that we have constructed a COVID-19 epidemic agent risk assessment system suitable for the individual epidemiological characteristics of COVID-19 in China, which can adjust and reflect on the existing COVID-19 epidemic intervention strategies and individual health behaviors. To provide scientific theoretical basis and information decision-making tools for effective prevention and control of COVID-19 and public health intervention in China. Full article

(This article belongs to the Special Issue Understanding the Transmission Dynamics, Control Strategies, and Epidemiological Characteristics of the COVID-19 Pandemic)

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25 pages, 10504 KiB

Open AccessArticle

The Effect of Transborder Mobility on COVID-19 Incidences in Belgium

by Febe Brackx, Fien Vanongeval, Yessika Adelwin Natalia, Geert Molenberghs and Thérèse Steenberghen

Int. J. Environ. Res. Public Health 2022, 19(16), 9968; https://doi.org/10.3390/ijerph19169968 - 12 Aug 2022

Cited by 2 | Viewed by 1624

Abstract

Belgium is a geographically small country bordered by The Netherlands, France, Germany, and Luxembourg, with intense transborder mobility, defined as mobility in the border regions with neighboring countries. It is therefore of interest to examine how the 14-day COVID-19 confirmed case incidence in the border regions is influenced by that of the adjacent regions in the neighboring countries and thus, whether and how it differs from that in the adjacent non-border regions within Belgium. To this end, the 14-day COVID-19 confirmed case incidence is studied at the level of Belgian provinces, well-defined border areas within Belgium, and adjacent regions in the neighboring countries. Auxiliary information encompasses work-related border traffic, travel rates, the proportion of people with a different nationality, the stringency index of the non-pharmaceutical interventions, and the degree of urbanization at the level of the municipality. Especially in transnational urbanized areas such as between the Belgian and Dutch provinces of Limburg and between the Belgian province of Antwerp and the Dutch province of North Brabant, the impact on incidence is visible, at least at some points in time, especially when the national incidences differ between neighboring countries. In contrast, the intra-Belgian language border regions show very little transborder impact on the incidence curves, except around the Brussels capital region, leading to various periods where the incidences are very different in the Dutch-speaking north and the French-speaking south of Belgium. Our findings suggest that while travel restrictions may be needed at some points during a pandemic, a more fine-grained approach than merely closing national borders may be considered. At the same time, in border regions with considerable transborder mobility, it is recommended to coordinate the non-pharmaceutical interventions between the authorities of the various countries overlapping with the border region. While this seems logical, there are clear counterexamples, e.g., where non-essential shops, restaurants, and bars are closed in one country but not in the neighboring country. Full article

(This article belongs to the Special Issue Understanding the Transmission Dynamics, Control Strategies, and Epidemiological Characteristics of the COVID-19 Pandemic)

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22 pages, 80635 KiB

Open AccessArticle

Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

by Abdelgader Alamrouni, Fidan Aslanova, Sagiru Mati, Hamza Sabo Maccido, Afaf. A. Jibril, A. G. Usman and S. I. Abba

Int. J. Environ. Res. Public Health 2022, 19(2), 738; https://doi.org/10.3390/ijerph19020738 - 10 Jan 2022

Cited by 25 | Viewed by 2709

Abstract

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK. Full article

(This article belongs to the Special Issue Understanding the Transmission Dynamics, Control Strategies, and Epidemiological Characteristics of the COVID-19 Pandemic)

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