Dynamics Of Pandemic Research Articles

Different Approaches to the Modelling of COVID-19

In this paper some innovative aspects of the mathematical modelling of classic epidemiology problems for the study of models related to the COVID-19 pandemic dynamics are presented. In addition, they are compared to real-world data using numerical methods in order to approximate the solutions. One of these models includes a non-transmitting compartment and another one, a delay-differential equation in the SIR-type method. Finally, a comparative discussion of the results is also presented.

COVID-19 - Modeling and Parameter Estimation for Brazil and Portugal: How predictable is the future?

This work addresses the COVID-19 pandemic on two fronts: proposing a system of ordinary differential equations to model it and fitting this model to Brazilian and Portuguese data. It presents estimations to important parameters for the infection dynamics, such as the percentage of asymptomatic individuals, and it stresses out that non-biological human aspects, for example, cultural, social, and economic, are not only impacted by the pandemic but also impact the pandemic dynamics itself. We state that, despite significant variations in the parameters, due to those human elements present in the contemporary pandemic, and despite the strong nonlinearities of the problem, wise human intervention is possible and able to minimize human losses. We show that the mortality rate does not behave as one would expect for a biological problem, independent of cultural aspects, and we also point to possible dates for the peaks of infection in both countries depending on the control of the transmissibility.

1.B. Round table: Biological, socioeconomic, legal and environmental health determinants in the COVID-19 pandemic

COVID-19 represents a concrete global health threat that has resulted in exacerbated inequalities, threatening progress towards SDG3, and particularly SDG 3.8, i.e. UHC - leave no one behind. Underlying differences in terms of health and social care system organisation, service provision models and pre-existing epidemiological context have resulted in heterogeneous scenarios for burden of disease (BoD) distribution, both in terms of COVID-19-specific outcomes, and because of collateral damage, not only across, but also within countries and regions. Individual proximal factors such as age, gender, ethnicity, socioeconomic position, underlying comorbidities and perception of risk have contributed to increased individual risk of acquiring the infection and of developing severe disease complications, including death. In addition, systemic issues, i.e., individual financial capacity, legal status, and acute versus chronic care needs have affected access both to care and commodity. From the global health perspective, we are also witnessing determinants threatening sustainable development, with tremendous differences in terms of capacity to combat the disease and deploy disease control measures, as well as ensure financial stability, economic growth and societal resilience. Ensuring equitable allocation of test, vaccines and medicines, and the development of comprehensive strategies necessitates a deeper understanding of those at most at risk, with due protection to those most vulnerable across the world. A comprehensive approach to tackle all these challenges, whilst ensuring due consideration is paid to ethical and legal issues, necessitates sound understanding of determinants, vulnerability with systematic measurement and interdisciplinary, cross-sectoral collaboration. This RT will bring the perspective of academia, researchers and practitioners in the field, and NGOs, offering insights on emerging research, practice and strategies. It aims to broker dialogue, to identify key steps to counter the damaging effect of inequities to social cohesion and system resilience, and to inform policy making and global public health actions.Key messages Biological, socioeconomic, legal and geographical determinants are at interplay, affecting the COVID-19 pandemic dynamics both in terms of immediate and delayed health outcomes.Assessing and weighing the links of various determinants and COVID-19 is essential to inform current and future policy decisions towards attaining UHC and to ensure an equitable, resilient society.

Stochastic model of the transmission dynamics of COVID-19 pandemic

In this paper, we formulate an SVITR deterministic model and extend it to a stochastic model by introducing intensity of stochastic factors and Brownian motion. Our basic qualitative analysis of both models includes the positivity of the solution, invariant region, disease-free equilibrium point, basic reproduction number, local and global stability of disease-free equilibrium point, endemic equilibrium point, and sensitivity. We obtain the stochastic reproduction number and local stability by using twice differentiable Itô’s formula. We prove the global stability of the disease-free equilibrium point by using a Lyapunov function. We determine the sensitivity of the effect of each parameter on basic reproduction number of the model by using a normalized sensitivity index formula. On the other hand, we demonstrate numerical simulation results of deterministic and stochastic models of COVID-19 by using Maple 18 and MATLAB software. Our simulation results indicate that reducing the contact between infected and susceptible individuals and improvement of treatment play a vital role in COVID-19 pandemic control.

A Data Driven Analysis and Forecast of COVID-19 Dynamics during the Third Wave Using SIRD Model in Bangladesh

In this study, we developed a compartmental SIRD model to analyze and forecast the transmission dynamics of the COVID-19 pandemic in Bangladesh during the third wave caused by the Indian delta variant. With the help of the nonlinear system of differential equations, this model can analyze the trends and provide reliable predictions regarding how the epidemic would evolve. The basic reproduction number regarding the pandemic has been determined analytically. The parameters used in this model have been estimated by fitting our model to the reported data for the months of May, June, and July 2021 and the goodness of fit of the parameter’s value has been found by the respective regression coefficients. Further, we conducted a sensitivity analysis of the basic reproduction number and observed that decreasing the transmission rate is the most significant factor in disease prevention. Our proposed model’s appropriateness for the available COVID-19 data in Bangladesh has been demonstrated through numerical simulations. According to the numerical simulation, it is evident that a rise in the transmission rate leads to a significant increase in the infected number of the population. Numerical simulations have also been performed by using our proposed model to forecast the future transmission dynamics for COVID-19 over a longer period of time. Knowledge of these forecasts may help the government in adopting appropriate measures to prepare for unforeseen situations that may arise in Bangladesh as well as to minimize detrimental impacts during the outbreak.

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

BackgroundMany countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic.ObjectiveIn this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic.MethodsUsing a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19.ResultsData were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain.ConclusionsPatients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

Exploring Progress with Supply Chain Risk Management during the First Year of the COVID-19 Pandemic

Background: In response to calls for actionable research that considers ongoing pandemic risk dynamics, we explore how risks experienced and risk mitigation techniques used have changed during the first year of the pandemic. Methods: We used a survey and studied six cases; data were collected both at the start of the pandemic and one year into the pandemic. This paper offers the first empirical exploration of the first full year of the pandemic and provides data points from both early and one year into the pandemic. Results: Our findings indicate that not only are pandemic risks far from mitigated, several types of risks have also increased in severity. Multifaceted and multidirectional approaches have been adopted, going well beyond demand and supply risks (the risks most widely considered in the literature) and much more work remains for supply chain managers to mitigate risks and improve supply chain resilience. Conclusions: We find that in addition to the risk management techniques, considering behavioral aspects is key for navigating a pathway towards risk mitigation.

Associations Between the Perceived Severity of the COVID-19 Pandemic, Cyberchondria, Depression, Anxiety, Stress, and Lockdown Experience: Cross-sectional Survey Study.

BackgroundThe outbreak of the COVID-19 pandemic has caused great panic among the public, with many people suffering from adverse stress reactions. To control the spread of the pandemic, governments in many countries have imposed lockdown policies. In this unique pandemic context, people can obtain information about pandemic dynamics on the internet. However, searching for health-related information on the internet frequently increases the possibility of individuals being troubled by the information that they find, and consequently, experiencing symptoms of cyberchondria.ObjectiveWe aimed to examine the relationships between people’s perceived severity of the COVID-19 pandemic and their depression, anxiety, and stress to explore the role of cyberchondria, which, in these relationship mechanisms, is closely related to using the internet. In addition, we also examined the moderating role of lockdown experiences.MethodsIn February 2020, a total of 486 participants were recruited through a web-based platform from areas in China with a large number of infections. We used questionnaires to measure participants’ perceived severity of the COVID-19 pandemic, to measure the severity of their cyberchondria, depression, anxiety, and stress symptoms, and to assess their lockdown experiences. Confirmatory factor analysis, exploratory factor analysis, common method bias, descriptive statistical analysis, and correlation analysis were performed, and moderated mediation models were examined.ResultsThere was a positive association between perceived severity of the COVID-19 pandemic and depression (β=0.36, t=8.51, P<.001), anxiety (β=0.41, t=9.84, P<.001), and stress (β=0.46, t=11.45, P<.001), which were mediated by cyberchondria (β=0.36, t=8.59, P<.001). The direct effects of perceived severity of the COVID-19 pandemic on anxiety (β=0.07, t=2.01, P=.045) and stress (β=0.09, t=2.75, P=.006) and the indirect effects of cyberchondria on depression (β=0.10, t=2.59, P=.009) and anxiety (β=0.10, t=2.50, P=.01) were moderated by lockdown experience.ConclusionsThe higher the perceived severity of the COVID-19 pandemic, the more serious individuals’ symptoms of depression, anxiety, and stress. In addition, the associations were partially mediated by cyberchondria. Individuals with higher perceived severity of the COVID-19 pandemic were more likely to develop cyberchondria, which aggravated individuals’ depression, anxiety, and stress symptoms. Negative lockdown experiences exacerbated the COVID-19 pandemic’s impact on mental health.

Infection surveillance measures during the COVID-19 pandemic in Germany.

Introduction: To address the question as to which infection surveillance measures are used during the ongoing COVID-19 pandemic in Germany and how they differ from pre-existing approaches.Methods: In accordance with the systematic approach of a scoping review, a literature search was conducted in national and international medical literature databases using a search string. The search in the databases was limited to the period from 01.01.2000 to 15.11.2020 and has been subsequently completed by hand search until 08.03.2021. A hand search, even beyond 15.11.2020, seemed necessary and reasonable, since due to the dynamics of the ongoing COVID-19 pandemic, a large number of articles and regulations are being published very quickly at short notice.Results: The literature search resulted in the following number of hits in the databases listed below: PubMed: 165 articlesCochrane: 1 review and 35 studiesWeb of Science: 217 articlesRobert Koch Institute: 49 articles Thus, a total of 467 hits were identified, with a total of 124 hits being duplicates. From these, 138 articles were considered relevant to the COVID-19 infection surveillance situation in Germany based on established criteria. After reading the full texts, 92 articles and websites were ultimately included in the scoping review.Discussion: Many of the lessons learned from previous outbreaks seem to have been implemented in the infection surveillance measures during the ongoing COVID-19 pandemic in Germany. Most of the changes compared with previous measures were based on technological streamlining of existing procedures and changes and more inclusion of the population in different infection surveillance measures.

Network models to evaluate vaccine strategies towards herd immunity in COVID-19

Vaccination remains a critical element in the eventual solution to the COVID-19 public health crisis. Many vaccines are already being mass produced and supplied in many countries. However, the COVID-19 vaccination programme will be the biggest in history. Reaching herd immunity will require an unprecedented mass immunisation campaign that will take several months and millions of dollars.Using different network models, COVID-19 pandemic dynamics of different countries can be recapitulated such as in Italy. Stochastic computational simulations highlight that peak epidemic sizes in a population strongly depend on the network structure. Assuming a vaccine efficacy of at least 80% in a mass vaccination program, at least 70% of a given population should be vaccinated to obtain herd immunity, independently of the network structure. If the vaccine efficacy reports lower levels of efficacy in practice, then the coverage of vaccination would be needed to be even higher. Simulations suggest that the “Ring of Vaccination” strategy, vaccinating susceptible contact and contact of contacts, would prevent new waves of COVID −19 meanwhile a high percent of the population is vaccinated.

The real COVID-19 pandemic dynamics in Qatar in 2021: simulations, predictions and verifications of the SIR model

The third COVID-19 pandemic wave in Qatar was simulated with the use of the generalized SIR-model and the accumulated number of cases reported by Johns Hopkins University for the period: April 25 - May 8, 2021. The results were compared with the SIR simulations performed before for the second wave and the number of laboratory-confirmed cases in the first half of 2021. Despite the mass vaccination that began in December 2020, Qatar experienced a new epidemic wave in March-April 2021. As of the end of June 2021, the positive effects of vaccination were still unclear, although the number of fully vaccinated was already approaching half the population. Additional simulations have demonstrated that many COVID-19 cases are not detected. The real accumulated number of cases in Qatar can exceed the laboratory-confirmed one more than 5 times. This fact drastically increases the probability of meeting an infectious person and the epidemic duration.

Antibiotic Use during the COVID-19 Pandemic in a Tertiary Hospital with an Ongoing Antibiotic Stewardship Program.

During the recent pandemic, the fact that the clinical manifestation of COVID-19 may be indistinguishable from bacterial infection, as well as concerns of bacterial co-infection, have been associated with an increased use of antibiotics. The objective of this study was to assess the effect of targeted antibiotic stewardship programs (ASP) on the use of antibiotics in designated COVID-19 departments and to compare it to the antibiotic use in the equivalent departments in the same periods of 2018 and 2019. Antibiotic consumption was assessed as days of treatment (DOT) per 1000 patient days (PDs). The COVID-19 pandemic was divided into three periods (waves) according to the pandemic dynamics. The proportion of patients who received at least one antibiotic was significantly lower in COVID-19 departments compared to equivalent departments in 2018 and 2019 (Wave 2: 30.2% vs. 45.6% and 44.9%, respectively; Wave 3: 30.5% vs. 47.8% and 50.1%, respectively, p < 0.001). The DOT/1000PDs in every COVID-19 wave was lower than during similar periods in 2018 and 2019 (179-282 DOT/1000PDs vs. 452-470 DOT/1000PDs vs. 426-479 DOT/1000PDs, respectively). Moreover, antibiotic consumption decreased over time during the pandemic. In conclusion, a strong ASP is effective in restricting antibiotic consumption, particularly for COVID-19 which is a viral disease that may mimic bacterial sepsis but has a low rate of concurrent bacterial infection.

Modeling the Transmission Dynamics of COVID-19 Pandemic in Caputo Type Fractional Derivative

COVID-19 disease, a deadly pandemic ravaging virtually throughout the world today, is undoubtedly a great calamity to human existence. There exists no complete curative medicine or successful vaccines that could be used for the complete control of this deadly pandemic at the moment. Consequently, the study of the trends of this pandemic is critical and of great importance for disease control and risk management. Computation of the basic reproduction number by means of mathematical modeling can be helpful in estimating the potential and severity of an outbreak and providing insightful information which is useful to identify disease intensity and necessary interventions. Considering the enormity of the challenge and the burdens which the spread of this COVID-19 disease placed on healthcare system, the present paper attempts to study the pattern and the trend of spread of this disease and prescribes a mathematical model which governs COVID-19 pandemic using Caputo type derivative. Local stability of the equilibria is also discussed in the paper. Some numerical simulations are given to illustrate the analytical results. The obtained results shows that applied numerical technique is computationally strong for modeling COVID-19 pandemic.

Use of Fuzzy Coalition Games in Socially Oriented Decision Making During Hospitalization in Pandemic

The problems of organizing medical care in the context of the COVID-19 pandemic, associated with the uncertainty and limitedness of various resources, led to the need to improve decision-making systems for hospitalization of patients. Situational management can improve the decision-making process to fit the current situation better. At the same time, it becomes important to take into account the influence of psychological factors on decisions made during hospitalization. The paper proposes the use of coalition games for situational management during hospitalization of patients. The players and members of the coalition are hospitals, ambulance teams, patients and computed tomography centers. The goal of the game is to form a coalition of participants that provides the maximum benefit in terms of time and cost of hospitalization at the time of decision making. The general scheme of hospitalization, the main sources of information about the situation, the formulation and formalization of the problem are considered. An experiment was carried out in which the formation of a coalition during hospitalization was tested based on data obtained from analyzing the dynamics of the COVID-19 pandemic. Due to the small amount of data and the lack of approved models of the situation development, when carrying out the calculation, some of the parameters were estimated using heuristic models of the development of the situation, based on the analysis of information from open sources of information. The experiment result contains a set of coalitions that provide the maximum benefit under the specified constraints. At the same time, the calculation time of the coalition game allows using the proposed model of decision-making support during hospitalization in the dispatch service of ambulance stations.

Visible and Real Sizes of New COVID-19 Pandemic Waves in Ukraine

Background. To simulate the COVID-19 pandemic dynamics, various data sets and different mathematical models can be used. In particular, previous simulations for Ukraine were based on smoothing of the dependence of the number of cases on time, classical and the generalized SIR (susceptible-infected-removed) models. Different simulation and comparison methods were based on official accumulated number of laboratory confirmed cases and the data reported by Johns Hopkins University. Since both datasets are incomplete (a very large percentage of infected persons are asymptomatic), the accuracy of calculations and predictions is limited. In this paper we will try to assess the degree of data incompleteness and correct the relevant forecasts. Objective. We aimed to estimate the real sizes of two new epidemic waves in Ukraine and compare them with visible dynamics based on the official number of laboratory confirmed cases. We also aimed to estimate the epidemic durations and final numbers of cases. Methods. In this study we use the generalized SIR model for the epidemic dynamics and its known exact solution. The known statistical approach is adopted in order to identify both the degree of data incompleteness and parameters of SIR model. Results. We have improved the method of estimating the unknown parameters of the generalized SIR model and calculated the optimal values ​​of the parameters. In particular, the visibility coefficients and the optimal values of the model parameters were estimated for two pandemic waves in Ukraine occurred in December 2020–March 2021. The real number of cases and the real number of patients spreading the infection versus time were calculated. Predictions of the real final sizes and durations of the pandemic in Ukraine are presented. If current trends continue, the end of the pandemic should be expected no earlier than in August 2022. Conclusions. New method of the unknown parameters identification for the generalized SIR model was proposed, which allows estimating the coefficients of data incompleteness as well. Its application for two pandemic waves in Ukraine has demonstrated that the real number of COVID-19 cases is approximately four times higher than those shown in official statistics. Probably, this situation is typical for other countries. The reassessments of the COVID-19 pandemic dynamics in other countries and clarification of world forecasts are necessary.

Absolute humidity of atmospheric air and COVID-19

Today COVID-19 is number one global point of focus. Therefore, study of the effects of environmental conditions, in which exist pandemic subjects — people and viruses, on pandemic dynamics and results is extremely important. The authors made a correlation analysis of dependence between incidence/mortality of population and absolute and relative humidity in 73 countries and regions on different continents of the Earth. The methodology developed defines how and in what periods of time the environmental factors effect on human incidence and mortality, how strongly particular atmospheric parameter affects the process of infection and disease flow. The undertaken calculations allowed to prove that the absolute humidity is one of the dominant natural factor which influences on pandemic COVID-19 and other infectious diseases dynamics. The growth of absolute air humidity can have both positive and negative effect on incidence and mortality of population while the effect’s character depends on absolute humidity’s own level and other atmospheric parameters. Correlation of absolute and relative humidity with incidence/mortality at the same time can be different in value or sign. Existing regulations at the federal level in Russian Federation are established without taking into account the minimum allowable and physiologically optimal value of absolute humidity, and need corrections. The question of the impact of absolute humidity is of great importance for northern territories, where most of the year the value of absolute humidity is less than the minimum allowed. The achieved results show high degree of the impact of absolute humidity on incidence and mortality of population due to COVID-19 and contribute to better understanding of pandemic peaks cyclicality and conscious forecasting of start of periods of the most dangerous epidemiological reality.

A Public Health Reset Through Contractualism

A Public Health Reset Through Contractualism

The COVID-19 Pandemic and Commercial Property Rent Dynamics

This paper utilizes timely proprietary data to examine the contemporary impact of the COVID-19 pandemic on commercial property rent dynamics in the Asia–Pacific region. Given that the Asia–Pacific region was the first to be impacted by the public health crisis, it is important to examine how the COVID-19 pandemic has affected the real estate markets in this region and to assess how the region has been recovering since then. Our regression analysis, controlling for different macroeconomic fundamentals and city and property type fixed effects, documents substantial declines in rents of approximately 15% during the first six months of 2020 across the Asia–Pacific commercial property market. We further observe that the most significant declines in rent occur in regions where exposure to the COVID-19 pandemic is the more severe, and in the retail property sector, where we have been observing continued declines of over 30%, with little recovery as of the second quarter of 2020. In additional analysis, we examine capital values and show that while capital targeting the retail property sector has been muted, there is some evidence showing capital flows into the residential and industrial sectors. We also show that fiscal stimuli imposed by governments have moderated the adverse impact of the pandemic. Overall, our study shows that while the effect of the COVID-19 public health crisis is detrimental to commercial real estate, its impact varies significantly across different regions and property sectors.

COVID-19 spread, detection, and dynamics in Bogota, Colombia

Latin America has been severely affected by the COVID-19 pandemic but estimations of rates of infections are very limited and lack the level of detail required to guide policy decisions. We implemented a COVID-19 sentinel surveillance study with 59,770 RT-PCR tests on mostly asymptomatic individuals and combine this data with administrative records on all detected cases to capture the spread and dynamics of the COVID-19 pandemic in Bogota from June 2020 to early March 2021. We describe various features of the pandemic that appear to be specific to a middle income countries. We find that, by March 2021, slightly more than half of the population in Bogota has been infected, despite only a small fraction of this population being detected. The initial buildup of immunity contributed to the containment of the pandemic in the first and second waves. We also show that the share of the population infected by March 2021 varies widely by occupation, socio-economic stratum, and location. This, in turn, has affected the dynamics of the spread with different groups being infected in the two waves.

Impact of essential workers in the context of social distancing for epidemic control.

New emerging infectious diseases are identified every year, a subset of which become global pandemics like COVID-19. In the case of COVID-19, many governments have responded to the ongoing pandemic by imposing social policies that restrict contacts outside of the home, resulting in a large fraction of the workforce either working from home or not working. To ensure essential services, however, a substantial number of workers are not subject to these limitations, and maintain many of their pre-intervention contacts. To explore how contacts among such "essential" workers, and between essential workers and the rest of the population, impact disease risk and the effectiveness of pandemic control, we evaluated several mathematical models of essential worker contacts within a standard epidemiology framework. The models were designed to correspond to key characteristics of cashiers, factory employees, and healthcare workers. We find in all three models that essential workers are at substantially elevated risk of infection compared to the rest of the population, as has been documented, and that increasing the numbers of essential workers necessitates the imposition of more stringent controls on contacts among the rest of the population to manage the pandemic. Importantly, however, different archetypes of essential workers differ in both their individual probability of infection and impact on the broader pandemic dynamics, highlighting the need to understand and target intervention for the specific risks faced by different groups of essential workers. These findings, especially in light of the massive human costs of the current COVID-19 pandemic, indicate that contingency plans for future epidemics should account for the impacts of essential workers on disease spread.