Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection

Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia

Quantifying the uncertainty in data-driven mechanistic models is fundamental in public health applications. COVID-19 is a complex disease that had a significant impact on global health and economies. Several mathematical models were used to understand the complexity of the transmission dynamics under different hypotheses to support the decision-making for disease management. This paper highlights various scenarios of a 6D epidemiological model known as SEIQRD (Susceptible-Exposed-Infected-Quarantined-Recovered-Deceased) to evaluate its effectiveness in prediction and state estimation during the spread of COVID-19 pandemic. Then we investigate the suitability of the classical 4D epidemiological model known as SIRD (Susceptible-Infected-Recovered-Deceased) in the long-term behaviour in order to make a comparison between these models. The primary aim of this paper is to establish a foundational basis for the validity and epidemiological model comparisons in long-term behaviour which may help identify the degree of model complexity that is required based on two approaches viz. the Bayesian inference employing the nested sampling algorithm and recursive state estimation utilizing the Extended Kalman Filter (EKF). Our approach acknowledges the potential imperfections and uncertainties inherent in compartmental epidemiological models. By integrating our proposed methodology, these models can consistently generate predictions closely aligned with the observed data on active cases and deaths. This framework, implemented within the EKF algorithm, offers a robust tool for addressing future, unknown pandemics. Moreover, we present a systematic methodology for time-varying parameter estimation along with uncertainty quantification using Saudi Arabia COVID-19 data and obtain the credible confidence intervals of the epidemiological nonlinear dynamical system model parameters.

Investigation of epidemiological and clinical characteristics of people infected with SARS-CoV-2 during the second pandemic of COVID-19 in Chengdu, China

Objective

This study investigated the epidemiological and clinical characteristics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected patients during the second pandemic of COVID-19 (coronavirus disease of 2019) in Chengdu, China. Furthermore, the differences between first infection and re-infection cases were also compared and analyzed to provide evidence for better prevention and control of SARS-CoV-2 re-infection.

Methods

An anonymous questionnaire survey was conducted using an online platform (wjx.cn) between May 20, 2023 to September 12, 2023.

Results

This investigation included 62.94% females and 32.97% of them were 18-30 years old. Furthermore, 7.19-17.18% of the participants either did not receive vaccination at all or only received full vaccination, respectively. Moreover, 577 (57.64%) participants were exposed to cluster infection. The clinical manifestations of these patients were mainly mild to moderate; 78.18% of participants had a fever for 1-3 days, while 37.84% indicated a full course of disease for 4-6 days. In addition, 40.66% of the participants had re-infection and 72.97% indicated their first infection approximately five months before. The clinical symptoms of the first SARS-CoV-2 infection were moderate to severe, while re-infection indicated mild to moderate symptoms (the severity of symptoms other than diarrhea and conjunctival congestion had statistically significant differences) (p < 0.05). Moreover, 70.53 and 59.21% of first and re-infection cases had fever durations of 3-5 and 0-2 days, respectively. Whereas 47.91 and 46.40% of first and re-infection cases had a disease course of 7-9 and 4-6 days.

Conclusion

The SARS-CoV-2 infected individuals in Chengdu, China, during the second pandemic of COVID-19 had mild clinical symptoms and a short course of disease. Furthermore, compared with the first infection, re-infection cases had mild symptoms, low incidences of complications, short fever duration, and course of disease.

Forecasting and what-if analysis of new positive COVID-19 cases during the first three waves in Italy

The joint exploitation of data related to epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms can support the development of predictive models that can be used to forecast new positive cases and study the impact of more or less severe restrictions. In this work, we integrate heterogeneous data from several sources and solve a multivariate time series forecasting task, specifically targeting the Italian case at both national and regional levels, during the first three waves of the pandemic. The goal is to build a robust predictive model to predict the number of new cases over a given time horizon so that any restrictive actions can be better planned. In addition, we perform a what-if analysis based on the best-identified predictive models to evaluate the impact of specific restrictions on the trend of positive cases. Our focus on the first three waves is motivated by the fact that it represents a typical emergency scenario (when no stable cure or vaccine is available) that may occur when a new pandemic spreads. Our experimental results prove that exploiting the considered heterogeneous data leads to accurate predictive models, reaching a WAPE of 5.75% at the national level. Furthermore, in the subsequent what-if analysis, we observed that strong all-in-one initiatives, such as total lockdowns, may not be adequate, while more specific and targeted solutions should be adopted. The developed models can help policy and decision-makers better plan intervention strategies and retrospectively analyze the effects of the decisions made at different scales. Joint exploitation of data on epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms to learn predictive models to forecast new positive cases.

Evaluation of possible COVID-19 reinfection in children: A multicenter clinical study

BACKGROUND

Although it was originally unknown whether there would be cases of reinfection of coronavirus disease 2019 (COVID-19) as seen with other coronaviruses, cases of reinfection were reported from various regions recently. However, there is little information about reinfection in children.

METHODS

In this study, we aimed to investigate the incidence and clinical findings of reinfection in pediatric patients who had recovered from COVID-19. We retrospectively evaluated all patients under 18 years of age with COVID-19 infection from a total of eight healthcare facilities in Turkey, between March 2020 and July 2021. Possible reinfection was defined as a record of confirmed COVID-19 infection based on positive reverse transcription-polymerase chain reaction (RT-PCR) test results at least 3 months apart.

RESULTS

A possible reinfection was detected in 11 out of 8840 children, which yielded an incidence of 0.12%. The median duration between two episodes of COVID-19 was 196 (92-483) days. When initial and second episodes were compared, the rates of symptomatic and asymptomatic disease were similar for both, as was the severity of the disease (p = 1.000). Also, there was no significant difference in duration of symptoms (p = 0.498) or in hospitalization rates (p = 1.000). Only one patient died 15 days after PCR positivity, which resulted in a 9.1% mortality rate for cases of reinfection in pediatric patients.

CONCLUSION

We observed that children with COVID-19 were less likely to be exposed to reinfection when compared with adults. Although the clinical spectrum of reinfection was mostly similar to the first episode, we reported death of a healthy child during the reinfection.

An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data

The prevalence of COVID-19 has been the most serious health challenge of the 21th century to date, concerning national health systems on a daily basis, since December 2019 when it appeared in Wuhan City. Nevertheless, most of the proposed mathematical methodologies aiming to describe the dynamics of an epidemic, rely on deterministic models that are not able to reflect the true nature of its spread. In this paper, we propose a SEIHCRDV model - an extension/improvement of the classic SIR compartmental model - which also takes into consideration the populations of exposed, hospitalized, admitted in intensive care units (ICU), deceased and vaccinated cases, in combination with an unscented Kalman filter (UKF), providing a dynamic estimation of the time dependent system's parameters. The stochastic approach is considered necessary, as both observations and system equations are characterized by uncertainties. Apparently, this new consideration is useful for examining various pandemics more effectively. The reliability of the model is examined on the daily recordings of COVID-19 in France, over a long period of 265 days. Two major waves of infection are observed, starting in January 2021, which signified the start of vaccinations in Europe providing quite encouraging predictive performance, based on the produced NRMSE values. Special emphasis is placed on proving the non-negativity of SEIHCRDV model, achieving a representative basic reproductive number R 0 and demonstrating the existence and stability of disease equilibria according to the formula produced to estimate R 0 . The model outperforms in predictive ability not only deterministic approaches but also state-of-the-art stochastic models that employ Kalman filters. Furthermore, the relevant analysis supports the importance of vaccination, as even a small increase in the dialy vaccination rate could lead to a notable reduction in mortality and hospitalizations.

Intelligent Decision-Support System for Epidemiological Diagnostics. II. Information Technologies Development*, *

The article projects the components of the intelligent decision support system for epidemiological diagnostics and investigates their interaction with the user. The system includes a bank of models and machine learning methods, a bank of population dynamics models, visualization and reporting tools, and management decision-making unit. The concept of information technology to ensure biosafety of the population is provided. A model of specified information technology use cases is developed and a sequence diagram is constructed. A model of information technology components and ways of their deployment on a server are proposed.

Data-driven prediction of COVID-19 cases in Germany for decision making

BACKGROUND

The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation.

METHODS

We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021.

RESULTS

The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible.

CONCLUSIONS

We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.

The Effect of Varying Interval Definitions on the Prevalence of SARS-CoV-2 Reinfections: A Retrospective Cross-Sectional Cohort Study

Background: We assessed the SARS-CoV-2 reinfection rate in a large patient cohort, and evaluated the effect of varying time intervals between two positive tests on assumed reinfection rates using viral load data. Methods: All positive SARS-CoV-2 samples collected between 1 March 2020 and 1 August 2021 from a laboratory in the region Kennemerland, the Netherlands, were included. The reinfection rate was analyzed using different time intervals between two positive tests varying between 2 and 16 weeks. SARS-CoV-2 PCR crossing point (Cp) values were used to estimate viral loads. Results: In total, 679,513 samples were analyzed, of which 53,366 tests (7.9%) were SARS-CoV-2 positive. The number of reinfections varied between 260 (0.52%) for an interval of 2 weeks, 89 (0.19%) for 4 weeks, 52 (0.11%) for 8 weeks, and 37 (0.09%) for a minimum interval of 16 weeks between positive tests. The median Cp-value (IQR) in the second positive samples decreased when a longer interval was chosen, but stabilized from week 8 onwards. Conclusions: Although the calculated reinfection prevalence was relatively low (0.11% for the 8-week time interval), choosing a different minimum interval between two positive tests resulted in major differences in reinfection rates. As reinfection Cp-values stabilized after 8 weeks, we hypothesize this interval to best reflect novel infection rather than persistent shedding.

COVID-19: What if immunity wanes?

Using a simple economic model in which social distancing reduces contagion, we study the implications of waning immunity for the epidemiological dynamics and social activity. If immunity wanes, we find that COVID-19 likely becomes endemic and that social distancing is here to stay until the discovery of a vaccine or cure. But waning immunity does not necessarily change optimal actions on the onset of the pandemic. Decentralized equilibria are virtually independent of waning immunity until close to peak infections. For centralized equilibria, the relevance of waning immunity decreases in the probability of finding a vaccine or cure, the costs of infection (e.g., infection-fatality rate), the degree of partial immunity and the presence of other NPIs that lower contagion (e.g., quarantining and mask use). In simulations calibrated to July 2020, our model suggests that waning immunity is virtually unimportant for centralized equilibria until at least 2021. This provides vital time for individuals and policy-makers to learn about immunity against SARS-CoV-2 before it becomes critical.

A New Numerical Scheme for Time Fractional Diffusive SEAIR Model with Non-Linear Incidence Rate: An Application to Computational Biology

In this paper, we propose a modified fractional diffusive SEAIR epidemic model with a nonlinear incidence rate. A constructed model of fractional partial differential equations (PDEs) is more general than the corresponding model of fractional ordinary differential equations (ODEs). The Caputo fractional derivative is considered. Linear stability analysis of the disease-free equilibrium state of the epidemic model (ODEs) is presented by employing Routh–Hurwitz stability criteria. In order to solve this model, a fractional numerical scheme is proposed. The proposed scheme can be used to find conditions for obtaining positive solutions for diffusive epidemic models. The stability of the scheme is given, and convergence conditions are found for the system of the linearized diffusive fractional epidemic model. In addition to this, the deficiencies of accuracy and consistency in the nonstandard finite difference method are also underlined by comparing the results with the standard fractional scheme and the MATLAB built-in solver pdepe. The proposed scheme shows an advantage over the fractional nonstandard finite difference method in terms of accuracy. In addition, numerical results are supplied to evaluate the proposed scheme’s performance.

Coronavirus reinfections: An outlook on evidences and effects

After recovery from COVID-19, a person may become infected again due to reactivation of the virus inside the human body or reinfection with a genetically distinct mutant virus owing to reinfection. The COVID-19 reinfection has been recorded all around the world, albeit it is still uncommon. The reinfection with COVID-19 raises several questions about virus characteristics such as mutation, growth, functioning, and transmissibility, level and durability of immunity, diagnosis, therapy, and efficacy of vaccine(s) on genetically modified viruses and their durability and safety. This chapter focuses on various aspects of COVID-19 reinfection, including its severity, frequency, immunopathogenesis, immune responses, effect on vaccine development, Corona waves and herd immunity, management and prevention strategies. COVID-19 reinfections are often asymptomatic or mildly symptomatic, and are milder than first infections, with a few exceptions. The management of reinfection should be the same as the treatment of the first COVID-19 infection. The deep, extensive, rapid and real-time whole-genome sequencing studies, as well as an enhanced vaccination drive, and rigorous adherence to COVID-19 appropriate behavior, would be critical in limiting the severity of transmission and reinfection.

Bayesian Framework for Multi-Wave COVID-19 Epidemic Analysis Using Empirical Vaccination Data

The COVID-19 pandemic has highlighted the necessity of advanced modeling inference using the limited data of daily cases. Tracking a long-term epidemic trajectory requires explanatory modeling with more complexities than the one with short-time forecasts, especially for the highly vaccinated scenario in the latest phase. With this work, we propose a novel modeling framework that combines an epidemiological model with Bayesian inference to perform an explanatory analysis on the spreading of COVID-19 in Israel. The Bayesian inference is implemented on a modified SEIR compartmental model supplemented by real-time vaccination data and piecewise transmission and infectious rates determined by change points. We illustrate the fitted multi-wave trajectory in Israel with the checkpoints of major changes in publicly announced interventions or critical social events. The result of our modeling framework partly reflects the impact of different stages of mitigation strategies as well as the vaccination effectiveness, and provides forecasts of near future scenarios.

Estimation of COVID-19 recovery and decease periods in Canada using delay model

We derive a novel model escorted by large scale compartments, based on a set of coupled delay differential equations with extensive delays, in order to estimate the incubation, recovery and decease periods of COVID-19, and more generally any infectious disease. This is possible thanks to some optimization algorithms applied to publicly available database of confirmed corona cases, recovered cases and death toll. In this purpose, we separate (1) the total cases into 14 groups corresponding to 14 incubation periods, (2) the recovered cases into 406 groups corresponding to a combination of incubation and recovery periods, and (3) the death toll into 406 groups corresponding to a combination of incubation and decease periods. In this paper, we focus on recovery and decease periods and their correlation with the incubation period. The estimated mean recovery period we obtain is 22.14 days (95% Confidence Interval (CI) 22.00–22.27), and the 90th percentile is 28.91 days (95% CI 28.71–29.13), which is in agreement with statistical supported studies. The bimodal gamma distribution reveals that there are two groups of recovered individuals with a short recovery period, mean 21.02 days (95% CI 20.92–21.12), and a long recovery period, mean 38.88 days (95% CI 38.61–39.15). Our study shows that the characteristic of the decease period and the recovery period are alike. From the bivariate analysis, we observe a high probability domain for recovered individuals with respect to incubation and recovery periods. A similar domain is obtained for deaths analyzing bivariate distribution of incubation and decease periods.

COVID-19 reinfection: the role of natural immunity, vaccines, and variants

The COVID-19 pandemic has altered innumerable lives. Although recent mass vaccinations offer a glimmer of hope, the rising death toll and new variants continue to dominate the current scenario. As we begin to understand more about SARS-CoV-2 infections, the territory of reinfections with COVID-19 remains unexplored. In this review, we will discuss several aspects of reinfection: (a) How is COVID-19 reinfection characterized? (b) Does prior literature differentiate between reinfection and reactivation? (c) What SARS-CoV-2 strains do the vaccines target and can they protect against new strains? Larger and longer timeline studies are needed to understand reinfection risks. With the ongoing distribution of the SARS-CoV-2 vaccines to provide protection, the understanding of the possibility for SARS-CoV-2 reinfection remains critical.

Abbreviations CDC: Centers for Disease ControlSARS-CoV-2: Severe acute respiratory syndrome coronavirus 2COVID-19: Coronavirus disease 2019RT-PCR: Reverse Transcription Polymerase Chain ReactionPASC: Post-Acute Sequelae of SARS-CoV-2 infection

Evaluation of Reinfection in COVID-19 Patients in the World: A Narrative Review

Background: The evaluation of reinfection and the genetic structure of all human and virus genomes could help to develop programs and protocols for providing services and ultimately to prevent the disease by producing more effective vaccines. Therefore, the aim of this study was to investigate the presence and occurrence of COVID-19 reinfection through a narrative review study. Methods: We searched the Medline (PubMed), Embase, Scopus, Web of Science, Cochrane library, Ovid, and CINHAL databases. Inclusion criteria included all studies whose main purpose was to provide information about the occurrence or presence of reinfection in patients with COVID-19. An independent samples t test was used to compare the continuous outcomes between the 2 groups. Results: The mean duration of the first episode in the group with mild or moderate COVID-19 was 24.42±1.67 days, and it was 21.80±3.79 days in the group with severe COVID-19. The mean duration of the second episode (reinfection) in patients with mild or moderate form was 15.38 ± 5.57 days, and it was 19.20±2.98 days in patients with severe form. In both episodes, the duration of the disease did not significantly differ between the 2 groups (p=0.484 in the first episode; p=0.675 in the second episode), but the interval to the occurrence of reinfection in patients with the mild or moderate form was significantly longer than those with the severe form (p<0.001). In this instance, the time interval in patients with the mild or moderate form was 36.63±5.71 days while in those with the severe form of the disease it was 29.70±5.65 days. Besides, the genomes of the viruses isolated from the first and second episode were different. Conclusion: According to the results, all patients should be very careful about the severity of the second episode because of the more need for medical interventions for saving the patients. The interval between the first end and the second episode as well as the duration of each episode is highly important for better management of the disease.

Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling

This paper presents a new stochastic-based method for modelling and analysis of COVID-19 spread. A new deterministic Susceptible, Exposed, Infectious, Recovered (Re-infected) and Deceased-based Social Distancing model, named SEIR(R)D-SD, is proposed by introducing the re-infection rate and social distancing factor into the traditional SEIRD (Susceptible, Exposed, Infectious, Recovered and Deceased) model to account for the effects of re-infection and social distancing on COVID-19 spread. The deterministic SEIRD(R)D-SD model is further converted into the stochastic form to account for uncertainties involved in COVID-19 spread. Based on this, an extended Kalman filter (EKF) is developed based on the stochastic SEIR(R)D-SD model to simultaneously estimate both model parameters and transmission state of COVID-19 spread. Simulation results and comparison analyses demonstrate that the proposed method can effectively account for the re-infection and social distancing as well as uncertain effects on COVID-19 spread, leading to improved accuracy for prediction of COVID-19 spread.

Impact of vaccine supplies and delays on optimal control of the COVID-19 pandemic: mapping interventions for the Philippines

BACKGROUND

Around the world, controlling the COVID-19 pandemic requires national coordination of multiple intervention strategies. As vaccinations are globally introduced into the repertoire of available interventions, it is important to consider how changes in the local supply of vaccines, including delays in administration, may be addressed through existing policy levers. This study aims to identify the optimal level of interventions for COVID-19 from 2021 to 2022 in the Philippines, which as a developing country is particularly vulnerable to shifting assumptions around vaccine availability. Furthermore, we explore optimal strategies in scenarios featuring delays in vaccine administration, expansions of vaccine supply, and limited combinations of interventions.

METHODS

Embedding our work within the local policy landscape, we apply optimal control theory to the compartmental model of COVID-19 used by the Philippine government's pandemic surveillance platform and introduce four controls: (a) precautionary measures like community quarantines, (b) detection of asymptomatic cases, (c) detection of symptomatic cases, and (d) vaccinations. The model is fitted to local data using an L-BFGS minimization procedure. Optimality conditions are identified using Pontryagin's minimum principle and numerically solved using the forward-backward sweep method.

RESULTS

Simulation results indicate that early and effective implementation of both precautionary measures and symptomatic case detection is vital for averting the most infections at an efficient cost, resulting in [Formula: see text] reduction of infections compared to the no-control scenario. Expanding vaccine administration capacity to 440,000 full immunizations daily will reduce the overall cost of optimal strategy by [Formula: see text], while allowing for a faster relaxation of more resource-intensive interventions. Furthermore, delays in vaccine administration require compensatory increases in the remaining policy levers to maintain a minimal number of infections. For example, delaying the vaccines by 180 days (6 months) will result in an [Formula: see text] increase in the cost of the optimal strategy.

CONCLUSION

We conclude with practical insights regarding policy priorities particularly attuned to the Philippine context, but also applicable more broadly in similar resource-constrained settings. We emphasize three key takeaways of (a) sustaining efficient case detection, isolation, and treatment strategies; (b) expanding not only vaccine supply but also the capacity to administer them, and; (c) timeliness and consistency in adopting policy measures.

Beyond the new normal: Assessing the feasibility of vaccine-based suppression of SARS-CoV-2

As the COVID-19 pandemic drags into its second year, there is hope on the horizon, in the form of SARS-CoV-2 vaccines which promise disease suppression and a return to pre-pandemic normalcy. In this study we critically examine the basis for that hope, using an epidemiological modeling framework to establish the link between vaccine characteristics and effectiveness in bringing an end to this unprecedented public health crisis. Our findings suggest that a return to pre-pandemic social and economic conditions without fully suppressing SARS-CoV-2 will lead to extensive viral spread, resulting in a high disease burden even in the presence of vaccines that reduce risk of infection and mortality. Our modeling points to the feasibility of complete SARS-CoV-2 suppression with high population-level compliance and vaccines that are highly effective at reducing SARS-CoV-2 infection. Notably, vaccine-mediated reduction of transmission is critical for viral suppression, and in order for partially-effective vaccines to play a positive role in SARS-CoV-2 suppression, complementary biomedical interventions and public health measures must be deployed simultaneously.

COVID-19 reinfection in a healthcare worker after exposure with high dose of virus: A case report

Reinfection with COVID-19 is possible after exposure to a high dose of the virus. Due to immunity acquired during the previous infection, light symptoms are expected. The finding indicates importance of continuous protection in healthcare workers.

Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

Simple Summary

A modified SIR model was applied to provide COVID-19 pandemic analysis and predictions for Gulf Cooperation Council countries, as well as representative countries in Europe and New York City. We estimated reported, infected, and unreported cases from cumulative reported cases and simulated data. We also estimated the basic reproduction rates at different phases of the pandemic. Outputs show that the modified SIR model fits very well with the outcome of the COVID-19 pandemic for the studied countries and could be generalized to other countries. The model prediction emphasizes the value of significant interventions in public health in regulating the epidemic taking into account that a constant fraction of the infected cases remain unreported during the pandemic. We report and analyze the effectiveness of preventive/intervention measures applied to the overall community to curb the severity of the pandemic. Our model could be used to support public health authorities with respect to post-outbreak reopening decisions, highlighting effective measures that need to be maintained, eased, or implemented to support safe reopening strategies in the GCC countries.

Abstract

Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

8806 Russian patients demonstrate T cell count as better marker of COVID-19 clinical course severity than SARS-CoV-2 viral load

The article presents a comparative analysis of SARS-CoV-2 viral load (VL), T lymphocyte count and respiratory index PaO₂:FiO₂ ratio as prospective markers of COVID-19 course severity and prognosis. 8806 patients and asymptomatic carriers were investigated in time interval 15 March–19 December 2020. T cell count demonstrated better applicability as a marker of aggravating COVID-19 clinical course and unfavourable disease prognosis than SARS-CoV-2 VL or PaO₂:FiO₂ ratio taken alone. Using T cell count in clinical practice may provide an opportunity of early prediction of deteriorating a patient’s state.

SARS-CoV-2 Reinfection among Healthcare Workers in Mexico: Case Report and Literature Review

Since the onset of the COVID-19 pandemic, there have been multiple questions regarding reinfections associated with SARS-CoV-2. Healthcare workers on duty, due to overexposure in environments where there are more cases of COVID-19, are more prone to become infected by this virus. Here, we report 4 cases that meet the definition of clinical reinfection by SARS-CoV-2, as well as a literature review on this subject; all occurred in healthcare workers in Acapulco Guerrero, Mexico who provide their services in a hospital that cares for patients with COVID-19. The time between the manifestation of the first and second infection for each case was 134, 129, 107 and 82 days, all patients presented symptomatology in both events. The time between remission of the first infection and onset of second infection was 108, 109, 78 and 67 days for each case, while the time to confirmation by reverse transcription polymerase chain reaction (RT-PCR) between infections was 134, 124, 106 and 77 days. In two of the four cases the reinfection resulted in a more severe case, while in the remaining two cases the manifestation of symptoms and complications was similar to that presented in the first infection. Given this scenario, greater care is needed in the management of the pandemic caused by SARS-CoV-2 to protect healthcare workers and the general public from risks and complications caused by a possible reinfection by SARS-CoV-2.

[Reasons for rejecting official recommendations and measures concerning protection against SARS-CoV-2-a qualitative study of social media posts]

BACKGROUND

In order to slow down the spread of SARS-CoV‑2 (severe acute respiratory syndrome coronavirus type 2) the federal states and the government in Germany have implemented protective measures with far-reaching consequences for the population and the economy. Amongst others, these measures include the temporary restriction of the operation of leisure facilities as well as contact and travel restrictions. These government regulations and recommendations have provoked mixed reactions, with some parts of the population not complying accordingly.

OBJECTIVES

The aim of the present study is to explore reasons for the noncompliance with protective measures on the basis of social media posts.

MATERIALS AND METHODS

Three social networks (Facebook, Twitter, and YouTube comments) were systematically investigated for the period 2 March to 18 April 2020 with regard to attitudes and beliefs about "social distancing" and other protective measures by means of qualitative document and content analysis. A total of 119 postings were included in the analysis and interpreted.

RESULTS

Six main categories and four subcategories were identified in terms of the rejection of protective measures: misinformation of social media (trivialization and doubts about the effectiveness of protective measures), mistrust of the established public media, knowledge deficits and uncertainty, restriction of basic rights, the role of authorities (population control and poor trust in the Robert Koch Institute), and economic consequences of the pandemic.

CONCLUSION

Misinformation in social media and knowledge deficits may contribute to underestimating the pandemic. In addition, economic pressures may contribute to rejecting protective measures. To increase the acceptance of implemented protective measures, health education and transparent and evidence-based communication represent relevant determinants.

Antibody tests: They are more important than we thought

Antibody testing is a non-pharmaceutical intervention - not recognized so far in the literature - to prevent COVID-19 contagion. I show this in a simple economic model of an epidemic in which agents choose social activity under health state uncertainty. In the model, susceptible and asymptomatic agents are more socially active when they think they might be immune. And this increased activity escalates infections, deaths, and welfare losses. Antibody testing, however, prevents this escalation by revealing that those agents are not immune. Through this mechanism, I find that antibody testing prevents about 12% of COVID-19 related deaths within 12 months.

Towards Dynamic lockdown strategies controlling pandemic spread under healthcare resource budget

COVID-19 is one of the deadliest pandemics in modern human history that has killed nearly a million people and rapidly inundated the healthcare resources around the world. Current lockdown measures to curb infection spread are threatening to bring the world economy to a halt, necessitating dynamic lockdown policies that incorporate the healthcare resource budget of people in a zone. We conceive a dynamic pandemic lockdown strategy that employs reinforcement learning to modulate the zone mobility, while restricting the COVID-19 hospitalizations within its healthcare resource budget. We employ queueing theory to model the inflow and outflow of patients and validate the approach through extensive simulation on real demographic and epidemiological data from the boroughs of New York City. Our experiments demonstrate that this approach can not only adapt to the varying trends in contagion in a region by regulating its own lockdown level, but also manages the overheads associated with time-varying dynamic lockdown policies.