Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020

Airborne pathogen monitoring and dispersion modelling on passenger ships: A review

The COVID-19 pandemic demonstrated a profound inability of pre-pandemic passenger ship policies implemented by both ship operators and governmental authorities to detect and address newly emerging diseases. The essentiality of maritime transport puts into focus the risk of approach to address known and new emerging airborne infectious diseases that, due to increasing capacity, are likely to occur on passenger ships. In order to enhance the passenger experience, prepare shipping for pandemics like COVID-19, and improve the resilience and safety of the industry, this review critically synthesises existing literature on (1) monitoring ventilation conditions and aerosol dispersion, linking them to airborne transmission risk using airborne aerosols and ventilation performance as input parameters for computational fluid dynamics (CFD) simulations, and (2) modelling airborne disease transmission risk in controlled passenger ship environments. This review analysed 39 studies on aerosol monitoring, thermal comfort, and infection risk modelling on passenger ships (2000-2023). Additionally, 55 papers on CFD modelling of airborne pathogen dispersion were reviewed: 22 included validation, with most focused on built environments and only four specifically addressing ship environments. Two major challenges relate to the complexity and poorly characterised ventilation boundary conditions on passenger ships, and the other is the lack of suitable validation data. For this reason, ship experimental studies are required for CFD model validation. Only a handful of studies were found that have measured aerosol concentrations on board passenger ships. To the best of our knowledge, there have been no studies conducted on aerosol mass or airborne transmission sampling on board passenger ships or other types of vessels. The results of this review have the potential to create synergistic connections between experimental and modelling studies to inform, characterise and improve the development of numerical models that can accurately estimate infection risk on ships for prevention, mitigation and management of outbreaks.

Practicing pharmacist education based on the experiences of medical support on the cruise ship Diamond Princess

BACKGROUND

Passengers on the cruise ship Diamond Princess (DP), which departed Yokohama on 20 January 2020, were found to be infected with the new coronavirus after arrival in Hong Kong. Passengers and crew were not allowed to disembark, instead being quarantined on board; an onboard pandemic resulted. Many passengers were elderly and in need of medications; pharmacists and other professionals, including the author, were assigned to provide medical support (the author participated on two occasions). Many passengers were not Japanese nationals; those who required medicines not sold in Japan received analogs of medicines that are sold in Japan. Pharmacists were required to complete medication guidance documents (in English). The author considered that by using this experience as teaching material, pharmacy students would not only learn English but also become educated in terms of drug therapy.

METHOD

The author created an exercise for second-year students at Teikyo University in which they were required to provide real-world medical support. The educational effects were measured by analyzing the answers to questionnaires completed before and after the exercise and 'Impressions of the exercise' homework.

RESULTS

Using real emergency events as a teaching tool enhanced students' motivation to learn English and pursue professional pharmacy education (the latter was scheduled to begin in earnest in the third year). At that time, the new coronavirus was poorly understood. The author's experiences taught students that medical workers are educated to offer care even when they are at risk of infection. Translation software (a form of artificial intelligence [AI]) was used to create medication guidance documents in English. The students learnt that if AI translations, i.e., medication guidance documents in English, were accepted at face value, they would be held responsible if the documents were in error.

CONCLUSION

By both listening to the author's lecture on a real-world medical support situation and completing an assignment, students learned many things that are difficult to teach via lectures alone, including the dangers arising when using AI technology in clinical settings and the mindset of medical professionals.

What drives the effectiveness of social distancing in combating COVID-19 across U.S. states?

We propose a new theory of information-based voluntary social distancing in which people's responses to disease prevalence depend on the credibility of reported cases and fatalities and vary locally. We embed this theory into a new pandemic prediction and policy analysis framework that blends compartmental epidemiological/economic models with Machine Learning. We find that lockdown effectiveness varies widely across US States during the early phases of the COVID-19 pandemic. We find that voluntary social distancing is higher in more informed states, and increasing information could have substantially changed social distancing and fatalities.

Sero-prevalence of SARS-CoV-2 antibodies in Ethiopia: Results of the National Population Based Survey, 2021

BACKGROUND

SARS-CoV-2 pandemic has caused a continuing health crisis affecting the public health system globally. Population-based serological surveys are a highly valuable and recommended method to measure population exposure and spread of pandemic, given the existence of asymptomatic cases and little access to diagnostic testing. This national population-based study aims to estimate the seroprevalence of SARS-CoV-2 infection in all parts of Ethiopia and determine potential risk factors and burden of infection.

METHODS

A nationwide seroprevalence survey was done among 12,756 households (HHs) across the country using three-stage stratified sampling technique from April 15, 2021 to May 16, 2021 among population of Ethiopia above 15 years of age. One member of each of the selected HHs, who fulfilled the eligibility criteria, was randomly selected. We captured data using interviews and finger prick blood samples to test for anti-SARS-CoV-2 antibodies using high specificity rapid diagnostic tests (RDTs). A questionnaire was used to capture all necessary data on demographics, social exposure, and history of vaccination for SARS-CoV-2, symptoms compatible with SARS-CoV-2, and any known medical conditions. The data were collected using an open data kits (ODK) software and imported into STATA version 17 for analysis. Descriptive statistics (frequencies and proportions) were used to summarize data on the study variables. Forest plots and maps were used to visualize the seroprevalence of SARS-CoV-2 across various individual and environmental factors. The study sample was weighted, and the survey set command in Stata (svy) was used in the analyses to account for the survey design. Adjusted Odd ratio (AOR) was used to determine higher risk factors of having been infected at least once, 95% confidence interval to assess precision of the estimates, and a P value ≤  0.05 to determine statistically significant.

RESULT AND DISCUSSION

This study indicated the overall national prevalence of seropositivity was 9.3% that suggests nearly one in ten individuals in Ethiopia was exposed to SARS-CoV-2 infection by May 2021. All regional states in the country are affected with SARS-CoV-2 infection although infection was more common in densely populated regions. Seroprevalence was significantly higher among, individual, aged 35-44, 55-64 and 65 and over years had more odds of being infected by SARS-CoV-2 compared with those aged 15-24 years. The seroprevalence is also high among professional/technical occupations, and among those having at least one comorbidity. The participants who had seven and more members had higher odds of infection compared with those who had two or less members. The odds of infection among respondents, who reported having ever tested for COVID-19 and being sick since March 2020, were higher compared with their counterparts. Among the environmental factors, the odds of SARS-CoV-2 infection in urban residents were higher than in the rural setting. In relation to geographic administration boundaries, participants from Harari Region, Addis Ababa, and Benishangul Gumuz had higher odds of infection compared to those from Afar Regions respective.

CONCLUSION AND RECOMMENDATIONS

This study reveals the overall seroprevalence of SARS CoV-2 antibodies in Ethiopia was 10.0% as of May 2021. The seroprevalence of IgG antibodies against COVID-19 is higher than that of IgM antibodies, indicating a past infection. SARS-CoV-2 antibody seroprevalence was varied by regional state, sex, residence area, age, and occupational status. It also suggests that the majority of Ethiopia's have inadequate knowledge of understanding about SARS-CoV-2 antibodies, we recommend strengthening public health and social measures to mitigate the spread of COVID-19 diseases, including increased vaccination coverage and testing capability. All responsible authorities and stakeholders working locally, nationally, and globally need to support strengthening health systems and be prepared to combat morbidity and mortality and to encourage ongoing vaccination efforts. Periodic seroprevalence surveys will aid in monitoring the status and progress of the COVID-19 pandemic.

Behaviour quantification of public health policy adoption - the case of non-pharmaceutical measures during COVID-19

In this work, we provide estimates of non-pharmaceutical interventions (NPIs) adoption and its effects on the COVID-19 disease transmission across the province of Ontario, Canada, in 2020. Using freely available data, we estimate perceived risks of infection and a personal discomfort with complying with NPIs for Ontarians across 34 public health units. With the use of game theory, we model a time series of decision making processes in each public health region to extract an estimate of the adoption level of NPIs from March to December 2020. In conjunction with a susceptible-exposed-recovered-isolated compartmental model for Ontario, we are able to estimate a province-wide effectiveness level of NPIs. Last but not least, we show the model's versatility by applying it to Pennsylvania and Georgia in the United States.

Mapping the viral battlefield: SARS-CoV-2 infection dynamics among healthcare workers in Brazil

BACKGROUND

Understanding the dynamics of SARS-CoV-2 viral infection and factors associated with in-hospital transmission rates among healthcare workers (HCW) is crucial for their protection. Brazil experienced high mortality rates due to COVID-19, and limited data are available on transmission of SARS-CoV-2 infection among HCW. This cohort study aimed to assess the dynamic of SARS-CoV-2 infections in HCW from two tertiary hospitals in central Brazil, one of them a Reference Hospital for COVID-19.

METHODS

From May 2020 to January 2021, 554 HCW directly involved with COVID-19 care were followed through 12 biweekly visits. During these visits, blood, nasal, and oropharyngeal samples were collected, and participants underwent interviews. SARS-CoV-2 detection was carried out using RT-qPCR, while the assessment of seroprevalence was based on IgG detection. Additionally, 35 positive samples underwent viral whole-genome sequencing.

RESULTS

The infection prevalence, as per RT-qPCR, was 28.5% (24.9-32.4), reflecting an overall attack rate ranging from 0.5% to 9.5%, marked by two peaks in August and December 2020. Oligosymptomatic and asymptomatic infections accounted for 14% of prevalent infections. The seroprevalence rate stood at 25.8%. The hospitalization rate was 8.2%, with a fatality rate of 1.3%. Risk factors associated with a positive diagnosis of COVID-19 included being male, working at the referral hospital, having a graduate-education level, and using hydroxychloroquine and zinc for prevention or treatment. One reinfection was identified. Absenteeism was 56.6%. The infection dynamics mirrored the pattern observed in the general population.

CONCLUSION

One-third of the professionals in the followed cohort were infected. Being male, working in a COVID-19 referral center, having a low level of education, and using medications for preventive treatment represented risk factors. Healthcare workers at the COVID-19 referral hospital exhibited a higher incidence rate compared to those at the non-referral hospital, increasing the plausibility that some of the infections occur in the hospital environment.

Prevalence and correlates of facemask usage during the second wave of COVID-19 pandemic in Uganda

The World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (US CDC) documented wearing facemasks in public as one of the most important prevention measures to limit COVID-19 spread. Considering this, WHO and the US CDC developed guidelines for wearing facemasks in public. This study aimed to determine the prevalence and correlates of facemask wearing during the COVID-19 pandemic in northern Uganda. We conducted a cross-sectional study on 587 adults across nine districts in northern Uganda, across 24 high-volume health facilities offering free COVID-9 vaccines. Respondents were selected from the health facilities using a single-stage systematic sampling method. Data was collected in a face-to-face questionnaire interview with an internal validity of Cronbach's α = 0.72 and entered into Excel. A local Institutional Research Board (IRB) approved the study, and Stata 18 was used for data analysis using Modified Poisson Regression to generate prevalence ratios (PR) and adjusted prevalence ratios (aPR), with a p-value set at < 0.05. The reported prevalence of facemask wearing in public among respondents was high [88.7%,95%CI:86%,-91%]. A multivariate analysis found that obese respondents and those who were receptive (agreed) to the lockdown measures were respectively,1.12 times more likely to wear facemasks [aPR = 1.12,95%CI:1.04-1.19;p < 0.01], and1.23 times more likely to wear facemasks [aPR = 1.23, 95%CI:1.07-1.41;p < 0.01]. The most significant finding from this study was the high prevalence of self-reported facemask wearing among adult community members in northern Uganda. The correlates of wearing facemasks were, being obese and agreeing with the presidential directives on the lockdown measures. Although this prevalence is within acceptable rates, the strict enforcement of the practice by security forces has raised concerns among many community members and human rights advocates. We recommend more studies on communities' perspectives on the challenges and benefits of facemask-wearing after the COVID-19 pandemic.

Cryptic transmission of a SARS-CoV-2 variant detected by wastewater surveillance in Panama

The COVID-19 pandemic highlighted the critical role of viral genomic surveillance, prompting numerous countries to enhance their monitoring systems for acute respiratory infections (ARIs), especially influenza-like illnesses (ILIs). Given the significance of asymptomatic cases in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, cases often undetected by the ILI surveillance, a more comprehensive approach was essential to track the circulation of SARS-CoV-2 variants in the population. In response, many countries swiftly adopted wastewater surveillance, which allowed the early detection of SARS-CoV-2 variants before they were identified through molecular characterization from confirmed clinical cases. In this report, we detail the implementation of SARS-CoV-2 wastewater genomic surveillance in Panama during the first half of 2024. Wastewater samples were collected monthly in duplicate at two collection points from three districts of Panama city metropolitan area for testing by SARS-CoV-2 RT-qPCR, and positive samples were analyzed by next-generation sequencing to identify sublineages. A total of 36 wastewater samples and 822 samples obtained through the clinical surveillance were analyzed for molecular detection and sequencing. Sublineages detected by wastewater surveillance were compared to those detected by clinical surveillance for the same period of time. Wastewater surveillance allowed the identification of the Omicron sublineage JN.1.16.1 in the capital city and its surroundings, which was not detected by the clinical surveillance in the country, despite its global circulation. This highlights the critical need to sustain both genomic surveillance programs beyond the pandemic in countries like Panama that serve as pivotal exchange hubs.

SARS-CoV-2 IgG Seroprevalence in the Okinawa Main Island and Remote Islands in Okinawa, Japan, 2020-2021

We estimated the seroprevalence of anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) immunoglobulin G (IgG) in different island groups in Okinawa. A cross-sectional serosurvey was conducted over three periods between July 2020 and February 2021. A total of 2,683 serum samples were collected from six referral medical centers, each covering a separate region in Okinawa. In the main island, the seroprevalence was 0.0% (0/392; 95% confidence interval (CI), 0.0- 0.9%), 0.6% (8/1,448, 0.2-1.1%), and 1.4% (8/582, 0.6-2.7%) during the 1st, 2nd, and 3rd serosurvey, respectively. In remote islands, the seroprevalence was 0.0% (0/144; 95% CI, 0.0-2.5%) and 1.6% (2/123, 0.2-5.8%) during the 2nd and 3rd survey, respectively. The case detection ratio was 2.7 (95% CI, 1.3-5.3) in the main island and 2.8 (0.7-11.1) in remote islands during the 3rd survey. The case detection ratio was the highest in individuals aged 20-29 years (8.3; 95% CI, 3.3-21.4) in the main island and in those aged 50-59 years (14.1; 2.1-92.7) in remote islands, suggesting underreporting of clinical cases by the surveillance system in these subgroups. A serosurvey conducted during an emerging infectious disease epidemic can be useful for validating the reliability of the surveillance system by providing case detection ratio.

Genetic and Epigenetic Determinants of COVID-19 Susceptibility: A Systematic Review

BACKGROUND

The molecular mechanisms regulating coronavirus pathogenesis are complex, including virus-host interactions associated with replication and innate immune control. However, some genetic and epigenetic conditions associated with comorbidities increase the risk of hospitalization and can prove fatal in infected patients. This systematic review will provide insight into host genetic and epigenetic factors that interfere with COVID-19 expression in light of available evidence.

METHODS

This study conducted a systematic review to examine the genetic and epigenetic susceptibility to COVID-19 using a comprehensive approach. Through systematic searches and applying relevant keywords across prominent online databases, including Scopus, PubMed, Web of Science, and Science Direct, we compiled all pertinent papers and reports published in English between December 2019 and June 2023.

RESULTS

The findings reveal that the host's HLA genotype plays a substantial role in determining how viral protein antigens are showcased and the subsequent immune system reaction to these antigens. Within females, genes responsible for immune system regulation are found on the X chromosome, resulting in reduced viral load and inflammation levels when contrasted with males. Possessing blood group A may contribute to an increased susceptibility to contracting COVID-19 as well as a heightened risk of mortality associated with the disease. The capacity of SARS-CoV-2 involves inhibiting the antiviral interferon (IFN) reactions, resulting in uncontrolled viral multiplication.

CONCLUSION

There is a notable absence of research into the gender-related predisposition to infection, necessitating a thorough examination. According to the available literature, a significant portion of individuals affected by the ailment or displaying severe ramifications already had suppressed immune systems, categorizing them as a group with elevated risk.

Early COVID-19 Pandemic Preparedness: Informing Public Health Interventions and Hospital Capacity Planning Through Participatory Hybrid Simulation Modeling

We engaged with health sector stakeholders and public health professionals within the health system through a participatory modeling approach to support policy-making in the early COVID-19 pandemic in Saskatchewan, Canada. The objective was to use simulation modeling to guide the implementation of public health measures and short-term hospital capacity planning to mitigate the disease burden from March to June 2020. We developed a hybrid simulation model combining System Dynamics (SD), discrete-event simulation (DES), and agent-based modeling (ABM). SD models the population-level transmission of COVID-19, ABM simulates individual-level disease progression and contact tracing intervention, and DES captures COVID-19-related hospital patient flow. We examined the impact of mixed mitigation strategies-physical distancing, testing, conventional and digital contact tracing-on COVID-19 transmission and hospital capacity for a worst-case scenario. Modeling results showed that enhanced contact tracing with mass testing in the early pandemic could significantly reduce transmission, mortality, and the peak census of hospital beds and intensive care beds. Using a participatory modeling approach, we not only directly informed policy-making on contact tracing interventions and hospital surge capacity planning for COVID-19 but also helped validate the effectiveness of the interventions adopted by the provincial government. We conclude with a discussion on lessons learned and the novelty of our hybrid approach.

Reflections on COVID-19: A Literature Review of SARS-CoV-2 Testing

Although the Coronavirus disease 2019 (COVID-19) pandemic has ended, there are still many important lessons we can learn, as the pandemic profoundly affected every area of laboratory practice. During the pandemic, extensive changes to laboratory staffing had to be implemented, as many healthcare institutions required regular screening of all healthcare staff. Several studies examined the effectiveness of different screening regimens and concluded that repeated testing, even with lower sensitivity tests, could rival the performance of gold-standard RT-PCR testing in the detection of new cases. Many assay evaluations were performed both in the earlier and later periods of the pandemic. They included both nucleocapsid/spike antibodies and automated antigen assays. Early in the pandemic, it was generally agreed that the initial nucleocapsid antibody assays had poor sensitivity when used before 14 days of disease onset, with total or IgG antibodies being preferred over the use of IgM. Spike antibody assays gradually replaced nucleocapsid antibody assays, as most people were vaccinated. Spike antibodies tracked the rise in antibodies after vaccination with mRNA vaccines and became invaluable in the assessment of vaccine response. Studies demonstrated robust antibody secretion with each vaccine dose and could last for several months post-vaccination. When antigen testing was introduced, they became effective tools to identify affected patients when used serially or in an orthogonal fashion with RT-PCR testing. Despite the numerous findings during the pandemic period, research in COVID-19 has slowed. To this day it is difficult to identify a true neutralizing antibody test for the virus. An appropriate antibody level that would confer protective immunity against the plethora of new variants remains elusive. We hope that a summary of events during the pandemic could provide important insights to consider in planning for the next viral pandemic.

Assessment of environmental factors influencing SARS-CoV-2 in Vietnam's surface water across two years of clinical data

Wastewater-based epidemiology (WBE) is an effective, non-invasive method for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by tracking viral prevalence in water. This study aimed to investigate the presence of SARS-CoV-2 in surface water in Vietnam over two years. One-step quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays were employed to quantify SARS-CoV-2 and its variant-specific mutation sites (G339D/E484A) and pepper mild mottle virus (PMMoV) from a total of 315 samples (105 samples per site) to compare with reported Coronavirus disease 2019 (COVID-19) cases and environmental factors. SARS-CoV-2 was detected in 38 % (40/105), 43 % (45/105), and 39 % (41/105) of water samples from Sites A, B, and C, respectively, with concentrations of 3.0-5.6 log10 copies/L. PMMoV concentrations were 5.1-8.9 log10 copies/L. SARS-CoV-2 levels were higher in winter compared with summer. There was a strong positive association between the mutant type and SARS-CoV-2 concentrations (Spearman's rho = 0.77, p < 0.01). The mean concentrations of mutant and nonmutant types were 2.3 and 1.8 log10 copies/L, respectively. Peaks in SARS-CoV-2 concentrations preceded reported COVID-19 cases by 2-4 weeks, with the highest association observed at a 4-week delay (Pearson's correlation coefficient: 0.46-0.53). Environmental factors, including temperature, pH, and electrical conductivity, correlated negatively with SARS-CoV-2 (Spearman's rho = -0.21, -0.28, and -0.21, respectively, p < 0.05), whereas average rainfall, humidity, and dissolved oxygen correlated positively (Spearman's rho = 0.20, 0.27, and 0.51, respectively, p < 0.05). These correlations highlight the significance of environmental variables in understanding viral prevalence in water. Our findings confirmed the utility of WBE as an early warning system for long-term monitoring. Future research should incorporate environmental factors to improve prediction accuracy for clinical cases and other waterborne diseases.

SARS-CoV-2 seroprevalence and antibody trajectories after easing of COVID-19 restrictions: a longitudinal study in China

Background

We aimed to evaluate the seroprevalence of SARS-CoV-2 and investigate the trajectories of protective immunity and associated risk factors in eastern China between March and November 2023 after the easing of COVID-19 restrictions.

Materials and methods

We conducted repeated population-based seroepidemiologic studies using a multistage, population-stratified, cluster random sampling method. We measured neutralizing antibodies (nAbs) using a fluorescence immunoassay. We calculated both overall and stratified seroprevalence. The latent class growth mixed model (LCGMM) was used to analyze the dynamic trajectories of antibodies, and a multinomial logistic regression model was used to identify factors associated with different antibody trajectory patterns.

Results

A total of 6,147 participants were included at baseline, with a median age of 53.61 years. Both observed and adjusted seroprevalence remained high and stable throughout the study period. The LCGMM identified four distinct antibody trajectories: 75.22% of participants had a high and stable antibody trajectory, while nearly 8% of them exhibited an increase, decline, or low-stable antibody trajectory. Younger participants, women, those fully vaccinated, and individuals with a history of previous infection were more likely to have high and stable antibody trajectories.

Conclusion

The majority of the population maintained sustained protective immunity after the outbreak, following the easing of COVID-19 restrictions across the country.

Where are the cruise ships? mobility and immobility of cruises under COVID-19

The aim of this study is to reveal the spatial-temporal characteristics and influencing factors of the static and dynamic distribution of global cruise ships, against the backdrop of the COVID-19 pandemic, which has transformed the global cruise network from "lines" to "points". This study collected trajectory data for 292 cruise ships and data for 789 cruise ports worldwide from March 2020 to July 2021. Based on the relationship between port and navigation, port management rights research, and port geography theory, we analyzed the spatial distribution and spatiotemporal migration of cruise ships with ArcGIS tools. It was found that, compared with normal times, the distribution of cruise ships in regional markets, countries, and ports showed stronger spatial agglomeration characteristics and formed four types, which were mainly influenced by differences in cruise operators' fleet scale, positioning, and itinerary. With the improvement of the epidemic, cruise ships trended to gather at the cruise home port. During the COVID-19 pandemic, there was an obvious separation between the epidemic prevention country, flag country, and operator country. Operators were inclined to berth their cruise ships in the countries where cruise ships were registered, countries of operators, and ports with high integration. Rather than simply emphasizing the static state of the cruise shipping network, the global cruise geography under the COVID-19 pandemic reflects the right relationship between ports, cruise ships, and companies. This study provides a methodological framework for analyzing the cruise shipping network at the port level and has practical implications for micro-interpretation of the dotted cruise shipping network during the COVID-19 pandemic.

A Complex Case of COVID-19 Pneumonitis in a Patient With Follicular Lymphoma

Protracted COVID-19 is increasingly recognised in immunocompromised patients, particularly those with haematological malignancies. Here, we present the case of a patient with protracted COVID-19 and an underlying B-cell malignancy. Standard COVID-19 treatment with remdesivir and steroids proved ineffective in this patient as she continued to have evolving ground-glass opacities on imaging. A multidisciplinary involvement altered treatment to include a combination of antivirals nirmatrelvir/ritonavir (Paxlovid) and remdesivir, a monoclonal antibody and immunoglobulins leading to a clinical cure. This report highlights the need for a more tailored approach in this patient sub-group than the rest of the population.

A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo

Compartmental models have emerged as robust computational frameworks and have yielded remarkable success in the fight against COVID-19. This study proposes a vaccination-based compartmental model for COVID-19 transmission dynamics. The model reflects the specific stages of COVID-19 infection and integrates a vaccination strategy, allowing for a comprehensive analysis of how vaccination rates influence the disease spread. We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm. First, excluding vaccination dynamics from the model, we estimated key epidemiological parameters like infection, recovery, and disease-induced death rates. This analysis yielded a basic reproduction number (R 0 ) of 1.5. Second, we incorporated vaccination dynamics and estimated the vaccination rate for three vaccines: 0.0051 per day for both Pfizer and Moderna and 0.0059 per day for Janssen. The fitted curves show reductions in the epidemic peak for all three vaccines. Pfizer and Moderna vaccines bring the peak down from 8,029 infected cases to 5,616 infected cases, while the Janssen vaccine reduces it, to 6,493 infected cases. Simulations of the model by varying the vaccination rate and vaccine efficacy were performed. A highly effective vaccine (95% efficacy) with a daily vaccination rate of 0.006 halved COVID-19 infections, reducing cases from 8,029 to around 4,000. The results also show that the model's prediction accuracy for new observations improves with the number of observed data used to train the model.

Severe acute respiratory syndrome coronavirus 2-reactive salivary antibody detection in South Carolina emergency healthcare workers, September 2019-March 2020

On 19 January 2020, the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was identified in the United States, with the first cases in South Carolina confirmed on 06 March 2020. Due to initial limited testing capabilities and potential for asymptomatic transmission, it is possible that SARS-CoV-2 may have been present earlier than previously thought, while the immune status of at-risk populations was unknown. Saliva from 55 South Carolina emergency healthcare workers (EHCWs) was collected from September 2019 to March 2020, pre- and post-healthcare shifts, and stored frozen. To determine the presence of SARS-CoV-2-reactive antibodies, saliva-acquired post-shift was analysed by enzyme-linked immunosorbent assay (ELISA) with a repeat of positive or inconclusive results and follow-up testing of pre-shift samples. Two participants were positive for SARS-CoV-2 N/S1-reactive IgG, confirmed by follow-up testing, with S1 receptor binding domain (RBD)-specific IgG present in one individual. Positive samples were collected from medical students working in emergency medical services (EMSs) in October or November 2019. The presence of detectable anti-SARS-CoV-2 antibodies in 2019 suggests that immune responses to the virus existed in South Carolina, and the United States, in a small percentage of EHCWs prior to the earliest documented coronavirus disease 2019 (COVID-19) cases. These findings suggest the feasibility of saliva as a noninvasive tool for surveillance of emerging outbreaks, and EHCWs represent a high-risk population that should be the focus of infectious disease surveillance.

Six and twelve-month respiratory outcomes in a cohort of severe and critical COVID-19 survivors: A prospective monocentric study in Latin America

Introduction

Severe COVID-19 can result in long-term sequelae known as "chronic COVID," characterized by a wide range of persistent physical and mental symptoms. Chest imaging and pulmonary function test alterations have been observed in recovered patients. Most studies focus on up to a 3-month follow-up after symptom onset or hospital discharge, with few reports on long-term follow-up and limited evidence regarding disease progression in Latin America.

Methods

This study aims to describe the clinical characteristics and changes in pulmonary function, imaging, and quality of life in severe and critical COVID-19 patients requiring ICU admission in a high-complexity hospital in Latin America. A prospective cohort of survivors underwent clinical, radiological, pulmonary function, and quality of life assessments 6 and 12 months post-discharge.

Results

One hundred twelve patients were included, all of whom attended the 6-month follow-up, and 99 returned for the 12-month follow-up. Most subjects had no previous respiratory symptoms or significant medical history. At the end of the follow-up period, 74% of the patients showed interstitial infiltrates in chest tomography and a higher frequency of fibroatelectatic tracts and parenchymal bands. Pulmonary function tests returned to normal ranges, except for carbon monoxide diffusion, but no altered scores were reported in the questionnaires.

Conclusion

Despite residual radiological findings, most parameters studied in severe and critical COVID-19 survivors improved over the 12-month follow-up period. Regardless of the imaging abnormalities, the improvement in variables such as symptomatic relief and normal pulmonary function suggests that these alterations are transient. Carbon monoxide diffusion did not normalize by the end of the follow-up, which is consistent with the abnormalities reported in multiple studies, indicating a potential disease-related pattern.

Evaluating infectious disease outbreak potential and mitigation effectiveness on cruise ships

The cruise ship sector is a major part of the tourism industry, and an estimated over 30 million passengers are transformed worldwide each year. Cruise ships bring diverse populations into proximity for many days, facilitating the transmission of respiratory illnesses. The objective of this study is to develop a modeling framework to inform the development of viable disease risk management policies and measures to control disease outbreaks on cruises. Our model, parameterized and calibrated using the data of the COVID-19 outbreak on the Diamond Princess cruise ship in 2020, is used to assess the impact of the mitigation measures such as mask wearing, vaccination, on-board and pre-traveling testing measures. Our results indicate mask wearing in public places as the cheapest and most affordable measure can drop the number of cumulative confirmed cases by almost 50%. This measure along with the vaccination by declining the number of the cumulative confirmed cases by more than 94% is the most effective measure to control outbreaks on cruises. According to our findings, outbreaks are more predominant in the passenger population than the crew members, however, the protection measures are more beneficial if they are applied by both crew members and passengers. Regarding the testing measure, pre-traveling testing is more functional than the on-board testing to control outbreaks on cruises.

Urban Wastewater-Based Surveillance of SARS-CoV-2 Virus: A Two-Year Study Conducted in City of Patras, Greece

Wastewater-based epidemiology, during the COVID-19 pandemic years, has been applied as a complementary approach, worldwide, for tracking SARS-CoV-2 virus into the community and used as an early warning of the prevalence of COVID-19 infection. The present study presents the results of the 2-year surveillance project, in the city of Patras, Greece. The purpose of the study was to monitor SARS-CoV-2 and implement WBE as an early warning method of monitoring Public Health impact. The presence of SARS-CoV-2 was determined and quantified in 310 samples using RT-qPCR assays. For the years 2022 and 2023, 93.5% and 78.7% of samples were found positive, respectively. Comparison of detection methods have been conducted to select the method with the highest recovery of the viral load. A seasonal variation of the virus was recorded, showing a recession in summer months confirming the country's epidemiological data as indicated by positive correlation of wastewater viral load with registered cases of COVID-19 infections during these years (p < 0.05) and moreover sealed with a significant negative correlation observed with Daily Average (p < 0.01) and Daily Maximum Temperature (p < 0.01). More research was carried out to elucidate a possible association of physicochemical characteristics of wastewater with viral load showing positive correlation with Chlorides (p < 0.01) advocating possible increased use of chlorine-based disinfectants and Electrical Conductivity (p < 0.01) indicates that wastewater during periods of increased infections is more heavily loaded with ions from chemical and biological pollutants. No correlation found with rainfall and physicochemical indicators, such as COD, BOD5, Total Phosphorus, Total Nitrogen, and Total Suspended Solids. According to the findings, WBE represents a useful tool in the management of epidemics based on an environmental approach and it can also shed light on the interacting parameters that capture Public Health since any infections that may lead to epidemics lead to a parallel change in the use of pharmaceuticals, antimicrobials, disinfectants, and microbial load in urban wastewater.

AlphaFold2 Reveals Structural Patterns of Seasonal Haplotype Diversification in SARS-CoV-2 Nucleocapsid Protein Variants

The COVID-19 pandemic saw the emergence of various Variants of Concern (VOCs) that took the world by storm, often replacing the ones that preceded them. The characteristic mutant constellations of these VOCs increased viral transmissibility and infectivity. Their origin and evolution remain puzzling. With the help of data mining efforts and the GISAID database, a chronology of 22 haplotypes described viral evolution up until 23 July 2023. Since the three-dimensional atomic structures of proteins corresponding to the identified haplotypes are not available, ab initio methods were here utilized. Regions of intrinsic disorder proved to be important for viral evolution, as evidenced by the targeted change to the nucleocapsid (N) protein at the sequence, structure, and biochemical levels. The linker region of the N-protein, which binds to the RNA genome and self-oligomerizes for efficient genome packaging, was greatly impacted by mutations throughout the pandemic, followed by changes in structure and intrinsic disorder. Remarkably, VOC constellations acted co-operatively to balance the more extreme effects of individual haplotypes. Our strategy of mapping the dynamic evolutionary landscape of genetically linked mutations to the N-protein structure demonstrates the utility of ab initio modeling and deep learning tools for therapeutic intervention.

Reduction in ACE2 expression in peripheral blood mononuclear cells during COVID-19 - implications for post COVID-19 conditions

BACKGROUND

Severe COVID-19 is uncommon, restricted to 19% of the total population. In response to the first virus wave (alpha variant of SARS-CoV-2), we investigated whether a biomarker indicated severity of disease and, in particular, if variable expression of angiotensin converting enzyme 2 (ACE2) in blood might clarify this difference in risk and of post COVID -19 conditions (PCC).

METHODS

The IRB-approved study compared patients hospitalized with severe COVID-19 to healthy controls. Severe infection was defined requiring oxygen or increased oxygen need from baseline at admission with positive COVID-19 PCR. A single blood sample was obtained from patients within a day of admission. ACE2 RNA expression in blood cells was measured by an RT-PCR assay. Plasma ACE1 and ACE2 enzyme activities were quantified by fluorescent peptides. Plasma TIMP-1, PIIINP and MMP-9 antigens were quantified by ELISA. Data were entered into REDCap and analyzed using STATA v 14 and GraphPad Prism v 10.

RESULTS

Forty-eight patients and 72 healthy controls were recruited during the pandemic. ACE2 RNA expression in peripheral blood mononuclear cells (PBMC) was rarely detected acutely during severe COVID-19 but common in controls (OR for undetected ACE2: 12.4 [95% CI: 2.62-76.1]). ACE2 RNA expression in PBMC did not determine plasma ACE1 and ACE2 activity, suggesting alternative cell-signaling pathways. Markers of fibrosis (TIMP-1 and PIIINP) and vasculopathy (MMP-9) were additionally elevated. ACE2 RNA expression during severe COVID-19 often responded within hours to convalescent plasma. Analogous to oncogenesis, we speculate that potent, persistent, cryptic processes following COVID-19 (the renin-angiotensin system (RAS), fibrosis and vasculopathy) initiate or promote post-COVID-19 conditions (PCC) in susceptible individuals.

CONCLUSIONS

This work elucidates biological and temporal plausibility for ACE2, TIMP1, PIIINP and MMP-9 in the pathogenesis of PCC. Intersection of these independent systems is uncommon and may in part explain the rarity of PCC.

Evolutionary Invasion Analysis of Modern Epidemics Highlights the Context-Dependence of Virulence Evolution

Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mechanics underlying epidemics, we lack ways to reliably predict how the relationship between virulence (the harm to hosts caused by an infection) and transmission will evolve in certain virus-host contexts. In this study, we invoke evolutionary invasion analysis-a method used to identify the evolution of uninvadable strategies in dynamical systems-to examine how the virulence-transmission dichotomy can evolve in models of virus infections defined by different natural histories. We reveal peculiar patterns of virulence evolution between epidemics with different disease natural histories (SARS-CoV-2 and hepatitis C virus). We discuss the findings with regards to the public health implications of predicting virus evolution, and in broader theoretical canon involving virulence evolution in host-parasite systems.

In Vivo Delivery of Spherical and Cylindrical In Vitro Reconstituted Virus-like Particles Containing the Same Self-Amplifying mRNA

The dramatic effectiveness of recent mRNA (mRNA)-based COVID vaccines delivered in lipid nanoparticles has highlighted the promise of mRNA therapeutics in general. In this report, we extend our earlier work on self-amplifying mRNAs delivered in spherical in vitro reconstituted virus-like particles (VLPs), and on drug delivery using cylindrical virus particles. In particular, we carry out separate in vitro assemblies of a self-amplifying mRNA gene in two different virus-like particles: one spherical, formed with the capsid protein of cowpea chlorotic mottle virus (CCMV), and the other cylindrical, formed from the capsid protein of tobacco mosaic virus (TMV). The mRNA gene is rendered self-amplifying by genetically fusing it to the RNA-dependent RNA polymerase (RdRp) of Nodamura virus, and the relative efficacies of cell uptake and downstream protein expression resulting from their CCMV- and TMV-packaged forms are compared directly. This comparison is carried out by their transfections into cells in culture: expressions of two self-amplifying genes, enhanced yellow fluorescent protein (EYFP) and Renilla luciferase (Luc), packaged alternately in CCMV and TMV VLPs, are quantified by fluorescence and chemiluminescence levels, respectively, and relative numbers of the delivered mRNAs are measured by quantitative real-time PCR. The cellular uptake of both forms of these VLPs is further confirmed by confocal microscopy of transfected cells. Finally, VLP-mediated delivery of the self-amplifying-mRNA in mice following footpad injection is shown by in vivo fluorescence imaging to result in robust expression of EYFP in the draining lymph nodes, suggesting the potential of these plant virus-like particles as a promising mRNA gene and vaccine delivery modality. These results establish that both CCMV and TMV VLPs can deliver their in vitro packaged mRNA genes to immune cells and that their self-amplifying forms significantly enhance in situ expression. Choice of one VLP (CCMV or TMV) over the other will depend on which geometry of nucleocapsid is self-assembled more efficiently for a given length and sequence of RNA, and suggests that these plant VLP gene delivery systems will prove useful in a wide variety of medical applications, both preventive and therapeutic.

Assessment of fever screening at airports in detecting domestic passengers infected with SARS-CoV-2, 2020-2022, Okinawa prefecture, Japan

BACKGROUND

While airport screening measures for COVID-19 infected passengers at international airports worldwide have been greatly relaxed, observational studies evaluating fever screening alone at airports remain scarce. The purpose of this study is to retrospectively assess the effectiveness of fever screening at airports in preventing the influx of COVID-19 infected persons.

METHODS

We conducted a retrospective epidemiological analysis of fever screening implemented at 9 airports in Okinawa Prefecture from May 2020 to March 2022. The number of passengers covered during the same period was 9,003,616 arriving at 9 airports in Okinawa Prefecture and 5,712,983 departing passengers at Naha Airport. The capture rate was defined as the proportion of reported COVID-19 cases who would have passed through airport screening to the number of suspected cases through fever screening at the airport, and this calculation used passengers arriving at Naha Airport and surveillance data collected by Okinawa Prefecture between May 2020 and March 2021.

RESULTS

From May 2020 to March 2021, 4.09 million people were reported to pass through airports in Okinawa. During the same period, at least 122 people with COVID-19 infection arrived at the airports in Okinawa, but only a 10 suspected cases were detected; therefore, the capture rate is estimated to be up to 8.2% (95% CI: 4.00-14.56%). Our result of a fever screening rate is 0.0002% (95%CI: 0.0003-0.0006%) (10 suspected cases /2,971,198 arriving passengers). The refusal rate of passengers detected by thermography who did not respond to temperature measurements was 0.70% (95% CI: 0.19-1.78%) (4 passengers/572 passengers).

CONCLUSIONS

This study revealed that airport screening based on thermography alone missed over 90% of COVID-19 infected cases, indicating that thermography screening may be ineffective as a border control measure. The fact that only 10 febrile cases were detected after screening approximately 3 million passengers suggests the need to introduce measures targeting asymptomatic infections, especially with long incubation periods. Therefore, other countermeasures, e.g. preboarding RT-PCR testing, are highly recommended during an epidemic satisfying World Health Organization (WHO) Public Health Emergency of International Concern (PHEIC) criteria with pathogen characteristics similar or exceeding SARS-CoV-2, especially when traveling to rural cities with limited medical resources.

Lessons should be learned: Why did we not learn from the Spanish flu?

COVID-19 has become a global pandemic that has affected millions of people worldwide. The disease is caused by the novel coronavirus that was first reported in Wuhan, China, in December 2019. The virus is highly contagious and can spread from person to person through respiratory droplets when an infected person coughs, sneezes, talks, or breathes. The symptoms of COVID-19 include fever, cough, and shortness of breath, and in severe cases, it can lead to respiratory failure, pneumonia, and death. The Spanish flu, caused by the H1N1 influenza virus, and the COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 are two of the most significant global health crises in history. While these two pandemics occurred almost a century apart and are caused by different types of viruses, there are notable similarities in their impact, transmission, and public health responses. Here are some key similarities between the Spanish flu and SARS-CoV-2. The Spanish flu pandemic of 1918-1919 stands as one of the deadliest pandemics in human history, claiming the lives of an estimated 50 million people worldwide. Its impact reverberated across continents, leaving behind a legacy of devastation and lessons that, unfortunately, seem to have been forgotten or ignored over time. Despite the advancements in science, medicine, and public health in the intervening century, humanity found itself facing a strikingly similar situation with the outbreak of the COVID-19 pandemic. Additionally, amidst the search for effective measures to combat COVID-19, novel approaches such as iodine complexes, such as Iodine-V has emerged as potential interventions, reflecting the ongoing quest for innovative solutions to mitigate the impact of pandemics. This raises the poignant question: why did we not learn from the Spanish flu?

Measuring SARS-CoV-2 RNA concentrations in neighborhood wastewater

Longitudinal wastewater sampling during the COVID-19 pandemic was an important aspect of disease surveillance, adding to a more complete understanding of infection dynamics and providing important data for community public health monitoring and intervention planning. This was largely accomplished by testing SARS-CoV-2 RNA concentrations in samples from municipal wastewater treatment plants (WWTPs). We evaluated the utility of testing for virus levels upstream from WWTP within the residential neighborhoods that feed into the WWTP. We propose that monitoring virus dynamics across residential neighborhoods could reveal important public health-relevant information about community sub-group heterogeneity in virus concentrations. PRINCIPAL RESULTS: Virus concentration patterns display heterogeneity within neighborhoods and between neighborhoods over time. Sewage SARS-CoV-2 RNA concentrations as measured by RT-qPCR also corresponded closely to verified COVID-19 infection counts within individual neighborhoods. More importantly, our data suggest the loss of disease-relevant public health information when sampling occurs only at the level of WWTP instead of upstream in neighborhoods. Spikes in SARS-CoV-2 RNA concentrations in neighborhoods are often masked by dilution from other neighborhoods in the WWTP samples. MAJOR CONCLUSIONS: Wastewater-based epidemiology (WBE) employed at WWTP reliably detects SARS-CoV-2 in a city-sized population but provides less actionable public health information about neighborhoods experiencing greater viral infection and disease. Neighborhood sewershed sampling reveals important population-based information about local virus dynamics and improves opportunities for public health intervention. Longitudinally employed, neighborhood sewershed surveillance may provide a 3-6 day early warning of SARS-CoV-2 infection spikes and, importantly, highly specific information on subpopulations in a community particularly at higher risk at different points in time. Sampling in neighborhoods may thus provide timely and cost-saving information for targeted interventions within communities.

Association between meteorological variations and the superspreading potential of SARS-CoV-2 infections

BACKGROUND

While many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people.

METHODS

Contact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures.

RESULTS

A total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: -0.239 [95 % CI: -0.431 to -0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt.

CONCLUSIONS

This study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.

Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model

Accurate short-term predictions of COVID-19 cases with empirical models allow Health Officials to prepare for hospital contingencies in a two-three week window given the delay between case reporting and the admission of patients in a hospital. We investigate the ability of Gompertz-type empiric models to provide accurate prediction up to two and three weeks to give a large window of preparation in case of a surge in virus transmission. We investigate the stability of the prediction and its accuracy using bi-weekly predictions during the last trimester of 2020 and 2021. Using data from 2020, we show that understanding and correcting for the daily reporting structure of cases in the different countries is key to accomplish accurate predictions. Furthermore, we found that filtering out predictions that are highly unstable to changes in the parameters of the model, which are roughly 20%, reduces strongly the number of predictions that are way-off. The method is then tested for robustness with data from 2021. We found that, for this data, only 1-2% of the one-week predictions were off by more than 50%. This increased to 3% for two-week predictions, and only for three-week predictions it reached 10%.

Seroprevalence of SARS-CoV-2 IgG in blood donors in a teaching institute from Western part of Maharashtra

Introduction

COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 that has appeared as a global pandemic in recent times. Currently, the transmission rate has slowed down significantly, but the definite pathological reason behind this is still unknown. Therefore, the prevalence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody must be studied to establish the relation between the rate of transmission and antibody presence.

Materials and Methods

A clinical assessment was performed to evaluate the seroprevalence of SARS-CoV-2 Immunoglobulin G (IgG) antibodies among 299 healthy volunteers in the period of February to May 2021. Serum samples were analyzed using chemiluminescent microparticle immunoassay (CMIA) technology to detect the presence of IgG antibodies.

Result

It was observed that 21% of the participants were seropositive, and 78% of the population was seronegative across the different genders. This confirmed that the generation of antibodies is independent of gender. Simultaneously, a t-test was performed that further suggested no statistical correlation between gender and seroprevalence. Moreover, a comprehensive analysis was performed to establish the relation between age and blood group with the seroprevalence. However, there was no statistical relationship found among these parameters.

Conclusion

This study assisted in examining the underlying causes of high or low seroprevalence among healthy volunteers.

Diagnostics for Public Health - Infectious Disease Surveillance and Control

AbstractAccurate diagnostics are critical in public health to ensure successful disease tracking, prevention, and control. Many of the same characteristics are desirable for diagnostic procedures in both medicine and public health: for example, low cost, high speed, low invasiveness, ease of use and interpretation, day-to-day consistency, and high accuracy. This review lays out five principles that are salient when the goal of diagnosis is to improve the overall health of a population rather than that of a particular patient, and it applies them in two important use cases: pandemic infectious disease and antimicrobial resistance.

Modeling the health impact of increasing vaccine coverage and nonpharmaceutical interventions against coronavirus disease 2019 in Ghana

Seroprevalence studies assessing community exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Ghana concluded that population-level immunity remained low as of February 2021. Thus, it is important to demonstrate how increasing vaccine coverage reduces the economic and public health impacts associated with SARS-CoV-2 transmission. To that end, this study used a Susceptible-Exposed-Presymptomatic-Symptomatic-Asymptomatic-Recovered-Dead-Vaccinated compartmental model to simulate coronavirus disease 2019 (COVID-19) transmission and the role of public health interventions in Ghana. The impact of increasing vaccination rates and decline in transmission rates due to nonpharmaceutical interventions (NPIs) on cumulative infections and deaths averted was explored under different scenarios. Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) was used to investigate the uncertainty and sensitivity of the outcomes to the parameters. Simulation results suggest that increasing the vaccination rate to achieve 50% coverage was associated with almost 60,000 deaths and 25 million infections averted. In comparison, a 50% decrease in the transmission coefficient was associated with the prevention of about 150,000 deaths and 50 million infections. The LHS-PRCC results indicated that in the context of vaccination rate, cumulative infections and deaths averted were most sensitive to vaccination rate, waning immunity rates from vaccination, and waning immunity from natural infection. This study's findings illustrate the impact of increasing vaccination coverage and/or reducing the transmission rate by NPI adherence in the prevention of COVID-19 infections and deaths in Ghana.

Early predictors of intensive care unit admission among COVID-19 patients in Qatar

Background

COVID-19 is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission among patients with COVID-19.

Methods

This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29-May 29, 2020. For each case enrolled, one control was matched by age and gender.

Results

A total of 1,560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (adjusted odds ratio (aOR) = 1.64, 95% confidence interval (CI): 1.16-2.32, p = 0.005), diabetes (aOR = 1.52, 95% CI: 1.08-2.13, p = 0.016), obesity (aOR = 1.46, 95% CI: 1.03-2.08, p = 0.034), lymphopenia (aOR = 2.69, 95% CI: 1.80-4.02, p < 0.001), high AST (aOR = 2.59, 95% CI: 1.53-4.36, p < 0.001), high ferritin (aOR = 1.96, 95% CI: 1.40-2.74, p < 0.001), high CRP (aOR = 4.09, 95% CI: 2.81-5.96, p < 0.001), and dyspnea (aOR = 2.50, 95% CI: 1.77-3.54, p < 0.001).

Conclusion

Having cardiovascular disease, diabetes, obesity, lymphopenia, dyspnea, and increased AST, ferritin, and CRP were independent predictors for ICU admission in patients with COVID-19.

Stimulation of PSTPIP1 to trigger proinflammatory responses in asymptomatic SARS-CoV-2 infections

Background

A hyperinflammatory response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection gravely worsens the clinical progression of coronavirus disease 2019 (COVID-19). Although the undesirable effects of inflammasome activation have been correlated to the severity of COVID-19, the mechanisms of this process in the asymptomatic infection and disease progression have not yet been clearly elucidated.

Methods

We performed strand-specific RNA sequencing in 39 peripheral blood mononuclear cell (PBMC) samples from asymptomatic individuals（n = 10）, symptomatic patients（n = 16） and healthy donors（n = 13）.

Results

Dysregulation of pyrin inflammasomes along with the proline-serine-threonine phosphatase-interacting protein 1 (PSTPIP1) gene was identified in SARS-COV-2 infection. Notably, the PSTPIP1 expression level showed a significant negative correlation with an adjacent long-noncoding RNA (lncRNA) RP11-797A18.6 in the asymptomatic individuals compared with the healthy controls. In addition, a decline in the nuclear factor kappa B subunit 1 (NFKB1) gene expression was observed in asymptomatic infection, followed by a rise in the mild and moderate disease stages, suggesting that altered NFKB1 expression and associated proinflammatory signals may trigger a disease progression.

Conclusions

Overall, our results indicate that PSTPIP1-dependent pyrin inflammasomes-mediated pyroptosis and NF-κB activation might be potential preventive targets for COVID-19 disease development and progression.

Effects of Face Mask Mandates on COVID-19 Transmission in 51 Countries: Retrospective Event Study

BACKGROUND

The question of the utility of face masks in preventing acute respiratory infections has received renewed attention during the COVID-19 pandemic. However, given the inconclusive evidence from existing randomized controlled trials, evidence based on real-world data with high external validity is missing.

OBJECTIVE

To add real-world evidence, this study aims to examine whether mask mandates in 51 countries and mask recommendations in 10 countries increased self-reported face mask use and reduced SARS-CoV-2 reproduction numbers and COVID-19 case growth rates.

METHODS

We applied an event study approach to data pooled from four sources: (1) country-level information on self-reported mask use was obtained from the COVID-19 Trends and Impact Survey, (2) data from the Oxford COVID-19 Government Response Tracker provided information on face mask mandates and recommendations and any other nonpharmacological interventions implemented, (3) mobility indicators from Google's Community Mobility Reports were also included, and (4) SARS-CoV-2 reproduction numbers and COVID-19 case growth rates were retrieved from the Our World in Data-COVID-19 data set.

RESULTS

Mandates increased mask use by 8.81 percentage points (P=.006) on average, and SARS-CoV-2 reproduction numbers declined on average by -0.31 units (P=.008). Although no significant average effect of mask mandates was observed for growth rates of COVID-19 cases (-0.98 percentage points; P=.56), the results indicate incremental effects on days 26 (-1.76 percentage points; P=.04), 27 (-1.89 percentage points; P=.05), 29 (-1.78 percentage points; P=.04), and 30 (-2.14 percentage points; P=.02) after mandate implementation. For self-reported face mask use and reproduction numbers, incremental effects are seen 6 and 13 days after mandate implementation. Both incremental effects persist for >30 days. Furthermore, mask recommendations increased self-reported mask use on average (5.84 percentage points; P<.001). However, there were no effects of recommendations on SARS-CoV-2 reproduction numbers or COVID-19 case growth rates (-0.06 units; P=.70 and -2.45 percentage points; P=.59). Single incremental effects on self-reported mask use were observed on days 11 (3.96 percentage points; P=.04), 13 (3.77 percentage points; P=.04) and 25 to 27 (4.20 percentage points; P=.048 and 5.91 percentage points; P=.01) after recommendation. Recommendations also affected reproduction numbers on days 0 (-0.07 units; P=.03) and 1 (-0.07 units; P=.03) and between days 21 (-0.09 units; P=.04) and 28 (-0.11 units; P=.05) and case growth rates between days 1 and 4 (-1.60 percentage points; P=.03 and -2.19 percentage points; P=.03) and on day 23 (-2.83 percentage points; P=.05) after publication.

CONCLUSIONS

Contrary to recommendations, mask mandates can be used as an effective measure to reduce SARS-CoV-2 reproduction numbers. However, mandates alone are not sufficient to reduce growth rates of COVID-19 cases. Our study adds external validity to the existing randomized controlled trials on the effectiveness of face masks to reduce the spread of SARS-CoV-2.

COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina

We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.

Mitigating the COVID-19 Pandemic through Data-Driven Resource Sharing

COVID-19 outbreaks in local communities can result in a drastic surge in demand for scarce resources such as mechanical ventilators. To deal with such demand surges, many hospitals (1) purchased large quantities of mechanical ventilators, and (2) canceled/postponed elective procedures to preserve care capacity for COVID-19 patients. These measures resulted in a substantial financial burden to the hospitals and poor outcomes for non-COVID-19 patients. Given that COVID-19 transmits at different rates across various regions, there is an opportunity to share portable healthcare resources to mitigate capacity shortages triggered by local outbreaks with fewer total resources. This paper develops a novel data-driven adaptive robust simulation-based optimization (DARSO) methodology for optimal allocation and relocation of mechanical ventilators over different states and regions. Our main methodological contributions lie in a new policy-guided approach and an efficient algorithmic framework that mitigates critical limitations of current robust and stochastic models and make resource-sharing decisions implementable in real-time. In collaboration with epidemiologists and infectious disease doctors, we give proof of concept for the DARSO methodology through a case study of sharing ventilators among regions in Ohio and Michigan. The results suggest that our optimal policy could satisfy ventilator demand during the first pandemic's peak in Ohio and Michigan with 14% (limited sharing) to 63% (full sharing) fewer ventilators compared to a no sharing strategy (status quo), thereby allowing hospitals to preserve more elective procedures. Furthermore, we demonstrate that sharing unused ventilators (rather than purchasing new machines) can result in 5% (limited sharing) to 44% (full sharing) lower expenditure, compared to no sharing, considering the transshipment and new ventilator costs.

Silent battles: immune responses in asymptomatic SARS-CoV-2 infection

SARS-CoV-2 infections manifest with a broad spectrum of presentations, ranging from asymptomatic infections to severe pneumonia and fatal outcomes. This review centers on asymptomatic infections, a widely reported phenomenon that has substantially contributed to the rapid spread of the pandemic. In such asymptomatic infections, we focus on the role of innate, humoral, and cellular immunity. Notably, asymptomatic infections are characterized by an early and robust innate immune response, particularly a swift type 1 IFN reaction, alongside a rapid and broad induction of SARS-CoV-2-specific T cells. Often, antibody levels tend to be lower or undetectable after asymptomatic infections, suggesting that the rapid control of viral replication by innate and cellular responses might impede the full triggering of humoral immunity. Even if antibody levels are present in the early convalescent phase, they wane rapidly below serological detection limits, particularly following asymptomatic infection. Consequently, prevalence studies reliant solely on serological assays likely underestimate the extent of community exposure to the virus.

Evaluation of Genetic or Cellular Impairments in Type I IFN Immunity in a Cohort of Young Adults with Critical COVID-19

Several genetic and immunological risk factors for severe COVID-19 have been identified, with monogenic conditions relating to 13 genes of type I interferon (IFN) immunity proposed to explain 4.8% of critical cases. However, previous cohorts have been clinically heterogeneous and were not subjected to thorough genetic and immunological analyses. We therefore aimed to systematically investigate the prevalence of rare genetic variants causing inborn errors of immunity (IEI) and functionally interrogate the type I IFN pathway in young adults that suffered from critical COVID-19 yet lacked comorbidities. We selected and clinically characterized a cohort of 38 previously healthy individuals under 50 years of age who were treated in intensive care units due to critical COVID-19. Blood samples were collected after convalescence. Two patients had IFN-α autoantibodies. Genome sequencing revealed very rare variants in the type I IFN pathway in 31.6% of the patients, which was similar to controls. Analyses of cryopreserved leukocytes did not indicate any defect in plasmacytoid dendritic cell sensing of TLR7 and TLR9 agonists in patients carrying variants in these pathways. However, lymphocyte STAT phosphorylation and protein upregulation upon IFN-α stimulation revealed three possible cases of impaired type I IFN signaling in carriers of rare variants. Together, our results suggest a strategy of functional screening followed by genome analyses and biochemical validation to uncover undiagnosed causes of critical COVID-19.

Epidemiology of COVID-19

This chapter provides a detailed exploration of the epidemiology of COVID-19, focusing on several key aspects that offer valuable insights into the disease progression. A comprehensive comparison is made between the three related coronaviruses: SARS-CoV, MERS-CoV, and SARS-CoV-2, elucidating their similarities and differences in terms of transmission dynamics, clinical presentation, laboratory and radiological findings, infection mechanisms, and mortality rates. The concept of herd immunity is then discussed, exploring its relevance and potential implications for controlling the spread of COVID-19. Next, the chapter delves into the changing epidemiology of the disease, examining how various factors such as human behavior, public health interventions, and viral mutations have influenced its transmission patterns and severity over time. Finally, the timelines and evolution of COVID-19 are outlined, tracing the origins of the virus, its rapid global spread, and the emergence of new variants.

Estimating Hidden Population Size of COVID-19 using Respondent-Driven Sampling Method - A Systematic Review

INTRODUCTION

Currently, the ongoing COVID-19 pandemic is posing a challenge to health systems worldwide. Unfortunately, the true number of infections is underestimated due to the existence of a vast number of asymptomatic infected individual's proportion. Detecting the actual number of COVID-19-affected patients is critical in order to treat and prevent it. Sampling of such populations, so-called hidden or hard-to-reach populations, is not possible using conventional sampling methods. The objective of this research is to estimate the hidden population size of COVID-19 by using respondent-driven sampling (RDS) methods.

METHODS

This study is a systematic review. We have searched online databases of PubMed, Web of Science, Scopus, Embase, and Cochrane to identify English articles published from the beginning of December 2019 to December 2022 using purpose-related keywords. The complete texts of the final chosen articles were thoroughly reviewed, and the significant findings are condensed and presented in the table.

RESULTS

Of the 7 included articles, all were conducted to estimate the actual extent of COVID-19 prevalence in their region and provide a mathematical model to estimate the asymptomatic and undetected cases of COVID-19 amid the pandemic. Two studies stated that the prevalence of COVID-19 in their sample population was 2.6% and 2.4% in Sierra Leone and Austria, respectively. In addition, four studies stated that the actual numbers of infected cases in their sample population were significantly higher, ranging from two to 50 times higher than the recorded reports.

CONCLUSIONS

In general, our study illustrates the efficacy of RDS in the estimation of undetected asymptomatic cases with high cost-effectiveness due to its relatively trouble-free and low-cost methods of sampling the population. This method would be valuable in probable future epidemics.

Quantifying the presymptomatic transmission of COVID-19 in the USA

The emergence of many presymptomatic hidden transmission events significantly complicated the intervention and control of the spread of COVID-19 in the USA during the year 2020. To analyze the role that presymptomatic infections play in the spread of this disease, we developed a state-level metapopulation model to simulate COVID-19 transmission in the USA in 2020 during which period the number of confirmed cases was more than in any other country. We estimated that the transmission rate (i.e., the number of new infections per unit time generated by an infected individual) of presymptomatic infections was approximately 59.9% the transmission rate of reported infections. We further estimated that {at any point in time the} average proportion of infected individuals in the presymptomatic stage was consistently over 50% of all infected individuals. Presymptomatic transmission was consistently contributing over 52% to daily new infections, as well as consistently contributing over 50% to the effective reproduction number from February to December. Finally, non-pharmaceutical intervention targeting presymptomatic infections was very effective in reducing the number of reported cases. These results reveal the significant contribution that presymptomatic transmission made to COVID-19 transmission in the USA during 2020, as well as pave the way for the design of effective disease control and mitigation strategies.

Mitigation through on-site testing & education among formerly incarcerated individuals against Covid-19 - The MOSAIC study: Design and rationale

BACKGROUND

Many of the largest COVID-19 outbreaks in the United States have occurred at carceral facilities. Criminal legal system (CLS)-involved individuals typically face structural barriers accessing medical care post-release. Improving COVID-19 testing and education for CLS-involved individuals could improve health outcomes for this vulnerable population and the communities to which they return. Community-based organizations (CBO) and community health workers (CHWs) fill care gaps by connecting CLS-involved individuals with essential re-entry services. The MOSAIC study will: 1) test an onsite CHW-led SARS-CoV-2 testing and education intervention in a reentry CBO and 2) model the cost-effectiveness of this intervention compared to standard care.

METHODS

We will recruit 250 CLS-involved individuals who have left incarceration in the prior 90 days. Participants will be randomized to receive onsite Point-of-Care testing and education (O-PoC) or Standard of Care (SoC). Over one year, participants will complete quarterly questionnaires and biweekly short surveys through a mobile application, and be tested for SARS-CoV-2 quarterly, either at the CBO (O-PoC) or an offsite community testing site (SoC). O-PoC will also receive COVID-19 mitigation counseling and education from the CHW. Our primary outcome is the proportion of SARS-CoV-2 tests performed with results received by participants. Secondary outcomes include adherence to mitigation behaviors and cost-effectiveness of the intervention.

DISCUSSION

The MOSAIC study will offer insight into cost effective strategies for SARS-CoV-2 testing and education for CLS-involved individuals. The study will also contribute to the growing literature on CHW's role in health education, supportive counseling, and building trust between patients and healthcare organizations.

Modelling flock heterogeneity in the transmission of peste des petits ruminants virus and its impact on the effectiveness of vaccination for eradication

Peste des petits ruminants (PPR) is an acute infectious disease of small ruminants targeted for global eradication by 2030. The Global Strategy for Control and Eradication (GSCE) recommends mass vaccination targeting 70% coverage of small ruminant populations in PPR-endemic regions. These small ruminant populations are diverse with heterogeneous mixing patterns that may influence PPR virus (PPRV) transmission dynamics. This paper evaluates the impact of heterogeneous mixing on (i) PPRV transmission and (ii) the likelihood of different vaccination strategies achieving PPRV elimination, including the GSCE recommended strategy. We develop models simulating heterogeneous transmission between hosts, including a metapopulation model of PPRV transmission between villages in lowland Ethiopia fitted to serological data. Our results demonstrate that although heterogeneous mixing of small ruminant populations increases the instability of PPRV transmission-increasing the chance of fadeout in the absence of intervention-a vaccination coverage of 70% may be insufficient to achieve elimination if high-risk populations are not targeted. Transmission may persist despite very high vaccination coverage (>90% small ruminants) if vaccination is biased towards more accessible but lower-risk populations such as sedentary small ruminant flocks. These results highlight the importance of characterizing small ruminant mobility patterns and identifying high-risk populations for vaccination and support a move towards targeted, risk-based vaccination programmes in the next phase of the PPRV eradication programme. Our modelling approach also illustrates a general framework for incorporating heterogeneous mixing patterns into models of directly transmitted infectious diseases where detailed contact data are limited. This study improves understanding of PPRV transmission and elimination in heterogeneous small ruminant populations and should be used to inform and optimize the design of PPRV vaccination programmes.

Nasopharyngeal Viral Load Is the Major Driver of Incident Antibody Immune Response to SARS-CoV-2 Infection

Background

Virologic determinants of seroconversion to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were defined in a post hoc analysis of prospectively studied vaccine- and infection-naïve individuals at high risk for coronavirus disease 2019 (COVID-19).

Methods

This phase 3 COVID-19 prevention trial (NCT04452318) with casirivimab and imdevimab was conducted in July 2020-February 2021, before widespread vaccine availability. Placebo-treated participants who were uninfected (SARS-CoV-2 quantitative reverse transcription polymerase chain reaction [RT-qPCR] negative) and seronegative were assessed weekly for 28 days (efficacy assessment period [EAP]) for COVID-19 symptoms and SARS-CoV-2 infection by RT-qPCR of nasopharyngeal swab samples and for serostatus by antinucleocapsid immunoglobulin (Ig) G. Regression-based modeling, including causal mediation analysis, estimated the effects of viral load on seroconversion.

Results

Of 157/1069 (14.7%) uninfected and seronegative (for antispike IgG, antispike IgA, and antinucleocapsid IgG) participants who became infected during the EAP, 105 (65%) seroconverted. The mean (SD) maximum viral load of seroconverters was 7.23 (1.68) log10 copies/mL vs 4.8 (2.2) log10 copies/mL in those who remained seronegative; viral loads of ∼6.0 log10 copies/mL better predicted seroconversion. The mean of the maximum viral load was 7.11 log10 copies/mL in symptomatic participants vs 5.58 log10 copies/mL in asymptomatic participants. The mean duration of detectable viral load was longer in seroconverted vs seronegative participants: 3.24 vs 1.63 weeks.

Conclusions

Maximum SARS-CoV-2 viral load is a major driver of seroconversion and symptomatic COVID-19, with high viral loads (∼6.0 log10 copies/mL) better predicting seroconversion. Serology underestimates infection rates, incidence, and prevalence of SARS-CoV-2 infection.

A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile

A computational approach is adapted to analyze the parameter identifiability of a compartmental model. The model is intended to describe the progression of the COVID-19 pandemic in Chile during the initial phase in early 2020 when government declared quarantine measures. The computational approach to analyze the structural and practical identifiability is applied in two parts, one for synthetic data and another for some Chilean regional data. The first part defines the identifiable parameter sets when these recover the true parameters used to create the synthetic data. The second part compares the results derived from synthetic data, estimating the identifiable parameter sets from regional Chilean epidemic data. Experiments provide evidence of the loss of identifiability if some initial conditions are estimated, the period of time used to fit is before the peak, and if a significant proportion of the population is involved in quarantine periods.

Reproduction number projection for the COVID-19 pandemic

The recently derived Hybrid-Incidence Susceptible-Transmissible-Removed (HI-STR) prototype is a deterministic compartment model for epidemics and an alternative to the Susceptible-Infected-Removed (SIR) model. The HI-STR predicts that pathogen transmission depends on host population characteristics including population size, population density and social behaviour common within that population.

The HI-STR prototype is applied to the ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) to show that the original estimates of the Coronavirus Disease 2019 (COVID-19) basic reproduction number $\mathcal{R}_{0}$ R 0 for the United Kingdom (UK) could have been projected onto the individual states of the United States of America (USA) prior to being detected in the USA.

The Imperial College London (ICL) group’s estimate of $\mathcal{R}_{0}$ R 0 for the UK is projected onto each USA state. The difference between these projections and the ICL’s estimates for USA states is either not statistically significant on the paired Student t-test or not epidemiologically significant.

The SARS-CoV2 Delta variant’s $\mathcal{R}_{0}$ R 0 is also projected from the UK to the USA to prove that projection can be applied to a Variant of Concern (VOC). Projection provides both a localised baseline for evaluating the implementation of an intervention policy and a mechanism for anticipating the impact of a VOC before local manifestation.