The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

Modeling outbreaks of COVID-19 in China: The impact of vaccination and other control measures on curbing the epidemic

This study aims to examine the development trend of COVID-19 in China and propose a model to assess the impacts of various prevention and control measures in combating the COVID-19 pandemic. Using COVID-19 cases reported by the National Health Commission of China from January 2, 2020, to January 2, 2022, we established a Susceptible-Exposed-Infected-Asymptomatic-Quarantined-Vaccinated-Hospitalized-Removed (SEIAQVHR) model to calculate the COVID-19 transmission rate and Rt effective reproduction number, and assess prevention and control measures. Additionally, we built a stochastic model to explore the development of the COVID-19 epidemic. We modeled the incidence trends in five outbreaks between 2020 and 2022. Some important features of the COVID-19 epidemic are mirrored in the estimates based on our SEIAQVHR model. Our model indicates that an infected index case entering the community has a 50%-60% chance to cause a COVID-19 outbreak. Wearing masks and getting vaccinated were the most effective measures among all the prevention and control measures. Specifically targeting asymptomatic individuals had no significant impact on the spread of COVID-19. By adjusting prevention and control parameters, we suggest that increasing the rates of effective vaccination and mask-wearing can significantly reduce COVID-19 cases in China. Our stochastic model analysis provides a useful tool for understanding the COVID-19 epidemic in China.

Assessment of bidirectional impact of stigmatization induced self-medication on COVID-19 and malaria transmissions using mathematical modeling: Nigeria as a case study

The continual social and economic impact of infectious diseases on nations has maintained sustained attention on their control and treatment, of which self-medication has been one of the means employed by some individuals. Self-medication complicates the attempt of their control and treatment as it conflicts with some of the measures implemented by health authorities. Added to these complications is the stigmatization of individuals with some diseases in some jurisdictions. This study investigates the co-infection of COVID-19 and malaria and its related deaths and further highlights how self-medication and stigmatization add to the complexities of the fight against these two diseases using Nigeria as a study case. Using a mathematical model on COVID-19 and malaria co-infection, we address the question: to what degree does the impact of the interaction between COVID-19 and malaria amplify infections and deaths induced by both diseases via self-medication and stigmatization? We demonstrate that COVID-19 related self-medication due to misdiagnoses contributes substantially to the prevalence of disease. The control reproduction numbers for these diseases and quantification of model parameters uncertainties and sensitivities are presented.

Revisiting the dimensions of universal vaccine with special focus on COVID-19: Efficacy versus methods of designing

Even though the use of SARS-CoV-2 vaccines during the COVID-19 pandemic showed unprecedented success in a short time, it also exposed a flaw in the current vaccine design strategy to offer broad protection against emerging variants of concern. However, developing broad-spectrum vaccines is still a challenge for immunologists. The development of universal vaccines against emerging pathogens and their variants appears to be a practical solution to mitigate the economic and physical effects of the pandemic on society. Very few reports are available to explain the basic concept of universal vaccine design and development. This review provides an overview of the innate and adaptive immune responses generated against vaccination and essential insight into immune mechanisms helpful in designing universal vaccines targeting influenza viruses and coronaviruses. In addition, the characteristics, safety, and factors affecting the efficacy of universal vaccines have been discussed. Furthermore, several advancements in methods worthy of designing universal vaccines are described, including chimeric immunogens, heterologous prime-boost vaccines, reverse vaccinology, structure-based antigen design, pan-reactive antibody vaccines, conserved neutralizing epitope-based vaccines, mosaic nanoparticle-based vaccines, etc. In addition to the several advantages, significant potential constraints, such as defocusing the immune response and subdominance, are also discussed.

SARS-CoV-2 Transmission during High-Altitude Field Studies

Grimm, Mirjam, Lucie Ziegler, Annina Seglias, Maamed Mademilov, Kamila Magdieva, Gulzada Mirzalieva, Aijan Taalaibekova, Simone Suter, Simon R. Schneider, Fiona Zoller, Vera Bissig, Lukas Reinhard, Meret Bauer, Julian Müller, Tanja L. Ulrich, Arcangelo F. Carta, Patrick R. Bader, Konstantinos Bitos, Aurelia E. Reiser, Benoit Champigneulle, Damira Ashyralieva, Philipp M. Scheiwiller, Silvia Ulrich, Talant M. Sooronbaev, Michael Furian, and Konrad E. Bloch. SARS-CoV-2 Transmission during High-Altitude Field Studies. High Alt Med Biol. 00:00-00, 2024. Background: Throughout the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic, virus transmission during clinical research was of concern. Therefore, during high-altitude field studies performed in 2021, we took specific COVID-19 precautions and investigated the occurrence of SARS-CoV-2 infection. Methods: From May to September 2021, we performed studies in patients with chronic obstructive pulmonary disease (COPD) and in healthy school-age children in Kyrgyzstan in high-altitude facilities at 3,100 m and 3,250 m and at 760 m. The various implemented COVID-19 safety measures included systematic SARS-CoV-2 rapid antigen testing (RAT). Main outcomes were SARS-CoV-2-RAT-positive rate among participants and staff at initial presentation (prevalence) and SARS-CoV-2-RAT-positive conversion during and within 10 days after studies (incidence). Results: Among 338 participants and staff, SARS-CoV-2-RAT-positive prevalence was 15 (4.4%). During mean ± SD duration of individual study participation of 3.1 ± 1.0 day and within 10 days, RAT-positive conversion occurred in 1/237(0.4%) participants. Among staff working in studies for 31.5 ± 29.3 days, SARS-CoV-2-RAT-positive conversion was 11/101(10.9%). In all 338 individuals involved in the studies over the course of 15.6 weeks, the median SARS-CoV-2-RAT-positive incidence was 0.00%/week (quartiles 0.00; 0.64). Over the same period, the median background incidence among the total Kyrgyz population of 6,636 million was 0.06%/week (0.03; 0.11), p = 0.013 (Wilcoxon rank sum test). Conclusions: Taking precautions by implementing specific safety measures, SARS-CoV-2 transmission during clinical studies was very rare, and the SARS-CoV-2 incidence among participants and staff was lower than that in the general population during the same period. The results are reassuring and may help in decision-making on the conduct of clinical research in similar settings.

Mathematical analysis of simple behavioral epidemic models

COVID-19 highlighted the importance of considering human behavior change when modeling disease dynamics. This led to developing various models that incorporate human behavior. Our objective is to contribute to an in-depth, mathematical examination of such models. Here, we consider a simple deterministic compartmental model with endogenous incorporation of human behavior (i.e., behavioral feedback) through transmission in a classic Susceptible-Exposed-Infectious-Recovered (SEIR) structure. Despite its simplicity, the SEIR structure with behavior (SEIRb) was shown to perform well in forecasting, especially compared to more complicated models. We contrast this model with an SEIR model that excludes endogenous incorporation of behavior. Both models assume permanent immunity to COVID-19, so we also consider a modification of the models which include waning immunity (SEIRS and SEIRSb). We perform equilibria, sensitivity, and identifiability analyses on all models and examine the fidelity of the models to replicate COVID-19 data across the United States. Endogenous incorporation of behavior significantly improves a model's ability to produce realistic outbreaks. While the two endogenous models are similar with respect to identifiability and sensitivity, the SEIRSb model, with the more accurate assumption of the waning immunity, strengthens the initial SEIRb model by allowing for the existence of an endemic equilibrium, a realistic feature of COVID-19 dynamics. When fitting the model to data, we further consider the addition of simple seasonality affecting disease transmission to highlight the explanatory power of the models.

Optimal approaches for COVID-19 control: the use of vaccines and lockdowns across societal groups

Background

By March 2023, the COVID-19 illness had caused over 6.8 million deaths globally. Countries restricted disease spread through non-pharmaceutical interventions (NPIs; e.g. social distancing). More severe "lockdowns" were also required to manage disease spread. Although lockdowns effectively reduce virus transmission, they substantially disrupt economies and individual well-being. Fortunately, the availability of vaccines provides alternative approaches to manage disease spread. Yet, vaccination programs take several months to implement fully, require further time for individuals to develop immunity following inoculation, may not have complete coverage and/or may be imperfectly efficacious against the virus. Given these aspects of a vaccination programme, it is important to understand how NPIs (such as lockdowns) can be used in conjunction with vaccination to achieve public health goals.

Methods

We use mathematical methods to, investigate optimal approaches for vaccination under varying lockdown lengths and/or severities to prevent COVID-19-related deaths exceeding critical thresholds.

Results

We find that increases in vaccination rate cause a disproportionate decrease in the length and severity lockdowns to keep mortality levels below a critical threshold. With vaccination, severe lockdowns can further reduce infections by up to 89%. Notably, we include simple demographics, modelling three groups: vulnerable, front-line workers, and non-vulnerable. We investigate the sequence of vaccination. One counter-intuitive finding is that even though the vulnerable group is high risk, demographically, this is a small group and critically, per person, vaccination therefore occurs more slowly. Hence vaccinating this group first achieves limited gains in overall disease control.

Discussion

Importantly, we conclude that improved disease control may be best achieved by vaccinating the non-vulnerable group coupled with longer and/or more severe NPIs.

Influence of easing COVID-19 strategies following downgrading of the national infectious disease category on COVID-19 occurrence among hospitalized patients in Japan

PURPOSE

We aimed to evaluate the influence of easing COVID-19 preventive measures following its downgrading to a common infectious disease on COVID-19 occurrence among hospitalized patients.

METHODS

Nosocomial occurrence of COVID-19 was compared between periods with national infectious disease category 5 versus the preceding category 2 equivalent. Changes in the revised manual included a shorter duration of work restrictions for infected health care practitioners (HCPs); no work restriction for HCPs exposed to SARS-CoV-2 with a negative test on days 1, 3 and 5; discontinuation of universal pre-admission screening; and pre-emptive isolation of patients without screening. Wearing an N95 mask and face shield was required in procedure/care with moderate-to high-risk contact.

RESULTS

Although the mean monthly number of infected HCPs increased from 8.1 to 12.7 in the category 5 period (p = 0.034) and that of pre-admission screening decreased to one-fourth, the COVID-19 incidence in hospitalized patients remained similar between the two study periods (1.60 ± 5.59/month versus 1.40 ± 2.63/month, p = 0.358). Clusters, defined as ≥3 COVID-19 patients on the ward, were experienced twice in the preceding period and only once in the category 5 period. The index cases causing nosocomial SARS-CoV-2 transmission mostly involved rehabilitation therapists in the preceding period; five of six index cases were patients in the category 5 period. Following the expanded indication for N95 masks, neither SARS-CoV-2 transmission to patients nor transmission from infected patients was observed in HCPs for 1 year.

CONCLUSION

With sustained, enhanced standard precautions, easing prevention strategies could limit nosocomial SARS-CoV-2 infections.

Early Detection of COVID-19 in Female Athletes Using Wearable Technology

BACKGROUND

Heart rate variability (HRV), respiratory rate (RR), and resting heart rate (RHR) are common variables measured by wrist-worn activity trackers to monitor health, fitness, and recovery in athletes. Variations in RR are observed in lower-respiratory infections, and preliminary data suggest changes in HRV and RR are linked to early detection of COVID-19 infection in nonathletes.

HYPOTHESIS

Wearable technology measuring HRV, RR, RHR, and recovery will be successful for early detection of COVID-19 in NCAA Division I female athletes.

STUDY DESIGN

Cohort study.

LEVEL OF EVIDENCE

Level 2.

METHODS

Female athletes wore WHOOP, Inc. bands through the 2020 to 2021 competitive season. Of the athletes who tested positive for COVID (n = 33), 14 had enough data to be assessed (N = 14; 20.0 ± 1.3 years; 69.8 ± 7.2 kg; 172.0 ± 8.3 cm). Roughly 2 weeks of noninfected days were used to set baseline levels of HRV, RR, recovery, and RHR to compare with -3, -2, and -1 days before a positive COVID-19 result.

RESULTS

Increases in RR (P = 0.02) were detected on day -3. RHR (P < 0.01) and RR increased (P < 0.01), while HRV decreased (P < 0.05) on day -1, compared with baseline. Differences were noted in all variables on the day of the positive COVID-19 result: decreased HRV (P < 0.05) and recovery scores (P < 0.01), and increased RHR (P < 0.01) and RR (P < 0.01).

CONCLUSION

In female athletes, wearable technology was successful in predicting COVID-19 infection through changes in RR 3 days before a positive test, and also HRV and RHR the day before a positive test.

CLINICAL RELEVANCE

Wearable technology may be used, as part of a multifaceted approach, for the early detection of COVID-19 in elite athletes through monitoring of HRV, RR, and RHR for overall team health.

Estimating protection afforded by prior infection in preventing reinfection: applying the test-negative study design

The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection ($P{E}_S$) by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Mathematical modeling was used to demonstrate a theoretical foundation for applicability of the test-negative, case-control study design to derive $P{E}_S$. Apart from the very early phase of an epidemic, the difference between the test-negative estimate for $P{E}_S$ and true value of $P{E}_S$ was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of $P{E}_S$ and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated $P{E}_S$, but the underestimate was considerable only when > 50% of the population was ever infected. Misclassification of latent infection, misclassification of current active infection, and scale-up of vaccination all resulted in negligible bias in estimated $P{E}_S$. The test-negative design was applied to national-level testing data in Qatar to estimate $P{E}_S$ for SARS-CoV-2. $P{E}_S$ against SARS-CoV-2 Alpha and Beta variants was estimated at 97.0% (95% CI, 93.6-98.6) and 85.5% (95% CI, 82.4-88.1), respectively. These estimates were validated using a cohort study design. The test-negative design offers a feasible, robust method to estimate protection from prior infection in preventing reinfection.

Two biases in incubation time estimation related to exposure

BACKGROUND

Estimation of the SARS-CoV-2 incubation time distribution is hampered by incomplete data about infection. We discuss two biases that may result from incorrect handling of such data. Notified cases may recall recent exposures more precisely (differential recall). This creates bias if the analysis is restricted to observations with well-defined exposures, as longer incubation times are more likely to be excluded. Another bias occurred in the initial estimates based on data concerning travellers from Wuhan. Only individuals who developed symptoms after their departure were included, leading to under-representation of cases with shorter incubation times (left truncation). This issue was not addressed in the analyses performed in the literature.

METHODS

We performed simulations and provide a literature review to investigate the amount of bias in estimated percentiles of the SARS-CoV-2 incubation time distribution.

RESULTS

Depending on the rate of differential recall, restricting the analysis to a subset of narrow exposure windows resulted in underestimation in the median and even more in the 95th percentile. Failing to account for left truncation led to an overestimation of multiple days in both the median and the 95th percentile.

CONCLUSION

We examined two overlooked sources of bias concerning exposure information that the researcher engaged in incubation time estimation needs to be aware of.

Evaluation of self-collected nasal, urine, and saliva samples for molecular detection of SARS-CoV-2 using an EUA approved RT-PCR assay and a laboratory developed LAMP SARS-CoV-2 test

As the SARS-CoV-2 virus spread throughout the world, millions of positive cases of COVID-19 were registered and, even though there are millions of people already vaccinated against SARS-CoV-2, a large part of the global population remains vulnerable to contracting the virus. Massive nasopharyngeal sample collection in Puerto Rico at the beginning of the pandemic was limited by the scarcity of trained personnel and testing sites. To increase SARS-CoV-2 molecular testing availability, we evaluated the diagnostic accuracy of self-collected nasal, saliva, and urine samples using the TaqPath reverse transcription polymerase chain reaction (RT-PCR) COVID-19 kit to detect SARS-CoV-2. We also created a colorimetric loop-mediated isothermal amplification (LAMP) laboratory developed test (LDT) to detect SARS-CoV-2, as another strategy to increase the availability of molecular testing in community-based laboratories. Automated RNA extraction was performed in the KingFisher Flex instrument, followed by PCR quantification of SARS-CoV-2 on the 7500 Fast Dx RT-PCR using the TaqPath RT-PCR COVID-19 molecular test. Data was interpreted by the COVID-19 Interpretive Software from Applied Biosystems and statistically analyzed with Cohen's kappa coefficient (k). Cohen's kappa coefficient (k) for paired nasal and saliva samples showed moderate agreement (0.52). Saliva samples exhibited a higher viral load. We also observed 90% concordance between LifeGene-Biomarks' SARS-CoV-2 Rapid Colorimetric LAMP LDT and the TaqPath RT-PCR COVID-19 test. Our results suggest that self-collected saliva is superior to nasal and urine samples for COVID-19 testing. The results also suggest that the colorimetric LAMP LDT is a rapid alternative to RT-PCR tests for the detection of SARS-CoV-2. This test can be easily implemented in clinics, hospitals, the workplace, and at home; optimizing the surveillance and collection process, which helps mitigate global public health and socioeconomic upheaval caused by airborne pandemics.

Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach

The COVID-19 pandemic has significantly impacted global health, social, and economic situations since its emergence in December 2019. The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach, concentrating on the year 2021. We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model, incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis (EDA) approach. While no vaccine guarantees total immunity against the disease, and vaccine immunity wanes over time, it is critical to include and accurately estimate vaccine efficacy, as well as a constant vaccine immunity decay or wane factor, to better simulate the dynamics of vaccine-induced protection over time. Based on the distribution and effectiveness of vaccines, we integrated a data-driven estimation of vaccine efficacy, calculated at 75% for Malaysia, underscoring the model's realism and relevance to the specific context of the country. The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters. The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy. Our findings reveal that this distinct vaccination policy, which emphasizes an accelerated vaccination rate during the initial stages of the program, is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections. The study found that vaccinating 57-66% of the population (as opposed to 76% in the real data) with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections. The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination, offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies, particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy. While the methodology used in this study is specifically applied to national data from Malaysia, its successful application to local regions within Malaysia, such as Selangor and Johor, indicates its adaptability and potential for broader application. This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes, implying its usefulness for similar datasets from various geographical regions.

Estimating the impact of test-trace-isolate-quarantine systems on SARS-CoV-2 transmission in Australia

BACKGROUND

Australian states and territories used test-trace-isolate-quarantine (TTIQ) systems extensively in their response to the COVID-19 pandemic in 2020-2021. We report on an analysis of Australian case data to estimate the impact of test-trace-isolate-quarantine systems on SARS-CoV-2 transmission.

METHODS

Our analysis uses a novel mathematical modelling framework and detailed surveillance data on COVID-19 cases including dates of infection and dates of isolation. First, we directly translate an empirical distribution of times from infection to isolation into reductions in potential for onward transmission during periods of relatively low caseloads (tens to hundreds of reported cases per day). We then apply a simulation approach, validated against case data, to assess the impact of case-initiated contact tracing on transmission during a period of relatively higher caseloads and system stress (up to thousands of cases per day).

RESULTS

We estimate that under relatively low caseloads in the state of New South Wales (tens of cases per day), TTIQ contributed to a 54% reduction in transmission. Under higher caseloads in the state of Victoria (hundreds of cases per day), TTIQ contributed to a 42% reduction in transmission. Our results also suggest that case-initiated contact tracing can support timely quarantine in times of system stress (thousands of cases per day).

CONCLUSION

Contact tracing systems for COVID-19 in Australia were highly effective and adaptable in supporting the national suppression strategy from 2020-21, prior to the emergence of the Omicron variant in November 2021. TTIQ systems were critical to the maintenance of the strong suppression strategy and were more effective when caseloads were (relatively) low.

A COVID-19 cluster analysis in an office: Assessing the long-range aerosol and fomite transmissions with infection control measures

Simulated exposure to severe acute respiratory syndrome coronavirus 2 in the environment was demonstrated based on the actual coronavirus disease 2019 cluster occurrence in an office, with a projected risk considering the likely transmission pathways via aerosols and fomites. A total of 35/85 occupants were infected, with the attack rate in the first stage as 0.30. It was inferred that the aerosol transmission at long-range produced the cluster at virus concentration in the saliva of the infected cases on the basis of the simulation, more than 108 PFU mL-1. Additionally, all wearing masks effectiveness was estimated to be 61%-81% and 88%-95% reduction in risk for long-range aerosol transmission in the normal and fit state of the masks, respectively, and a 99.8% or above decline in risk of fomite transmission. The ventilation effectiveness for long-range aerosol transmission was also calculated to be 12%-29% and 36%-66% reductions with increases from one air change per hour (ACH) to two ACH and six ACH, respectively. Furthermore, the virus concentration reduction in the saliva to 1/3 corresponded to the risk reduction for long-range aerosol transmission by 60%-64% and 40%-51% with and without masks, respectively.

Reporting delays: A widely neglected impact factor in COVID-19 forecasts

Epidemic forecasts are only as good as the accuracy of epidemic measurements. Is epidemic data, particularly COVID-19 epidemic data, clean, and devoid of noise? The complexity and variability inherent in data collection and reporting suggest otherwise. While we cannot evaluate the integrity of the COVID-19 epidemic data in a holistic fashion, we can assess the data for the presence of reporting delays. In our work, through the analysis of the first COVID-19 wave, we find substantial reporting delays in the published epidemic data. Motivated by the desire to enhance epidemic forecasts, we develop a statistical framework to detect, uncover, and remove reporting delays in the infectious, recovered, and deceased epidemic time series. Using our framework, we expose and analyze reporting delays in eight regions significantly affected by the first COVID-19 wave. Further, we demonstrate that removing reporting delays from epidemic data by using our statistical framework may decrease the error in epidemic forecasts. While our statistical framework can be used in combination with any epidemic forecast method that intakes infectious, recovered, and deceased data, to make a basic assessment, we employed the classical SIRD epidemic model. Our results indicate that the removal of reporting delays from the epidemic data may decrease the forecast error by up to 50%. We anticipate that our framework will be indispensable in the analysis of novel COVID-19 strains and other existing or novel infectious diseases.

Performance of and Severe Acute Respiratory Syndrome Coronavirus 2 Diagnostics Based on Symptom Onset and Close Contact Exposure: An Analysis From the Test Us at Home Prospective Cohort Study

Background

Understanding changes in diagnostic performance after symptom onset and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure within different populations is crucial to guide the use of diagnostics for SARS-CoV-2.

Methods

The Test Us at Home study was a longitudinal cohort study that enrolled individuals across the United States between October 2021 and February 2022. Participants performed paired antigen-detection rapid diagnostic tests (Ag-RDTs) and reverse-transcriptase polymerase chain reaction (RT-PCR) tests at home every 48 hours for 15 days and self-reported symptoms and known coronavirus disease 2019 exposures immediately before testing. The percent positivity for Ag-RDTs and RT-PCR tests was calculated each day after symptom onset and exposure and stratified by vaccination status, variant, age category, and sex.

Results

The highest percent positivity occurred 2 days after symptom onset (RT-PCR, 91.2%; Ag-RDT, 71.1%) and 6 days after exposure (RT-PCR, 91.8%; Ag-RDT, 86.2%). RT-PCR and Ag-RDT performance did not differ by vaccination status, variant, age category, or sex. The percent positivity for Ag-RDTs was lower among exposed, asymptomatic than among symptomatic individuals (37.5% (95% confidence interval [CI], 13.7%-69.4%) vs 90.3% (75.1%-96.7%). Cumulatively, Ag-RDTs detected 84.9% (95% CI, 78.2%-89.8%) of infections within 4 days of symptom onset. For exposed participants, Ag-RDTs detected 94.0% (95% CI, 86.7%-97.4%) of RT-PCR-confirmed infections within 6 days of exposure.

Conclusions

The percent positivity for Ag-RDTs and RT-PCR tests was highest 2 days after symptom onset and 6 days after exposure, and performance increased with serial testing. The percent positivity of Ag-RDTs was lowest among asymptomatic individuals but did not differ by sex, variant, vaccination status, or age category.

Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru

Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic's impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.

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?

Deciphering Microbial Shifts in the Gut and Lung Microbiomes of COVID-19 Patients

COVID-19, caused by SARS-CoV-2, results in respiratory and cardiopulmonary infections. There is an urgent need to understand not just the pathogenic mechanisms of this disease but also its impact on the physiology of different organs and microbiomes. Multiple studies have reported the effects of COVID-19 on the gastrointestinal microbiota, such as promoting dysbiosis (imbalances in the microbiome) following the disease's progression. Deconstructing the dynamic changes in microbiome composition that are specifically correlated with COVID-19 patients remains a challenge. Motivated by this problem, we implemented a biomarker discovery pipeline to identify candidate microbes specific to COVID-19. This involved a meta-analysis of large-scale COVID-19 metagenomic data to decipher the impact of COVID-19 on the human gut and respiratory microbiomes. Metagenomic studies of the gut and respiratory microbiomes of COVID-19 patients and of microbiomes from other respiratory diseases with symptoms similar to or overlapping with COVID-19 revealed 1169 and 131 differentially abundant microbes in the human gut and respiratory microbiomes, respectively, that uniquely associate with COVID-19. Furthermore, by utilizing machine learning models (LASSO and XGBoost), we demonstrated the power of microbial features in separating COVID-19 samples from metagenomic samples representing other respiratory diseases and controls (healthy individuals), achieving an overall accuracy of over 80%. Overall, our study provides insights into the microbiome shifts occurring in COVID-19 patients, shining a new light on the compositional changes.

Protective potential of mesenchymal stem cells against COVID-19 during pregnancy

SARS-CoV-2 causes COVID-19. COVID-19 has led to severe clinical illnesses and an unprecedented death toll. The virus induces immune inflammatory responses specifically cytokine storm in lungs. Several published reports indicated that pregnant females are less likely to develop severe symptoms compared with non-pregnant. Putative protective role of maternal blood circulating fetal mesenchymal stem cells (MSCs) has emerged and have been put forward as an explanation to alleviated symptoms. MSCs with immune-modulatory, anti-inflammatory and anti-viral roles, hold great potential for the treatment of COVID-19. MSCs could be an alternative to treat infections resulting from the SARS-CoV-2 and potential future outbreaks. This review focuses on the MSCs putative protective roles against COVID-19 in pregnant females.

Advancements in SARS-CoV-2 detection: Navigating the molecular landscape and diagnostic technologies

According to information from the World Health Organization, the world has experienced about 430 million cases of COVID-19, a world-wide health crisis caused by the SARS-CoV-2 virus. This outbreak, originating from China in 2019, has led to nearly 6 million deaths worldwide. As the number of confirmed infections continues to rise, the need for cutting-edge techniques that can detect SARS-CoV-2 infections early and accurately has become more critical. To address this, the Federal Drug Administration (FDA) has issued emergency use authorizations (EUAs) for a wide range of diagnostic tools. These include tests based on detecting nucleic acids and antigen-antibody reactions. The quantitative real-time reverse transcription PCR (qRT-PCR) assay stands out as the gold standard for early virus detection. However, despite its accuracy, qRT-PCR has limitations, such as complex testing protocols and a risk of false negatives, which drive the continuous improvement in nucleic acid and serological testing approaches. The emergence of highly contagious variants of the coronavirus, such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529), has increased the need for tests that can specifically identify these mutations. This article explores both nucleic acid-based and antigen-antibody serological assays, assessing the performance of recently approved FDA tests and those documented in scientific research, especially in identifying new coronavirus strains.

A scoping review of global SARS-CoV-2 wastewater-based epidemiology in light of COVID-19 pandemic

Recently, wastewater-based epidemiology (WBE) research has experienced a strong impetus during the Coronavirus disease 2019 (COVID-19) pandemic. However, a few technical issues related to surveillance strategies, such as standardized procedures ranging from sampling to testing protocols, need to be resolved in preparation for future infectious disease outbreaks. This review highlights the study characteristics, potential use of WBE and overview of methods, as well as methods utilized to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) including its variant in wastewater. A literature search was performed electronically in PubMed and Scopus according to PRISMA guidelines for relevant peer-reviewed articles published between January 2020 and March 2022. The search identified 588 articles, out of which 221 fulfilled the necessary criteria and are discussed in this review. Most global WBE studies were conducted in North America (n = 75, 34 %), followed by Europe (n = 68, 30.8 %), and Asia (n = 43, 19.5 %). The review also showed that most of the application of WBE observed were to correlate SARS-CoV-2 ribonucleic acid (RNA) trends in sewage with epidemiological data (n = 90, 40.7 %). The techniques that were often used globally for sample collection, concentration, preferred matrix recovery control and various sample types were also discussed. Overall, this review provided a framework for researchers specializing in WBE to apply strategic approaches to their research questions in achieving better functional insights. In addition, areas that needed more in-depth analysis, data collection, and ideas for new initiatives were identified.

Long Term Impact of Adeno-tonsillectomy on Immunity Against Respiratory Viral Infections; Evidence Deduced During COVID-19 Pandemic

OBJECTIVES

To evaluate the risk of acquiring COVID-19 infection in patients who have undergone adeno-tonsillectomy (AT) as compared to their siblings.

METHODS

In this bidirectional cohort control study, 36 cohorts, younger than 18 y, who underwent AT, and 27 controls (siblings of the enrolled cohorts, younger than 18 y) were recruited. Incidence of COVID-19 was analyzed by symptoms suggestive of COVID-19 infection, COVID-19 testing, and SARS-CoV-2 specific antibody measurement.

RESULTS

In the cohort group, the overall COVID-19 positivity rate was 80.5% (n = 29/36) and symptomatic COVID-19 positivity rate was 68.9% (n = 20/29). Among the controls, the overall COVID-19 positivity rate was 44% (n = 12/27) and symptomatic COVID-19 positivity rate was 16% (n = 2/12). The cohorts had 1.8 times higher risk of contracting COVID-19 infection and the relative risk of symptomatic COVID-19 infections as compared to controls was 4.14.

CONCLUSIONS

This pilot study indicates that adeno-tonsillectomy poses children at a significantly higher risk of COVID-19 infections and likely other viral upper respiratory tract infections.

Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions

In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effectively curb the spread of the disease. Therefore, the present study was conducted within a controlled clinical environment to determine eventual detectable variations in the voice of COVID-19 patients, recovered and healthy subjects, and also to determine whether machine learning-based voice assessment (MLVA) can accurately discriminate between them, thus potentially serving as a more effective mass-screening tool. Three different subpopulations were consecutively recruited: positive COVID-19 patients, recovered COVID-19 patients and healthy individuals as controls. Positive patients were recruited within 10 days from nasal swab positivity. Recovery from COVID-19 was established clinically, virologically and radiologically. Healthy individuals reported no COVID-19 symptoms and yielded negative results at serological testing. All study participants provided three trials for multiple vocal tasks (sustained vowel phonation, speech, cough). All recordings were initially divided into three different binary classifications with a feature selection, ranking and cross-validated RBF-SVM pipeline. This brough a mean accuracy of 90.24%, a mean sensitivity of 91.15%, a mean specificity of 89.13% and a mean AUC of 0.94 across all tasks and all comparisons, and outlined the sustained vowel as the most effective vocal task for COVID discrimination. Moreover, a three-way classification was carried out on an external test set comprised of 30 subjects, 10 per class, with a mean accuracy of 80% and an accuracy of 100% for the detection of positive subjects. Within this assessment, recovered individuals proved to be the most difficult class to identify, and all the misclassified subjects were declared positive; this might be related to mid and short-term vocal traces of COVID-19, even after the clinical resolution of the infection. In conclusion, MLVA may accurately discriminate between positive COVID-19 patients, recovered COVID-19 patients and healthy individuals. Further studies should test MLVA among larger populations and asymptomatic positive COVID-19 patients to validate this novel screening technology and test its potential application as a potentially more effective surveillance strategy for COVID-19.

Quantifying the impact of hospital catchment area definitions on hospital admissions forecasts: COVID-19 in England, September 2020-April 2021

BACKGROUND

Defining healthcare facility catchment areas is a key step in predicting future healthcare demand in epidemic settings. Forecasts of hospitalisations can be informed by leading indicators measured at the community level. However, this relies on the definition of so-called catchment areas or the geographies whose populations make up the patients admitted to a given hospital, which are often not well-defined. Little work has been done to quantify the impact of hospital catchment area definitions on healthcare demand forecasting.

METHODS

We made forecasts of local-level hospital admissions using a scaled convolution of local cases (as defined by the hospital catchment area) and delay distribution. Hospital catchment area definitions were derived from either simple heuristics (in which people are admitted to their nearest hospital or any nearby hospital) or historical admissions data (all emergency or elective admissions in 2019, or COVID-19 admissions), plus a marginal baseline definition based on the distribution of all hospital admissions. We evaluated predictive performance using each hospital catchment area definition using the weighted interval score and considered how this changed by the length of the predictive horizon, the date on which the forecast was made, and by location. We also considered the change, if any, in the relative performance of each definition in retrospective vs. real-time settings, or at different spatial scales.

RESULTS

The choice of hospital catchment area definition affected the accuracy of hospital admission forecasts. The definition based on COVID-19 admissions data resulted in the most accurate forecasts at both a 7- and 14-day horizon and was one of the top two best-performing definitions across forecast dates and locations. The "nearby" heuristic also performed well, but less consistently than the COVID-19 data definition. The marginal distribution baseline, which did not include any spatial information, was the lowest-ranked definition. The relative performance of the definitions was larger when using case forecasts compared to future observed cases. All results were consistent across spatial scales of the catchment area definitions.

CONCLUSIONS

Using catchment area definitions derived from context-specific data can improve local-level hospital admission forecasts. Where context-specific data is not available, using catchment areas defined by carefully chosen heuristics is a sufficiently good substitute. There is clear value in understanding what drives local admissions patterns, and further research is needed to understand the impact of different catchment area definitions on forecast performance where case trends are more heterogeneous.

Workplace interventions to reduce the risk of SARS-CoV-2 infection outside of healthcare settings

BACKGROUND

Although many people infected with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) experience no or mild symptoms, some individuals can develop severe illness and may die, particularly older people and those with underlying medical problems. Providing evidence-based interventions to prevent SARS-CoV-2 infection has become more urgent with the potential psychological toll imposed by the coronavirus disease 2019 (COVID-19) pandemic. Controlling exposures to occupational hazards is the fundamental method of protecting workers. When it comes to the transmission of viruses, workplaces should first consider control measures that can potentially have the most significant impact. According to the hierarchy of controls, one should first consider elimination (and substitution), then engineering controls, administrative controls, and lastly, personal protective equipment. This is the first update of a Cochrane review published 6 May 2022, with one new study added.

OBJECTIVES

To assess the benefits and harms of interventions in non-healthcare-related workplaces aimed at reducing the risk of SARS-CoV-2 infection compared to other interventions or no intervention.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, Web of Science Core Collections, Cochrane COVID-19 Study Register, World Health Organization (WHO) COVID-19 Global literature on coronavirus disease, ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform, and medRxiv to 13 April 2023.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and non-randomised studies of interventions. We included adult workers, both those who come into close contact with clients or customers (e.g. public-facing employees, such as cashiers or taxi drivers), and those who do not, but who could be infected by coworkers. We excluded studies involving healthcare workers. We included any intervention to prevent or reduce workers' exposure to SARS-CoV-2 in the workplace, defining categories of intervention according to the hierarchy of hazard controls (i.e. elimination; engineering controls; administrative controls; personal protective equipment).

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Our primary outcomes were incidence rate of SARS-CoV-2 infection (or other respiratory viruses), SARS-CoV-2-related mortality, adverse events, and absenteeism from work. Our secondary outcomes were all-cause mortality, quality of life, hospitalisation, and uptake, acceptability, or adherence to strategies. We used the Cochrane RoB 2 tool to assess risk of bias, and GRADE methods to evaluate the certainty of evidence for each outcome.

MAIN RESULTS

We identified 2 studies including a total of 16,014 participants. Elimination-of-exposure interventions We included one study examining an intervention that focused on elimination of hazards, which was an open-label, cluster-randomised, non-inferiority trial, conducted in England in 2021. The study compared standard 10-day self-isolation after contact with an infected person to a new strategy of daily rapid antigen testing and staying at work if the test is negative (test-based attendance). The trialists hypothesised that this would lead to a similar rate of infections, but lower COVID-related absence. Staff (N = 11,798) working at 76 schools were assigned to standard isolation, and staff (N = 12,229) working at 86 schools were assigned to the test-based attendance strategy. The results between test-based attendance and standard 10-day self-isolation were inconclusive for the rate of symptomatic polymerase chain reaction (PCR)-positive SARS-CoV-2 infection (rate ratio (RR) 1.28, 95% confidence interval (CI) 0.74 to 2.21; 1 study; very low-certainty evidence). The results between test-based attendance and standard 10-day self-isolation were inconclusive for the rate of any PCR-positive SARS-CoV-2 infection (RR 1.35, 95% CI 0.82 to 2.21; 1 study; very low-certainty evidence). COVID-related absenteeism rates were 3704 absence days in 566,502 days-at-risk (6.5 per 1000 working days) in the control group and 2932 per 539,805 days-at-risk (5.4 per 1000 working days) in the intervention group (RR 0.83, 95% CI 0.55 to 1.25). We downgraded the certainty of the evidence to low due to imprecision. Uptake of the intervention was 71% in the intervention group, but not reported for the control intervention. The trial did not measure our other outcomes of SARS-CoV-2-related mortality, adverse events, all-cause mortality, quality of life, or hospitalisation. We found seven ongoing studies using elimination-of-hazard strategies, six RCTs and one non-randomised trial. Administrative control interventions We found one ongoing RCT that aims to evaluate the efficacy of the Bacillus Calmette-Guérin (BCG) vaccine in preventing COVID-19 infection and reducing disease severity. Combinations of eligible interventions We included one non-randomised study examining a combination of elimination of hazards, administrative controls, and personal protective equipment. The study was conducted in two large retail companies in Italy in 2020. The study compared a safety operating protocol, measurement of body temperature and oxygen saturation upon entry, and a SARS-CoV-2 test strategy with a minimum activity protocol. Both groups received protective equipment. All employees working at the companies during the study period were included: 1987 in the intervention company and 1798 in the control company. The study did not report an outcome of interest for this systematic review. Other intervention categories We did not find any studies in this category.

AUTHORS' CONCLUSIONS

We are uncertain whether a test-based attendance policy affects rates of PCR-positive SARS-CoV-2 infection (any infection; symptomatic infection) compared to standard 10-day self-isolation amongst school and college staff. A test-based attendance policy may result in little to no difference in absenteeism rates compared to standard 10-day self-isolation. The non-randomised study included in our updated search did not report any outcome of interest for this Cochrane review. As a large part of the population is exposed in the case of a pandemic, an apparently small relative effect that would not be worthwhile from the individual perspective may still affect many people, and thus become an important absolute effect from the enterprise or societal perspective. The included RCT did not report on any of our other primary outcomes (i.e. SARS-CoV-2-related mortality and adverse events). We identified no completed studies on any other interventions specified in this review; however, eight eligible studies are ongoing. More controlled studies are needed on testing and isolation strategies, and working from home, as these have important implications for work organisations.

Understanding health system resilience in responding to COVID-19 pandemic: experiences and lessons from an evolving context of federalization in Nepal

INTRODUCTION

The COVID-19 pandemic has tested the resilience capacities of health systems worldwide and highlighted the need to understand the concept, pathways, and elements of resilience in different country contexts. In this study, we assessed the health system response to COVID-19 in Nepal and examined the processes of policy formulation, communication, and implementation at the three tiers of government, including the dynamic interactions between tiers. Nepal was experiencing the early stages of federalization reform when COVID-19 pandemic hit the country, and clarity in roles and capacity to implement functions were the prevailing challenges, especially among the subnational governments.

METHODS

We adopted a cross-sectional exploratory design, using mixed methods. We conducted a desk-based review of all policy documents introduced in response to COVID-19 from January to December 2020, and collected qualitative data through 22 key informant interviews at three tiers of government, during January-March 2021. Two municipalities were purposively selected for data collection in Lumbini province. Our analysis is based on a resilience framework that has been developed by our research project, ReBUILD for Resilience, which helps to understand pathways to health system resilience through absorption, adaptation and transformation.

RESULTS

In the newly established federal structure, the existing emergency response structure and plans were utilized, which were yet to be tested in the decentralized system. The federal government effectively led the policy formulation process, but with minimal engagement of sub-national governments. Local governments could not demonstrate resilience capacities due to the novelty of the federal system and their consequent lack of experience, confusion on roles, insufficient management capacity and governance structures at local level, which was further aggravated by the limited availability of human, technical and financial resources.

CONCLUSIONS

The study findings emphasize the importance of strong and flexible governance structures and strengthened capacity of subnational governments to effectively manage pandemics. The study elaborates on the key areas and pathways that contribute to the resilience capacities of health systems from the experience of Nepal. We draw out lessons that can be applied to other fragile and shock-prone settings.

How robust are estimates of key parameters in standard viral dynamic models?

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

To mitigate the loss of lives during the COVID-19 pandemic, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with variants susceptible to mAb therapy. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response antiviral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.

Precision nutrition to reset virus-induced human metabolic reprogramming and dysregulation (HMRD) in long-COVID

SARS-CoV-2, the etiological agent of COVID-19, is devoid of any metabolic capacity; therefore, it is critical for the viral pathogen to hijack host cellular metabolic machinery for its replication and propagation. This single-stranded RNA virus with a 29.9 kb genome encodes 14 open reading frames (ORFs) and initiates a plethora of virus-host protein-protein interactions in the human body. These extensive viral protein interactions with host-specific cellular targets could trigger severe human metabolic reprogramming/dysregulation (HMRD), a rewiring of sugar-, amino acid-, lipid-, and nucleotide-metabolism(s), as well as altered or impaired bioenergetics, immune dysfunction, and redox imbalance in the body. In the infectious process, the viral pathogen hijacks two major human receptors, angiotensin-converting enzyme (ACE)-2 and/or neuropilin (NRP)-1, for initial adhesion to cell surface; then utilizes two major host proteases, TMPRSS2 and/or furin, to gain cellular entry; and finally employs an endosomal enzyme, cathepsin L (CTSL) for fusogenic release of its viral genome. The virus-induced HMRD results in 5 possible infectious outcomes: asymptomatic, mild, moderate, severe to fatal episodes; while the symptomatic acute COVID-19 condition could manifest into 3 clinical phases: (i) hypoxia and hypoxemia (Warburg effect), (ii) hyperferritinemia ('cytokine storm'), and (iii) thrombocytosis (coagulopathy). The mean incubation period for COVID-19 onset was estimated to be 5.1 days, and most cases develop symptoms after 14 days. The mean viral clearance times were 24, 30, and 39 days for acute, severe, and ICU-admitted COVID-19 patients, respectively. However, about 25-70% of virus-free COVID-19 survivors continue to sustain virus-induced HMRD and exhibit a wide range of symptoms that are persistent, exacerbated, or new 'onset' clinical incidents, collectively termed as post-acute sequelae of COVID-19 (PASC) or long COVID. PASC patients experience several debilitating clinical condition(s) with >200 different and overlapping symptoms that may last for weeks to months. Chronic PASC is a cumulative outcome of at least 10 different HMRD-related pathophysiological mechanisms involving both virus-derived virulence factors and a multitude of innate host responses. Based on HMRD and virus-free clinical impairments of different human organs/systems, PASC patients can be categorized into 4 different clusters or sub-phenotypes: sub-phenotype-1 (33.8%) with cardiac and renal manifestations; sub-phenotype-2 (32.8%) with respiratory, sleep and anxiety disorders; sub-phenotype-3 (23.4%) with skeleto-muscular and nervous disorders; and sub-phenotype-4 (10.1%) with digestive and pulmonary dysfunctions. This narrative review elucidates the effects of viral hijack on host cellular machinery during SARS-CoV-2 infection, ensuing detrimental effect(s) of virus-induced HMRD on human metabolism, consequential symptomatic clinical implications, and damage to multiple organ systems; as well as chronic pathophysiological sequelae in virus-free PASC patients. We have also provided a few evidence-based, human randomized controlled trial (RCT)-tested, precision nutrients to reset HMRD for health recovery of PASC patients.

Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Mutations make pandemics worse or better: modeling SARS-CoV-2 variants and imperfect vaccination

COVID-19 is a respiratory disease triggered by an RNA virus inclined to mutations. Since December 2020, variants of COVID-19 (especially Delta and Omicron) continuously appeared with different characteristics that influenced death and transmissibility emerged around the world. To address the novel dynamics of the disease, we propose and analyze a dynamical model of two strains, namely native and mutant, transmission dynamics with mutation and imperfect vaccination. It is also assumed that the recuperated individuals from the native strain can be infected with mutant strain through the direct contact with individual or contaminated surfaces or aerosols. We compute the basic reproduction number, R 0 , which is the maximum of the basic reproduction numbers of native and mutant strains. We prove the nonexistence of backward bifurcation using the center manifold theory, and global stability of disease-free equilibrium whenR 0 < 1 , that is, vaccine is effective enough to eliminate the native and mutant strains even if it cannot provide full protection. Hopf bifurcation appears when the endemic equilibrium loses its stability. An intermediate mutation rate ν 1 leads to oscillations. When ν 1 increases over a threshold, the system regains its stability and exhibits an interesting dynamics called endemic bubble. An analytical expression for vaccine-induced herd immunity is derived. The epidemiological implication of the herd immunity threshold is that the disease may effectively be eradicated if the minimum herd immunity threshold is attained in the community. Furthermore, the model is parameterized using the Indian data of the cumulative number of confirmed cases and deaths of COVID-19 from March 1 to September 27 in 2021, using MCMC method. The cumulative cases and deaths can be reduced by increasing the vaccine efficacies to both native and mutant strains. We observe that by considering the vaccine efficacy against native strain as 90%, both cumulative cases and deaths would be reduced by 0.40%. It is concluded that increasing immunity against mutant strain is more influential than the vaccine efficacy against it in controlling the total cases. Our study demonstrates that the COVID-19 pandemic may be worse due to the occurrence of oscillations for certain mutation rates (i.e., outbreaks will occur repeatedly) but better due to stability at a lower infection level with a larger mutation rate. We perform sensitivity analysis using the Latin Hypercube Sampling methodology and partial rank correlation coefficients to illustrate the impact of parameters on the basic reproduction number, the number of cumulative cases and deaths, which ultimately sheds light on disease mitigation.

Preparing correctional settings for the next pandemic: a modeling study of COVID-19 outbreaks in two high-income countries

Introduction

Correctional facilities are high-priority settings for coordinated public health responses to the COVID-19 pandemic. These facilities are at high risk of disease transmission due to close contacts between people in prison and with the wider community. People in prison are also vulnerable to severe disease given their high burden of co-morbidities.

Methods

We developed a mathematical model to evaluate the effect of various public health interventions, including vaccination, on the mitigation of COVID-19 outbreaks, applying it to prisons in Australia and Canada.

Results

We found that, in the absence of any intervention, an outbreak would occur and infect almost 100% of people in prison within 20 days of the index case. However, the rapid rollout of vaccines with other non-pharmaceutical interventions would almost eliminate the risk of an outbreak.

Discussion

Our study highlights that high vaccination coverage is required for variants with high transmission probability to completely mitigate the outbreak risk in prisons.

Characteristics and Outcomes of Coronavirus Disease- 2019 Among Pregnant Women in Saudi Arabia; a Retrospective Study

Background

Pregnancy-related coronavirus disease 2019 infection ranges from asymptomatic to very serious illness. This study aimed to determine the impact of the COVID-19 infection on pregnant women in the Jazan region of Saudi Arabia.

Methods

Retrospective observational study of women who had COVID-19 positive test in pregnancy admitted in King Fahd Hospital, Abu Arish General Hospital, and Sabya General Hospital, Jazan, Saudi Arabia during the period between March 2020 and March 2022. Data were extracted from the patient's records. Frequency and percentage distributions were calculated for categorical variables. Descriptive studies and regression analysis were conducted to evaluate the association between selected variables and pregnancy outcomes.

Results

Of the 33 pregnant women with confirmed infection, the majority were in their second and third trimester, with approximately 42.4% requiring intensive care unit (ICU) admission and oxygen therapy. The most prevalent symptoms were high respiratory rate and low blood pressure, often accompanied by fever, cough, and shortness of breath. Live births resulted in 54.5% of the cases, while two maternal deaths were reported. Significant associations were found between the need for non-invasive ventilation and timing of infection (p = 0.026), the mode of delivery and timing of infection (p = 0.036), and the mode of delivery and body mass index (BMI) (p = 0.007).

Conclusion

COVID-19 poses significant risks to pregnant women, particularly in the third trimester, and emphasized the importance of early identification of high-risk pregnancies, strategic planning, and enhanced monitoring during antenatal care.

Exploring factors shaping antibiotic resistance patterns in Streptococcus pneumoniae during the 2020 COVID-19 pandemic

Non-pharmaceutical interventions implemented to block SARS-CoV-2 transmission in early 2020 led to global reductions in the incidence of invasive pneumococcal disease (IPD). By contrast, most European countries reported an increase in antibiotic resistance among invasive Streptococcus pneumoniae isolates from 2019 to 2020, while an increasing number of studies reported stable pneumococcal carriage prevalence over the same period. To disentangle the impacts of the COVID-19 pandemic on pneumococcal epidemiology in the community setting, we propose a mathematical model formalizing simultaneous transmission of SARS-CoV-2 and antibiotic-sensitive and -resistant strains of S. pneumoniae. To test hypotheses underlying these trends five mechanisms were built into the model and examined: (1) a population-wide reduction of antibiotic prescriptions in the community, (2) lockdown effect on pneumococcal transmission, (3) a reduced risk of developing an IPD due to the absence of common respiratory viruses, (4) community azithromycin use in COVID-19 infected individuals, (5) and a longer carriage duration of antibiotic-resistant pneumococcal strains. Among 31 possible pandemic scenarios involving mechanisms individually or in combination, model simulations surprisingly identified only two scenarios that reproduced the reported trends in the general population. They included factors (1), (3), and (4). These scenarios replicated a nearly 50% reduction in annual IPD, and an increase in antibiotic resistance from 20% to 22%, all while maintaining a relatively stable pneumococcal carriage. Exploring further, higher SARS-CoV-2 R0 values and synergistic within-host virus-bacteria interaction mechanisms could have additionally contributed to the observed antibiotic resistance increase. Our work demonstrates the utility of the mathematical modeling approach in unraveling the complex effects of the COVID-19 pandemic responses on AMR dynamics.

A mixture of mobility and meteorological data provides a high correlation with COVID-19 growth in an infection-naive population: a study for Spanish provinces

Introduction

We use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions.

Methods

We develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth.

Results

We find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series.

Discussion

Our results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.

Pediatric Plastic Surgery in the COVID-19 Era

ABSTRACT

The COVID-19 pandemic has forced the healthcare system to adopt novel strategies to treat patients. Pediatric plastic surgeons are uniquely exposed to high rates of infections during examinations and surgeries via aerosol-generating procedures, in part because of the predilection of viral particles for the nasal cavities and pharynx. Telemedicine has emerged as a useful virtual medium for encouraging prolonged patient follow-up while maintaining physical distance. It has proven beneficial in mitigating infection risks while decreasing the financial burden on patients, their families, and healthcare teams. New trends driven by the pandemic added multiple elements to the patient-physician relationship and have left a lasting impact on the field of pediatric plastic surgery in clinical guidelines, surgical care, and patient outcomes. Lessons learned help inform pediatric plastic surgeons on how to reduce future viral infection risk and lead a more appropriately efficient surgical team depending on early triage.

Distorted correlations among censored data: causes, effects, and correction

Data censoring occurs when researchers do not know precise values of data points (e.g., age is 55+ or concentration ≤ .001). Censoring is frequent within psychology but typically unrecognized outside of longitudinal studies. We describe five circumstances when censoring may occur, demonstrate censoring distorts correlations, and discuss how censoring can create spurious factors. Next, we explain how to use R package lava to calculate maximum likelihood estimates (Holst and Budtz-Jørgensen Computational Statistics, 28(4), 1385-1452, 2013) of correlations between uncensored variables based upon censored variables. Previous research demonstrated these estimates were more accurate than Muthén's (1984) estimate for one particular model, but no research has systematically examined their accuracy. We therefore conducted a simulation study exploring the effects of the correlation, sample size, and censoring on point and interval estimates of correlations. Based upon 80 cells in which low values of normally distributed variables were censored, we recommend the constrained regression model with Wald confidence intervals. These methods were precise and unbiased unless both variables had 70% censoring and the correlation was large and negative (e.g., -.9), in which case estimates were closer to -1 than they should be. Opposite results would occur if low values of one variable and high values of the other were censored: Estimates would be precise and unbiased unless censoring was extreme and correlations were large and positive. To estimate large correlations accurately, we recommend researchers reduce censoring by using longer longitudinal studies, using scales with more response options, and matching measures to populations to reduce floor and ceiling effects.

Projecting COVID-19 intensive care admissions for policy advice, the Netherlands, February 2020 to January 2021

BackgroundModel projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.MethodThe projections were produced using an age-structured transmission model. A consistent, incremental update procedure integrating all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a changepoint analysis guided by Akaike's Information Criterion.ResultsThe model and update procedure allowed us to make weekly projections. Most 3-week prediction intervals were accurate in covering the later observed numbers of ICU admissions. When projections were too high in March and August 2020 or too low in November 2020, the estimated effectiveness of the policy changes was adequately adapted in the changepoint analysis based on the natural accumulation of incoming data.ConclusionThe model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation with the rise of new variants and the start of vaccination.

Reproducibility of COVID-era infectious disease models

Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus' transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease modelling articles from the COVID era. We found that four out of 100 randomly sampled studies released between January 2020 and August 2022 could be completely computationally reproduced using the resources provided (e.g., code, data, instructions) whilst a further eight were partially reproducible. For the 100 most highly cited articles from the same period we found that 11 were completely reproducible with a further 22 partially reproducible. Reflecting on our experience, we discuss common issues affecting computational reproducibility and how these might be addressed.

Path analysis of perceived disease vulnerability, COVID-19 fear, and lower vaccine hesitancy within the context of protection motivation theory

COVID-19 vaccinations have demonstrated effectiveness in reducing severe infections. However, vaccine hesitancy posed a major public health hurdle to combat the COVID-19 pandemic. Online spread of vaccine conspiracy beliefs generated unwarranted mistrust and resistance to vaccines. While numerous studies have explored the factors influencing vaccine hesitancy, there remains a lack of comprehensive understanding regarding the interplay between perceived disease vulnerability, COVID-19 fear, and vaccine hesitancy. Protection motivation theory posits citizens will evaluate perceived threats and take actions to mitigate potential harm. With a large U.S. sample, path analysis demonstrated individuals' perceived disease vulnerability was associated with lower vaccine hesitancy. Greater perceived disease vulnerability was associated with higher COVID-19 fear. Greater COVID-19 fear was associated with lower vaccine hesitancy. Greater vaccine conspiracy beliefs associated with higher vaccine hesitancy. However, in the presence of perceived vulnerability to disease, vaccine conspiracy beliefs associated with higher fear of COVID-19 and thereby lower vaccine hesitancy. We found under circumstances of higher perceived vulnerability to disease and fear of COVID-19, vaccine conspiratorial believers were less vaccine hesitant. We discuss how public health messaging can highlight personal risks to contracting COVID-19 to appeal to those who self-identify as disease prone, but may have reservations about vaccines because of misinformation. Successfully combating diseases entails reaching and gaining cooperation from misbelievers because misinformation is expected to continue in the digital age. By understand individual differences to vaccine hesitancy, it can help increase vaccinations and prevent severe illnesses in the post COVID-19 pandemic era.

Three dimensional analysis of the exhalation flow in the proximity of the mouth

The human exhalation flow is characterized in this work from the three-dimensional velocimetry results obtained by using the stereo particle image velocimetry (SPIV) measurement technique on the flow emitted from a realistic airway model. For this purpose, the transient exhalation flow through the mouth of a person performing two different breaths corresponding to two metabolic rates, standing relaxed (SR) and walking active (WA), is emulated and studied. To reproduce the flow realistically, a detailed three-dimensional model obtained from computed tomography measurements on real subjects is used. To cope with the variability of the experimental data, a subsequent analysis of the results is performed using the TR-PIV (time resolved particle image velocimetry) technique. Exhalation produces a transient jet that becomes a puff when flow emission ends. Three-dimensional vector fields of the jet velocity are obtained in five equally spaced transverse planes up to a distance of Image 1 from the mouth at equally spaced time instants Image 2 which will be referred to as phases (φ), from the beginning to the end of exhalation. The time evolution during exhalation of the jet area of influence, the velocity field and the jet air entrainment have been characterized for each of the jet cross sections. The importance of the use of realistic airway models for the study of this type of flow and the influence of the metabolic rate on its development are also analyzed. The results obtained contribute to the characterization of the human exhalation as a pathway of the transmission of pathogens such as SARS-CoV-2 virus.

How much natural ventilation rate can suppress COVID-19 transmission in occupancy zones?

Background

Previous research has emphasized the importance of efficient ventilation in suppressing COVID-19 transmission in indoor spaces, yet suitable ventilation rates have not been suggested.

Materials and Methods

This study investigated the impacts of mechanical, natural, single-sided, cross-ventilation, and three mask types (homemade, surgical, N95) on COVID-19 spread across eight common indoor settings. Viral exposure was quantified using a mass balance calculation of inhaled viral particles, accounting for initial viral load, removal via ventilation, and mask filtration efficiency.

Results

Results demonstrated that natural cross-ventilation significantly reduced viral load, decreasing from 10,000 to 0 viruses over 15 minutes in a 100 m2 space by providing ~1325 m3/h of outdoor air via two 0.6 m2 openings at 1.5 m/s wind speed. In contrast, single-sided ventilation only halved viral load at best.

Conclusion

Natural cross-ventilation with masks effectively suppressed airborne viruses, lowering potential infections and disease transmission. The study recommends suitable ventilation rates to reduce COVID-19 infection risks in indoor spaces.

Real-Time On-Site Monitoring of Viruses in Wastewater Using Nanotrap® Particles and RICCA Technologies

Wastewater-based epidemiology (WBE) is an effective and efficient tool for the early detection of infectious disease outbreaks in a community. However, currently available methods are laborious, costly, and time-consuming due to the low concentration of viruses and the presence of matrix chemicals in wastewater that may interfere with molecular analyses. In the present study, we designed a highly sensitive "Quick Poop (wastewater with fecal waste) Sensor" (termed, QPsor) using a joint approach of Nanotrap microbiome particles and RICCA (RNA Isothermal Co-Assisted and Coupled Amplification). Using QPsor, the WBE study showed a strong correlation with standard PEG concentrations and the qPCR technique. Using a closed format for a paper-based lateral flow assay, we were able to demonstrate the potential of our assay as a real-time, point-of-care test by detecting the heat-inactivated SARS-CoV-2 virus in wastewater at concentrations of 100 copies/mL and within one hour. As a proof-of-concept demonstration, we analyzed the presence of viral RNA of the SARS-CoV-2 virus and PMMoV in raw wastewater samples from wastewater treatment plants on-site and within 60 min. The results show that the QPsor method can be an effective tool for disease outbreak detection by combining an AI-enabled case detection model with real-time on-site viral RNA extraction and amplification, especially in the absence of intensive clinical laboratory facilities. The lab-free, lab-quality test capabilities of QPsor for viral prevalence and transmission in the community can contribute to the efficient management of pandemic situations.

A case of late-onset organizing pneumonia following COVID-19 infection in a post-kidney transplant patient

A 50-year-old man who had undergone a living-donor kidney transplant 12 years prior for chronic renal failure due to autosomal dominant polycystic kidney disease contracted coronavirus disease 19 (COVID-19). He had a positive antigen test, mild symptoms, sore throat, and fever of 37.9 ℃. The patient was treated with molnupiravir for 5 days, and the symptoms disappeared 5 days after onset. However, 10 days after onset, he developed a fever of approximately 37 ℃ and a non-productive cough; 27 days after onset, the patient was hospitalized for anorexia and a worsening respiratory condition. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen test results on admission were negative, and no antiviral medications were administered against SARS-CoV-2. Computed tomography revealed extensive ground-glass opacities in both lung fields. The patient was treated with steroid pulse therapy, ceftriaxone, atovaquone, azithromycin, and respiratory management using a high-flow nasal cannula. The combined therapies were successful, and the patient was managed with a nasal oxygen cannula after 3 days. Oxygen administration was discontinued after 6 days of hospitalization, and the patient was discharged after 14 days. Based on the laboratory findings, bacterial, interstitial, and Pneumocystis pneumonia were unlikely. The success of the steroid pulse therapy suggested that respiratory failure was caused by pneumonia due to the immune response after COVID-19 infection.

Constrained numerical deconvolution using orthogonal polynomials

In this article, we present an enhanced version of Cutler's deconvolution method to address the limitations of the original algorithm in estimating realistic input and output parameters. Cutler's method, based on orthogonal polynomials, suffers from unconstrained solutions, leading to the lack of realism in the deconvolved signals in some applications. Our proposed approach incorporates constraints using a ridge factor and Lagrangian multipliers in an iterative fashion, maintaining Cutler's iterative projection-based nature. This extension avoids the need for external optimization solvers, making it particularly suitable for applications requiring constraints on inputs and outputs. We demonstrate the effectiveness of the proposed method through two practical applications: the estimation of COVID-19 curves and the study of mavoglurant, an experimental drug. Our results show that the extended method presents a sum of squared residuals in the same order of magnitude of that of the original Cutler's method and other widely known unconstrained deconvolution techniques, but obtains instead physically plausible solutions, correcting the errors introduced by the alternative methods considered, as illustrated in our case studies.

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

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

Epidemiological contemplation for a currently pragmatic COVID-19 health passport: a perspective

The coronavirus disease 2019 (COVID-19) has caused a global pandemic that has wreaked havoc on the lives of millions of people around the world. Confinement measures aim to reduce the epidemic's spread and minimize the burden of morbidity and mortality. In response to the challenges caused by the pandemic, digital health passports have been developed exponentially. We highlight the latent epidemiological barriers to health passports to achieve standardized digital care platforms. This review paper not only highlights the epidemiological barriers but also articulates the possible infrastructure required to make the International Standard for a multi-factor authenticated and validated health passport.

Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia From Chest X-Ray Images

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method.