Nowcasting epidemics of novel pathogens: lessons from COVID-19

Nowcasting methods to improve the performance of respiratory sentinel surveillance: lessons from the COVID-19 pandemic

Respiratory diseases, including influenza and coronaviruses, pose recurrent global threats. This study delves into the respiratory surveillance systems, focusing on the effectiveness of SARI sentinel surveillance for total and severe cases incidence estimation. Leveraging data from the COVID-19 pandemic in Chile, we examined 2020-2023 data (a 159-week period) comparing census surveillance results of confirmed cases and hospitalizations, with sentinel surveillance. Our analyses revealed a consistent underestimation of total cases and an overestimation of severe cases of sentinel surveillance. To address these limitations, we introduce a nowcasting model, improving the precision and accuracy of incidence estimates. Furthermore, the integration of genomic surveillance data significantly enhances model predictions. While our findings are primarily focused on COVID-19, they have implications for respiratory virus surveillance and early detection of respiratory epidemics. The nowcasting model offers real-time insights into an outbreak for public health decision-making, using the same surveillance data that is routinely collected. This approach enhances preparedness for emerging respiratory diseases by the development of practical solutions with applications in public health.

A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort

Medical coding impacts patient care quality, payor reimbursement, and system reliability through the precision of patient information documentation. Inadequate coding specificity can have significant consequences at administrative and patient levels. Models to identify and/or enhance coding specificity practices are needed. Clinical records are not always available, complete, or homogeneous, and clinically driven metrics to assess medical practices are not logistically feasible at the population level, particularly in non-centralized healthcare delivery systems and/or for those who only have access to claims data. Data-driven approaches that incorporate all available information are needed to explore coding specificity practices. Using N = 487,775 hospitalization records of individuals diagnosed with dementia and discharged in 2022 from a large all-payor administrative claims dataset, we fitted logistic regression models using patient and facility characteristics to explain the coding specificity of principal and secondary diagnoses of dementia. A two-step approach was produced to allow for the flexible clustering of patient-level outcomes. Model outcomes were then used within a Poisson binomial model to identify facilities that over- or under-specify dementia diagnoses against healthcare industry standards across hospitalizations. The results indicate that multiple factors are significantly associated with dementia coding specificity, especially for principal diagnoses of dementia (AUC = 0.727). The practical use of this novel risk-adjusted metric is demonstrated for a sample of facilities and geospatially via a U.S. map. This study's findings provide healthcare facilities with a benchmark for assessing coding specificity practices and developing quality enhancements to align with healthcare industry standards, ultimately contributing to better patient care and healthcare system reliability.

Coronavirus Anxiety, COVID Anxiety Syndrome and Mental Health: A Test Among Six Countries During March 2021

The negative impact of the COVID-19 pandemic on mental health outcomes is widely documented. Specifically, individuals experiencing greater degrees of severity in coronavirus anxiety have demonstrated higher levels of generalized anxiety, depression and psychological distress. Yet the pathways in which coronavirus anxiety confers vulnerability are not well known. The present investigation sought to address this gap in the scientific literature by testing the indirect effect of the COVID-19 anxiety syndrome, which centres on the function of detecting and managing the environmental threat of virus exposure and its sequalae. Data were collected during the height of the pandemic (March 2021) and included 5297 adults across six countries. Structural equation modelling techniques revealed that the COVID-19 anxiety syndrome evidenced a statistically significant indirect effect between coronavirus anxiety and generalized anxiety, depression and work/social adjustment. Overall, results suggest there could be public health merit to targeting anxiety related to virus exposure to improve behavioural health for those who are struggling with excessive fear and worry.

Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky

BACKGROUND

Epidemiological nowcasting traditionally relies on count surveillance data. The availability and quality of such count data may vary over time, limiting representation of true infections. Wastewater data correlates with traditional surveillance data and may provide additional value for nowcasting disease trends.

METHODS

We obtained SARS-CoV-2 case, death, wastewater, and serosurvey data for Jefferson County, Kentucky (USA), between August 2020 and March 2021, and parameterized an existing nowcasting model using combinations of these data. We assessed the predictive performance and variability at the sewershed level and compared the effects of adding or replacing wastewater data to case and death reports.

FINDINGS

Adding wastewater data minimally improved the predictive performance of nowcasts compared to a model fitted to case and death data (Weighted Interval Score (WIS) 0.208 versus 0.223), and reduced the predictive performance compared to a model fitted to deaths data (WIS 0.517 versus 0.500). Adding wastewater data to deaths data improved the nowcasts agreement to estimates from models using cases and deaths data. These findings were consistent across individual sewersheds as well as for models fit to the aggregated total data of 5 sewersheds. Retrospective reconstructions of epidemiological dynamics created using different combinations of data were in general agreement (coverage >75%).

INTERPRETATION

These findings show wastewater data may be valuable for infectious disease nowcasting when clinical surveillance data are absent, such as early in a pandemic or in low-resource settings where systematic collection of epidemiologic data is difficult.

Bayesian monitoring of COVID-19 in Sweden

In an effort to provide regional decision support for the public healthcare, we design a data-driven compartment-based model of COVID-19 in Sweden. From national hospital statistics we derive parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally propose a posterior marginal estimator which provides for an improved temporal resolution of the reproduction number estimate as well as supports robustness checks via a parametric bootstrap procedure. From our computational approach we obtain a Bayesian model of predictive value which provides important insight into the progression of the disease, including estimates of the effective reproduction number, the infection fatality rate, and the regional-level immunity. We successfully validate our posterior model against several different sources, including outputs from extensive screening programs. Since our required data in comparison is easy and non-sensitive to collect, we argue that our approach is particularly promising as a tool to support monitoring and decisions within public health. Significance: Using public data from Swedish patient registries we develop a national-scale computational model of COVID-19. The parametrized model produces valuable weekly predictions of healthcare demands at the regional level and validates well against several different sources. We also obtain critical epidemiological insights into the disease progression, including, e.g., reproduction number, immunity and disease fatality estimates. The success of the model hinges on our novel use of filtering techniques which allows us to design an accurate data-driven procedure using data exclusively from healthcare demands, i.e., our approach does not rely on public testing and is therefore very cost-effective.

Vertical and seasonal dynamics of bacterial pathogenic communities at an aged organic contaminated site: Insights into microbial diversity, composition, interactions, and assembly processes

Under the background of the Coronavirus Disease 2019 (COVID-19) pandemic, research on pathogens deserves greater attention in the natural environment, especially in the widely distributed contaminated sites with complicated and severe organic pollution. In this study, the community composition and assembly of soil pathogens identified by the newly-developed 16S-based pipeline of multiple bacterial pathogen detection (MBPD) have been investigated on spatiotemporal scales in the selected organic polluted site. We demonstrated that the richness and diversity of the pathogenic communities were primarily controlled by soil depth, while the structure and composition of pathogenic communities varied pronouncedly with seasonal changes, which were driven by the alterations in both physiochemical parameters and organic contaminants over time. Network analysis revealed that the overwhelmingly positive interactions, identified multiple keystone species, and a well-organized modular structure maintained the stability and functionality of the pathogenic communities under environmental pressures. Additionally, the null-model analysis showed that deterministic processes dominated the pathogenic community assembly across soil profiles. In three seasons, stochasticity-dominated processes in spring and summer changed into determinism-dominated processes in winter. These findings extend our knowledge of the response of the bacterial pathogenic community to environmental disruptions brought on by organic contaminated sites.

Prehospital Cardiac Arrest Should be Considered When Evaluating Coronavirus Disease 2019 Mortality in the United States

BACKGROUND

Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is of high value. Coronavirus disease 2019 (COVID-19) offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance.

OBJECTIVES

This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the COVID-19 emergency. Here fitness for use means the statistical agreement between events across series.

METHODS

Thirteen weekly clinical event series from before and during the COVID-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) COVID-19 attributable mortality, CDC's excess mortality model, national Emergency Medical Services (EMS) calls, and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to Distributed Random Forest models. Models returned the variable importance when predicting the series of interest from the remaining time series.

RESULTS

Model r2 statistics ranged from 0.78 to 0.99 for the share of the volumes predicted correctly. Prehospital data were of high value, and cardiac arrest (CA) prior to EMS arrival was on average the best predictor (tied with study week). COVID-19 Medicare claims volumes can predict COVID-19 death certificates (agreement), while viral respiratory Medicare claim volumes cannot predict Medicare COVID-19 claims (disagreement).

CONCLUSION

Prehospital EMS data should be considered when evaluating the severity of COVID-19 because prehospital CA known to EMS was the strongest predictor on average across indices.

CRISPR/Cas12a-powered evanescent wave fluorescence nanobiosensing platform for nucleic acid amplification-free detection of Staphylococcus aureus with multiple signal enhancements

Although CRISPR-based biosensors for pathogenic detection are highly specific, they not sensitive enough and nucleic acid amplification is generally required to improve their sensitivity. However, this allows only binary operations and significantly limits practical applications. Here, a CRISPR/Cas12a-powered Evanescent wAve fluorescence nanobiosensing plaTform (CREAT) was developed for ultrasensitive nucleic acid amplification-free quantitative detection of pathogens with multiple signal enhancements. In addition to collateral cleavage amplification of the CRISPR/Cas12a system, we constructed nanophotonic structure-based evanescent wave fluorescence enhancement, Mg²⁺ or DNA-mediated fluorescence enhancement, and air-displacement fluorescence enhancement strategies for ultrasensitive detection of Staphylococcus aureus (S. aureus). Especially, the fluorescence signal detected by CREAT can be significantly enhanced by adding a simple air displacement step, thus improving detection sensitivity. This nanobiosensor detected real samples containing S. aureus, with a detection limit of 592 CFU/mL and 13.2 CFU/mL in 45 min and 90 min, respectively, which are comparable to those of RT-qPCR. This paves a new way for simple, rapid, sensitive, robust, and flexible on-site detection of S. aureus as well as other pathogens.

Identification of GINS1 as a therapeutic target in the cancer patients infected with COVID-19: a bioinformatics and system biology approach

BACKGROUND

Coronavirus disease 2019 (COVID-19) caused a series of biological changes in cancer patients which have rendered the original treatment ineffective and increased the difficulty of clinical treatment. However, the clinical treatment for cancer patients infected with COVID-19 is currently unavailable. Since bioinformatics is an effective method to understand undiscovered biological functions, pharmacological targets, and therapeutic mechanisms. The aim of this study was to investigate the influence of COVID-19 infection in cancer patients and to search the potential treatments.

METHODS

Firstly, we obtained the COVID-19-associated genes from seven databases and analyzed the cancer pathogenic genes from Gene Expression Omnibus (GEO) databases, respectively. The Cancer/COVID-19-associated genes were shown by Venn analyses. Moreover, we demonstrated the signaling pathways and biological functions of pathogenic genes in Cancer/COVID-19.

RESULTS

We identified that Go-Ichi-Ni-San complex subunit 1 (GINS1) is the potential therapeutic target in Cancer/COVID-19 by GEPIA. The high expression of GINS1 was not only promoting the development of cancers but also affecting their prognosis. Furthermore, eight potential compounds of Cancer/COVID-19 were identified from CMap and molecular docking analysis.

CONCLUSION

We revealed the GINS1 is a potential therapeutic target in cancer patients infected with COVID-19 for the first time, as COVID-19 will be a severe and prolonged pandemic. However, the findings have not been verified actually cancer patients infected with COVID-19, and further studies are needed to demonstrate the functions of GINS1 and the clinical treatment of the compounds.

How COVID-19 shaped mental health: from infection to pandemic effects

The Coronavirus Disease 2019 (COVID-19) pandemic has threatened global mental health, both indirectly via disruptive societal changes and directly via neuropsychiatric sequelae after SARS-CoV-2 infection. Despite a small increase in self-reported mental health problems, this has (so far) not translated into objectively measurable increased rates of mental disorders, self-harm or suicide rates at the population level. This could suggest effective resilience and adaptation, but there is substantial heterogeneity among subgroups, and time-lag effects may also exist. With regard to COVID-19 itself, both acute and post-acute neuropsychiatric sequelae have become apparent, with high prevalence of fatigue, cognitive impairments and anxiety and depressive symptoms, even months after infection. To understand how COVID-19 continues to shape mental health in the longer term, fine-grained, well-controlled longitudinal data at the (neuro)biological, individual and societal levels remain essential. For future pandemics, policymakers and clinicians should prioritize mental health from the outset to identify and protect those at risk and promote long-term resilience.

A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices

Screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among symptomatic and asymptomatic patients offers unique opportunities for curtailing the transmission of novel coronavirus disease 2019, commonly known as COVID-19. Molecular diagnostic techniques, namely reverse transcription loop-mediated isothermal amplification (RT-LAMP), reverse transcription-polymerase chain reaction (RT-PCR), and immunoassays, have been frequently used to identify COVID-19 infection. Although these techniques are robust and accurate, mass testing of potentially infected individuals has shown difficulty due to the resources, manpower, and costs it entails. Moreover, as these techniques are typically used to test symptomatic patients, healthcare systems have failed to screen asymptomatic patients, whereas the spread of COVID-19 by these asymptomatic individuals has turned into a crucial problem. Besides, respiratory infections or cardiovascular conditions generally demonstrate changes in physiological parameters, namely body temperature, blood pressure, and breathing rate, which signifies the onset of diseases. Such vitals monitoring systems have shown promising results employing artificial intelligence (AI). Therefore, the potential use of wearable devices for monitoring asymptomatic COVID-19 individuals has recently been explored. This work summarizes the efforts that have been made in the domains from laboratory-based testing to asymptomatic patient monitoring via wearable systems.

Addressing biohazards to food security in primary production

This review addresses ways to prepare for and to mitigate effects of biohazards on primary production of crops and livestock. These biohazards can be natural or intentional introductions of pathogens, and they can cause major economic damage to farmers, the agricultural industry, society, and international trade. Agroterrorism is the intentional introduction of animal or plant pathogens into agricultural production systems with the intention to cause socioeconomic harm and generate public fear. Although few acts of agroterrorism are reported, the threat of agroterrorism in Europe is real. New concerns about threats arise from the rapid advancements in biotechnology and emerging technologies. FORSA, an analytical framework for risk and vulnerability analysis, was used to review how to prepare for and mitigate the possible effects of natural or intentional biohazards in agricultural production. Analyzing the effects of a biohazard event involves multiple scientific disciplines. A comprehensive analysis of biohazards therefore requires a systems approach. The preparedness and ability to manage events are strengthened by bolstered farm biosecurity, increased monitoring and laboratory capacity, improved inter-agency communication and resource allocation. The focus of this review is on Europe, but the insights gained have worldwide applications. The analytical framework used here is compared to other frameworks. With climate change, Covid-19 and the war in Ukraine, the supply chains are challenged, and we foresee increasing food prices associated with social tensions. Our food supply chain becomes more fragile with more unknowns, thereby increasing the needs for risk and vulnerability analyses, of which FORSA is one example.

Remdesivir use and risks of acute kidney injury and acute liver injury among patients hospitalised with COVID-19: a self-controlled case series study

BACKGROUND AND AIM

To investigate and quantify the risks of AKI and ALI associated with remdesivir use, given the underlying diseases of SARS-CoV-2 infection.

METHODS

This self-controlled case series (SCCS) study was conducted using electronic hospital records between 23 January 2020 and 31 January 2021 as retrieved from the Hong Kong Hospital Authority which manages all laboratory-confirmed COVID-19 cases in Hong Kong. Outcomes of AKI and ALI were defined using the KDIGO Guideline and Asia Pacific Association of Study of Liver consensus guidelines. Incidence rate ratios (IRR) for AKI and ALI following the administration of remdesivir (exposure) in comparison to a non-exposure period were estimated using the conditional Poisson regression models.

RESULTS

Of 860 COVID-19 patients administered remdesivir during hospitalisation, 334 (38.8%) and 137 (15.9%) had incident ALI and AKI, respectively. Compared with the baseline period, both ALI and AKI risks were increased significantly during the pre-exposure period (ALI: IRR = 6.169, 95% CI = 4.549-8.365; AKI: IRR = 7.074, 95% CI = 3.763-13.298) and remained elevated during remdesivir treatment. Compared to the pre-exposure period, risks of ALI and AKI were not significantly higher in the first 2 days of remdesivir initiation (ALI: IRR = 1.261, 95% CI = 0.915-1.737; AKI: IRR = 1.261, 95% CI = 0.889-1.789) and between days 2 and 5 of remdesivir treatment (ALI: IRR = 1.087, 95% CI = 0.793-1.489; AKI: IRR = 1.152, 95% CI = 0.821-1.616).

CONCLUSION

The increased risks of AKI and ALI associated with intravenous remdesivir treatment for COVID-19 may be due to the underlying SARS-CoV-2 infection. The risks of AKI and ALI were elevated in the pre-exposure period, yet no such increased risks were observed following remdesivir initiation when compared to the pre-exposure period.

Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.

Mental disorders following COVID-19 and other epidemics: a systematic review and meta-analysis

COVID-19 has imposed a very substantial direct threat to the physical health of those infected, although the corollary impact on mental health may be even more burdensome. Here we focus on assessing the mental health impact of COVID-19 and of other epidemics in the community. We searched five electronic databases until December 9, 2020, for all peer-reviewed original studies reporting any prevalence or correlates of mental disorders in the general population following novel epidemics in English, Chinese or Portuguese. We synthesised prevalence estimates from probability samples during COVID-19 and past epidemics. The meta-analytical effect size was the prevalence of relevant outcomes, estimated via random-effects model. I² statistics, Doi plots and the LFK index were used to examine heterogeneity and publication bias. This study is pre-registered with PROSPERO, CRD42020179105. We identified 255 eligible studies from 50 countries on: COVID-19 (n = 247 studies), severe acute respiratory syndrome (SARS; n = 5), Ebola virus disease (n = 2), and 1918 influenza (n = 1). During COVID-19, we estimated the point prevalence for probable anxiety (20.7%, 95% CI 12.9-29.7), probable depression (18.1%, 13.0-23.9), and psychological distress (13.0%, 0-34.1). Correlates for poorer mental health include female sex, lower income, pre-existing medical conditions, perceived risk of infection, exhibiting COVID-19-like symptoms, social media use, financial stress, and loneliness. Public trust in authorities, availability of accurate information, adoption of preventive measures and social support were associated with less morbidity. The mental health consequences of COVID-19 and other epidemics could be comparable to major disasters and armed conflicts. The considerable heterogeneity in our analysis indicates that more random samples are needed. Health-care professionals should be vigilant of the psychological toll of epidemics, including among those who have not been infected.

Performance of early warning signals for disease re-emergence: A case study on COVID-19 data

Developing measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals (EWS) from complex systems theory have been introduced to detect impending critical transitions and extend the set of indicators. However, it is still debated whether they are generically applicable or potentially sensitive to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data has, so far, been limited. Hence, verifying EWS performance remains a challenge. In this study, we tackle this question by analyzing the performance of common EWS, such as increasing variance and autocorrelation, in detecting the emergence of COVID-19 outbreaks in various countries. Our work illustrates that these EWS might be successful in detecting disease emergence when some basic assumptions are satisfied: a slow forcing through the transitions and not-fat-tailed noise. In uncertain cases, we observe that noise properties or commensurable time scales may obscure the expected early warning signals. Overall, our results suggest that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to certain features of the underlying dynamics. Our findings thus pave a connection between theoretical and empirical studies, constituting a further step towards the application of EWS indicators for informing public health policies.

Genomic epidemiology of SARS-CoV-2 under an elimination strategy in Hong Kong

Hong Kong employed a strategy of intermittent public health and social measures alongside increasingly stringent travel regulations to eliminate domestic SARS-CoV-2 transmission. By analyzing 1899 genome sequences (>18% of confirmed cases) from 23-January-2020 to 26-January-2021, we reveal the effects of fluctuating control measures on the evolution and epidemiology of SARS-CoV-2 lineages in Hong Kong. Despite numerous importations, only three introductions were responsible for 90% of locally-acquired cases. Community outbreaks were caused by novel introductions rather than a resurgence of circulating strains. Thus, local outbreak prevention requires strong border control and community surveillance, especially during periods of less stringent social restriction. Non-adherence to prolonged preventative measures may explain sustained local transmission observed during wave four in late 2020 and early 2021. We also found that, due to a tight transmission bottleneck, transmission of low-frequency single nucleotide variants between hosts is rare.

Human Research Study of Particulate Propagation Distance from Human Respiratory Function

Background

Airborne viral pathogens like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be encapsulated and transmitted through liquid droplets/aerosols formed during human respiratory events.

Methods

The number and extent of droplets/aerosols at distances between 1 and 6 ft (0.305–1.829 m) for a participant wearing no face covering, a cotton single-layer cloth face covering, and a 3-layer disposable face covering were measured for defined speech and cough events. The data include planar particle imagery to illuminate emissions by a light-sheet and local aerosol/droplet probes taken with phase Doppler interferometry and an aerodynamic particle sizer.

Results

Without face coverings, droplets/aerosols were detected up to a maximum of 1.25 m (4.1ft ± 0.22–0.28 ft) during speech and up to 1.37 m (4.5ft ± 0.19–0.33 ft) while coughing. The cloth face covering reduced maximum axial distances to 0.61 m (2.0 ft ± 0.11–0.15 ft) for speech and to 0.67 m (2.2 ft ± 0.02–0.20 ft) while coughing. Using the disposable face covering, safe distance was reduced further to 0.15 m (0.50 ft ± 0.01–0.03 ft) measured for both emission scenarios. In addition, the use of face coverings was highly effective in reducing the count of expelled aerosols.

Conclusions

The experimental study indicates that 0.914 m (3 ft) physical distancing with face coverings is equally as effective at reducing aerosol/droplet exposure as 1.829 m (6 ft) with no face covering.

Timeline of SARS-CoV-2 Spread in Italy: Results from an Independent Serological Retesting

The massive emergence of COVID-19 cases in the first phase of pandemic within an extremely short period of time suggest that an undetected earlier circulation of SARS-CoV-2 might have occurred. Given the importance of this evidence, an independent evaluation was recommended by the World Health Organization (WHO) to test a subset of samples selected on the level of positivity in ELISA assays (positive, low positive, negative) detected in our previous study of prepandemic samples collected in Italy. SARS-CoV-2 antibodies were blindly retested by two independent centers in 29 blood samples collected in the prepandemic period in Italy, 29 samples collected one year before and 11 COVID-19 control samples. The methodologies used included IgG-RBD/IgM-RBD ELISA assays, a qualitative micro-neutralization CPE-based assay, a multiplex IgG protein array, an ELISA IgM kit (Wantai), and a plaque-reduction neutralization test. The results suggest the presence of SARS-CoV-2 antibodies in some samples collected in the prepandemic period, with the oldest samples found to be positive for IgM by both laboratories collected on 10 October 2019 (Lombardy), 11 November 2019 (Lombardy) and 5 February 2020 (Lazio), the latter with neutralizing antibodies. The detection of IgM and/or IgG binding and neutralizing antibodies was strongly dependent on the different serological assays and thresholds employed, and they were not detected in control samples collected one year before. These findings, although gathered in a small and selected set of samples, highlight the importance of harmonizing serological assays for testing the spread of the SARS-CoV-2 virus and may contribute to a better understanding of future virus dynamics.

The Hard Lessons and Shifting Modeling Trends of COVID-19 Dynamics: Multiresolution Modeling Approach

The COVID-19 pandemic has placed epidemiologists, modelers, and policy makers at the forefront of the global discussion of how to control the spread of coronavirus. The main challenges confronting modelling approaches include real-time projections of changes in the numbers of cases, hospitalizations, and fatalities, the consequences of public health policy, the understanding of how best to implement varied non-pharmaceutical interventions and potential vaccination strategies, now that vaccines are available for distribution. Here, we: (i) review carefully selected literature on COVID-19 modeling to identify challenges associated with developing appropriate models along with collecting the fine-tuned data, (ii) use the identified challenges to suggest prospective modeling frameworks through which adaptive interventions such as vaccine strategies and the uses of diagnostic tests can be evaluated, and (iii) provide a novel Multiresolution Modeling Framework which constructs a multi-objective optimization problem by considering relevant stakeholders’ participatory perspective to carry out epidemic nowcasting and future prediction. Consolidating our understanding of model approaches to COVID-19 will assist policy makers in designing interventions that are not only maximally effective but also economically beneficial.

Interspecies Jumping of Bat Coronaviruses

In the last two decades, several coronavirus (CoV) interspecies jumping events have occurred between bats and other animals/humans, leading to major epidemics/pandemics and high fatalities. The SARS epidemic in 2002/2003 had a ~10% fatality. The discovery of SARS-related CoVs in horseshoe bats and civets and genomic studies have confirmed bat-to-civet-to-human transmission. The MERS epidemic that emerged in 2012 had a ~35% mortality, with dromedaries as the reservoir. Although CoVs with the same genome organization (e.g., Tylonycteris BatCoV HKU4 and Pipistrellus BatCoV HKU5) were also detected in bats, there is still a phylogenetic gap between these bat CoVs and MERS-CoV. In 2016, 10 years after the discovery of Rhinolophus BatCoV HKU2 in Chinese horseshoe bats, fatal swine disease outbreaks caused by this virus were reported in southern China. In late 2019, an outbreak of pneumonia emerged in Wuhan, China, and rapidly spread globally, leading to >4,000,000 fatalities so far. Although the genome of SARS-CoV-2 is highly similar to that of SARS-CoV, patient zero and the original source of the pandemic are still unknown. To protect humans from future public health threats, measures should be taken to monitor and reduce the chance of interspecies jumping events, either occurring naturally or through recombineering experiments.

[Current issues of treatment and rehabilitation of patients with neurological disorders and the consequences of COVID-19. Resolution of Advisory Board]

Last year the global medical community faced the pandemic of the new coronavirus infection caused by SARS-CoV-2. To date, there is considerable expert experience, which indicates that the brain, along with the corresponding respiratory system, is a target organ for a new coronavirus infection. Moreover, a number of symptoms from the central and peripheral nervous system can persist for several weeks, months, and even tens of months. To designate such protracted clinical conditions, a new definition was introduced: «Post-COVID-19 Condition». Advisory Board of Neurologists and Rehabilitation Therapists met to, discuss of practical experience and taking into account scientific information about COVID-19, which was available at the time of the meeting, to develop unified approaches for the management of patients with neurological complications and the consequences of a new coronavirus infection. The Advisory Board worked out a resolution in which formulated the tactics of managing patients with neurological manifestations of COVID-19. The substantiation of the importance and expediency of the development and implementation of a special program of clinical examination of patients who have undergone COVID-19, which would include a clinical examination with a detailed assessment of cognitive functions to early identification and diagnosis of neurodegeneration and subsequent therapy, is given.

SARS-CoV-2 under an elimination strategy in Hong Kong

Hong Kong utilized an elimination strategy with intermittent use of public health and social measures and increasingly stringent travel regulations to control SARS-CoV-2 transmission. By analyzing >1700 genome sequences representing 17% of confirmed cases from 23-January-2020 to 26-January-2021, we reveal the effects of fluctuating control measures on the evolution and epidemiology of SARS-CoV-2 lineages in Hong Kong. Despite numerous importations, only three introductions were responsible for 90% of locally-acquired cases, two of which circulated cryptically for weeks while less stringent measures were in place. We found that SARS-CoV-2 within-host diversity was most similar among transmission pairs and epidemiological clusters due to a strong transmission bottleneck through which similar genetic background generates similar within-host diversity.

ONE SENTENCE SUMMARY

Out of the 170 detected introductions of SARS-CoV-2 in Hong Kong during 2020, three introductions caused 90% of community cases.

Risk communication during COVID-19: A descriptive study on familiarity with, adherence to and trust in the WHO preventive measures

BACKGROUND

Risk communication is a key component of public health interventions during an outbreak. As the coronavirus pandemic unfolded in late 2019, the World Health Organization (WHO) was at the forefront in the development of risk communication strategies. The WHO introduced a range of activities with the purpose of enabling the public to avail verified and timely information on COVID-19 prevention behaviors. Given the various WHO activities to protect the public health during COVID-19, it is important to investigate the extent of familiarity and uptake of the WHO recommendations among the public during the first wave of the pandemic.

METHODS

To do this, we conducted a large-scale Pan-European survey covering around 7500 individuals that are representative of populations from seven European countries, collected online during April 2-April 15, 2020. We use descriptive statistics including proportions and correlations and graphical representations such as bar charts to analyze and display the data.

RESULTS

Our findings suggest that information from the WHO in the context of COVID-19 is well trusted and acted upon by the public. Overall familiarity and adherence were quite high in most countries. Adherence was higher for social distancing recommendations compared to hygiene measures. Familiarity and adherence were higher among older, female, and highly educated respondents. However, country level heterogeneities were observed in the level of trust in information from the WHO, with countries severely affected by the pandemic reporting lower levels of trust.

CONCLUSION

Our findings call for efforts from health authorities to get regular feedback from the public on their familiarity and compliance with recommendations for preventive measures at all stages of the pandemic, to further develop and adapt risk communication as the pandemic evolves.