COVID-19 Surveillance Research Articles

Characterization of Respiratory Viruses in Patients with Acute Respiratory Infection in the City of Barranquilla during the SARS-CoV-2/COVID-19 Pandemic.

Introduction: Severe acute respiratory infection (SARI) is mainly caused by viral pathogens, with a high prevalence in high-risk populations such as infants and older adults. Coinfections by different viruses are frequent and, in some cases, associated with severe disease outcomes. Purpose: The main purpose of this study was to identify respiratory viruses circulating in Barranquilla during the peaks of the COVID-19 pandemic and estimate the prevalence of viral coinfections in samples from individuals with different degrees of respiratory infection. Methods: We received 5083 samples between epidemiological weeks 33-42 of 2021 submitted by the District Health Laboratory of Barranquilla and four local healthcare institutions during COVID-19 surveillance. Among them, we analyzed 101 samples from individuals presenting with influenza-like illness (ILI). Eighteen respiratory viruses, including SARS-CoV-2, were evaluated via qRT-PCR using nasal swabs or nasopharyngeal aspirate samples. Results: Of the 101 study individuals, 56 were male and 45 were female (55.5% and 44.5%, respectively); 25.7% of individuals were infected with at least one of the evaluated viruses. Respiratory syncytial virus (RSV) and human rhinovirus (HRV) were the two most frequently detected viruses (30.7% and 15.4% of total positives, respectively). Coinfections with two or more respiratory viruses accounted for 42% of the total positive cases. Discussion: Our findings indicate the presence of different respiratory viruses in swab or nasopharyngeal aspirate samples from individuals with ILI, including coinfections. These results reveal the circulation of several respiratory viruses in the city of Barranquilla, confirming their importance as potential causes of SARI in Colombia and the need for their active surveillance.

Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods

COVID-19 has a higher rate of morbidity and mortality among dialysis patients than the general population. Identifying infected patients early with the support of predictive models helps dialysis centers implement concerted procedures (e.g., temperature screenings, universal masking, isolation treatments) to control the spread of SARS-CoV-2 and mitigate outbreaks. We collect data from multiple sources, including demographics, clinical, treatment, laboratory, vaccination, socioeconomic status, and COVID-19 surveillance. Previous early prediction models, such as logistic regression, SVM, and XGBoost, require sophisticated feature engineering and need improved prediction performance. We create deep learning models, including Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), to predict SARS-CoV-2 infections during incubation. Our study shows deep learning models with minimal feature engineering can identify those infected patients more accurately than previously built models. Our Long Short-Term Memory (LSTM) model consistently performed well, with an AUC exceeding 0.80, peaking at 0.91 in August 2021. The CNN model also demonstrated strong results with an AUC above 0.75. Both models outperformed previous best XGBoost models by over 0.10 in AUC. Prediction accuracy declined as the pandemic evolved, dropping to approximately 0.75 between September 2021 and January 2022. Maintaining a 20% false positive rate, our LSTM and CNN models identified 66% and 64% of positive cases among patients, significantly outperforming XGBoost models at 42%. We also identify key features for dialysis patients by calculating the gradient of the output with respect to the input features. By closely monitoring these factors, dialysis patients can receive earlier diagnoses and care, leading to less severe outcomes. Our research highlights the effectiveness of deep neural networks in analyzing longitudinal data, especially in predicting COVID-19 infections during the crucial incubation period. These deep network approaches surpass traditional methods relying on aggregated variable means, significantly improving the accurate identification of SARS-CoV-2 infections.

Influenza-Like Illness in Lesotho From July 2020 to July 2021: Population-Based Participatory Surveillance Results.

Participatory surveillance involves at-risk populations reporting their symptoms using technology. In Lesotho, a landlocked country of 2 million people in Southern Africa, laboratory and case-based COVID-19 surveillance systems were complemented by a participatory surveillance system called "LeCellPHIA" (Lesotho Cell Phone Population-Based HIV Impact Assessment Survey). This report describes the person, place, and time characteristics of influenza-like illness (ILI) in Lesotho from July 15, 2020, to July 15, 2021, and reports the risk ratio of ILI by key demographic variables. LeCellPHIA employed interviewers to call participants weekly to inquire about ILI. The average weekly incidence rate for the year-long period was created using a Quasi-Poisson model, which accounted for overdispersion. To identify factors associated with an increased risk of ILI, we conducted a weekly data analysis by fitting a multilevel Poisson regression model, which accounted for 3 levels of clustering. The overall response rate for the year of data collection was 75%, which resulted in 122,985 weekly reports from 1776 participants. ILI trends from LeCellPHIA mirrored COVID-19 testing data trends, with an epidemic peak in mid to late January 2021. Overall, any ILI symptoms (eg, fever, dry cough, and shortness of breath) were reported at an average weekly rate of 879 per 100,000 (95% CI 782-988) persons at risk. Compared to persons in the youngest age group (15-19 years), all older age groups had an elevated risk of ILI, with the highest risk of ILI in the oldest age group (≥60 years; risk ratio 2.6, 95% CI 1.7-3.8). Weekly data were shared in near real time with the National COVID-19 Secretariat and other stakeholders to monitor ILI trends, identify and respond to increases in reports of ILI, and inform policies and practices designed to reduce COVID-19 transmission in Lesotho. LeCellPHIA, an innovative and cost-effective system, could be replicated in countries where cell phone ownership is high but internet use is not yet high enough for a web- or app-based surveilance system.

Experiences of community engagement for SARS-COV-2 and COVID-19 community surveillance in a rural and urban population in South Western Uganda

Background: We describe lessons learnt from community engagement activities for the successful implementation of a COVID-19 community surveillance study in Kalungu district, south-western Uganda. Methods: One rural and one urban site were selected for COVID-19/SARS-CoV-2 community surveillance in selected households and three public health facilities. COVID-19 pandemic and national COVID-19 protection measures were described and explained in meetings with: district, sub-county/town, village council officials, religious leaders, health workers and Community Advisory Board members. The community surveillance study was introduced to the meeting attendees and minutes captured in English/Luganda (subsequently translated to English). All minutes were manually coded and analysed thematically. Results: The minutes revealed that community members were generally supportive of the COVID-19 surveillance study. Members requested wider communities’ sensitisation about COVID-19 and the survey beyond the selected households. Misinformation and mistrust of Government statements were widespread. People in the community did not understand the `random sample’ selection for the study. Providing appropriate medical care, face masks and honouring appointments for study participants and ensuring that COVID-19 prevention measures were followed during the study improved participation. Conclusion: Successful COVID-19 community surveillance required continuous, active community engagement between the research team, and community stakeholders while maximising previous gains and exploiting existing resources. Keywords: SARS-COV-2; COVID-19; community engagement; surveillance; acceptability; East Africa.

Wastewater-based surveillance for Hepatitis A virus, Enterovirus, Poliovirus, and SARS-CoV-2 in São Tomé and Príncipe: A pilot study

BackgroundWastewater-based surveillance is a valuable tool for monitoring pathogen transmission in communities, especially in regions where formal surveillance systems are limited. AimThe aim of this study was to implement and evaluate a wastewater-based monitoring system for viral pathogens in São Tomé and Príncipe. MethodsA total of 122 water samples were collected bi-weekly from June 2022 to July 2023 at six locations in São Tomé city and analysed for molecular detection of Hepatitis A Virus (HAV), Enterovirus (EV), Poliovirus (PV), SARS-CoV-2, as well as JC-Polyomavirus (JCPyV) and pepper mild mottle virus (PMMoV) as indicators of human contamination. Prevalence was analysed per pathogen and across sampling locations. Results for SARS-CoV-2 were assessed together with notifications from national COVID-19 surveillance. Further, we estimated resources needed to establish a wastewater-based approach to assess community-level transmission of viral pathogens. ResultsAll 122 and 117 samples were found positive for PMMoV and JCPyV, respectively, demonstrating a high level of human contamination at all sampling locations. The prevalence of HAV and EV ranged from 0 % to 59 % and 56 % respectively. Consistent with national surveillance data the highest proportion of SARS-CoV-2 positive water samples coincides with the highest number of COVID-19 cases reported during the study, demonstrating the potential of wastewater-based surveillance to identify signals. In addition, for SARS-CoV-2 this approach provided evidence of continuous circulation of the virus in the community, most importantly during weeks when no COVID-19 cases were reported. ConclusionOur findings provide evidence of high transmission of HAV and EV in communities in São Tomé and continuous circulation of SARS-CoV-2, even in weeks without COVID-19 case notifications. This study demonstrates that monitoring of viral pathogens in humanly impacted open water streams and sewage tanks is a valuable tool to complement clinical surveillance in resource-limited settings.

Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

Accuracy of COVID-19 Prediction Modeling Techniques

The unprecedented impact of the COVID-19 pandemic has revealed that forecasting capability is critically needed in making strategic decisions and formulating reasonable countermeasures. This study aimed to assess the predictive accuracy in forecasting the numbers of COVID-19 cases using Thailand’s national COVID-19 surveillance database from January 2020– June 2021 based on three analytical models: a susceptible-exposed-infectious-recovery compartmental model, an auto-regressive integrated moving average model, and a long short-term memory (LSTM) network model. All forecasting methods had model parameters adjusted weekly according to the most recent situation and predictive accuracy measures, including the mean absolute percent error (MAPE). We found that the MAPE values ranged from 19.65%–22.54%, 28.95%–32.35%, 47.78%–53.55%, and 75.03–84.91% for forecasting one, two, four, and eight weeks ahead, respectively. Among the three models, the LSTM model had slightly higher accuracy than the other two models within the same forecasting range. These prediction models can be used for short-range forecasts in other similar settings while long-range forecasting requires monitoring and updating periodically.

Validation of Saliva as the Clinical Specimen Type for a University-Wide COVID-19 Surveillance Program.

At the beginning of the COVID-19 pandemic, the Georgia Institute of Technology made the decision to keep the university doors open for on-campus attendance. To manage COVID-19 infection rates, internal resources were applied to develop and implement a mass asymptomatic surveillance program. The objective was to identify infections early for proper follow-on verification testing, contact tracing, and quarantine/isolation as needed. Program success depended on frequent and voluntary sample collection from over 40,000 students, faculty, and staff personnel. At that time, the nasopharyngeal (NP) swab, not saliva, was the main accepted sample type for COVID-19 testing. However, due to collection discomfort and the inability to be self-collected, the NP swab was not feasible for voluntary and frequent self-collection. Therefore, saliva was selected as the clinical sample type and validated. A saliva collection kit and a sample processing and analysis workflow were developed. The results of a clinical sample-type comparison study between co-collected and matched NP swabs and saliva samples showed 96.7% positive agreement and 100% negative agreement. During the Fall 2020 and Spring 2021 semesters, 319,988 samples were collected and tested. The program resulted in maintaining a low overall mean positivity rate of 0.78% and 0.54% for the Fall 2020 and Spring 2021 semesters, respectively. For this high-throughput asymptomatic COVID-19 screening application, saliva was an exceptionally good sample type.

Enabling quantitative comparison of wastewater surveillance data across methods through data standardization without method standardization

Wastewater surveillance for COVID-19 and other pathogens has expanded globally. Rapid development and availability of various assays has facilitated swift adoption of wastewater surveillance in localities with diverse requirements. However, it presents challenges in comparing data due to methodological variations. Using surrogates for recovery control to address quantification biases has limitations as the recovery of surrogates and target pathogens often diverges significantly. Using non-spiked field-obtained wastewater samples as reference samples in an inter-lab study, this article proposes a straightforward, inexpensive, and most representative way of measuring relative quantification biases that occurs in analyzing field wastewater samples. Five labs participated in the study, testing five types of assays, resulting in a total of seven methods of lab-assay combinations. Each method quantified the concentration of SARS-CoV-2 and pepper mild mottle virus (PMMoV) RNAs in two types of reference samples. The results showed significant variations in quantification among methods, but the relative quantification biases were consistent across reference samples. This suggests that relative quantification biases measured with the reference samples are contingent on methods rather than wastewater samples, and that the once-determined method-specific factors can be used to correct for quantification biases in routine wastewater surveillance results. Subsequent data standardization was performed on year-long observational data from seven cities, serving as a preliminary validation of the proposed approach. This process demonstrated the potential for quantitative data comparison through the bias correction factors obtained in this inter-lab study.

Communication Disparities and Trusted Sources of COVID-19 Information in Massachusetts School Districts.

The COVID-19 pandemic led to the closure of prekindergarten to grade 12 schools and an inequitable return to full-time in-person learning. To explore how ethnic and racial differences across school districts in Massachusetts correlate with parents' attitudes, beliefs, and trusted sources of information about COVID-19 and mitigation strategies. An electronic survey was distributed by school administrators to parents and guardians in November and December 2021 using existing school district contact lists and established methods of communication (email in 2 school districts; email and text message in 1 district). Three school districts in Massachusetts (Chelsea, Medford, and Somerville). Parents of prekindergarten to grade 12 school students attending public schools. Parental attitudes and beliefs regarding mitigation strategies for COVID-19 (surveillance testing, masking, and vaccination); trusted information sources about COVID-19; preferred methods of communication from schools. A total of 1496 survey responses were analyzed. Chelsea respondents were predominantly Hispanic/LatinX (88%); Medford and Somerville were predominantly White/non-Hispanic (80% and 68%, respectively). Testing, masks, and vaccination were supported by >80% of parents/guardians across districts. However, there were statistically significant differences between school districts regarding participation in testing programs, implications of a child testing positive, vaccination of young children, communication preferences, and trusted sources of information. Although primarily focused on COVID-19, these results highlight opportunities for public health personnel and school administrators to work directly with parents and guardians in their school districts to improve communication strategies and be a trusted source of information for a variety of public health issues.

Emerging Omicron Subvariants EG.5 and BA.2.86: Implications for COVID-19 Immunity and Surveillance

The SARS-CoV-2 virus, responsible for the COVID-19 pandemic, has continuously evolved since its emergence. Among its variants, the Omicron lineage has become globally dominant, displaying a diverse range of sublineages. Two significant sub lineages within Omicron are EG.5 and BA.2.8, which have gained attention due to their unique characteristics and potential implications. EG.5 is characterized by a constellation of mutations, including N501Y, L452R, and D614G. Similarly, BA.2.8 carries the L452Q and P681R mutations, which enhance its ability to evade antibodies elicited by prior infection or vaccination. Similar to those in the EG.5, the clinical severity and impact of BA.2.8 on public health measures are still being assessed. These sublineages, with their unique genetic profiles and transmissibility characteristics, pose challenges to virulence response efforts. Continued surveillance, genomic sequencing, and research are essential to understand their behavior, assess their impact, and inform public health strategies accordingly. By a comprehensive synthesis of the literature, genomic data, and epidemiological insights, this study provides an analysis of Omicron mutations’ transmission dynamics, and clinical implications associated with these variants. The significance of variant surveillance in informing public health responses to the COVID-19 pandemic is underscored, highlighting the critical role of genomic sequencing in tracking viral evolution and guiding interventions. This manuscript aimed to elucidate the mechanisms underlying variant emergence, assess vaccine effectiveness against emerging strains, and explore the broader implications for global public health. This study provides insights into vaccine-induced immunity and the potential impact of variants on vaccine efficacy. Overall, this communication aims to inform public health practitioners, policymakers, and researchers engaged in the battle against COVID-19, offering actionable insights to mitigate the spread of the virus and improve pandemic response strategies.

Detection of COVID-19 incidence among attendees of the 2022 Hajj season

ObjectivesThis study investigated the presence of COVID-19 cases among pilgrims, health care workers (HCWs), and non-HCWs of Hajj 2022. MethodsNasopharyngeal samples were collected from 491 attendees of Hajj 2022. All participants received three doses of COVID-19 vaccines. Paired (n = 138; 69 participants) and unpaired (n = 422; 422 participants) nasopharyngeal swabs were subjected to reverse transcription–polymerase chain reaction targeting E gene of β-coronaviruses and RNA-dependent RNA polymerase of SARS-CoV-2. The results were linked to the participants’ profiles, including role during Hajj, presence of respiratory symptoms or comorbidities, contact with symptomatic individuals, smoking status, and COVID-19 recovery. ResultsA total of 20 (20 of 560; 3.6%) samples tested positive for COVID-19. Most cases (18 of 20; 90%) were pilgrims and non-HCWs. Six (30%) samples belonged to participants with previous positive reverse transcription–polymerase chain reaction. A total of 12 (60%) samples belonged to participants with respiratory symptoms. Three (15%) cases were linked to participants who had contact with individuals with respiratory symptoms. All cases belonged to individuals with no comorbidities, apart from a single case who has a chronic sinusitis. Five (25%) cases were smokers. No significant association was found between positive COVID-19 test and participants’ profiles. ConclusionsFew COVID-19 cases were detected in this study. Sustainable surveillance of COVID-19 and other respiratory viruses during Hajj seasons remains necessary.

SARS-CoV-2 infection among Colorado adults working outside the home: Occupation, race and ethnicity and mask use.

The workplace is an important setting for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure and transmission. Using data from a large case-control study in Colorado during 2021 and 2022, we aimed to evaluate working outside the home and SARS-CoV-2 infection, the racial and ethnic distribution of workers in occupations associated with infection, and workplace face mask use. Cases were Colorado adults with a positive SARS-CoV-2 test by reverse transcription-polymerase chain reaction (RT-PCR) reported to Colorado's COVID-19 surveillance system selected from surveillance data ≤12 days after their specimen collection date. Control participants were randomly selected adult Coloradans with a RT-PCR-confirmed negative SARS-CoV-2 test result reported to the same surveillance system. Working outside the home was associated with infection (odds ratio [OR] = 1.46, 95% confidence interval [CI]: 1.39-1.54). Among participants working outside the home, "Food Preparation and Serving Related" (aOR = 2.35, 95% CI: 1.80-3.06), "Transportation and Material Moving" (aOR = 2.09, 95% CI: 1.62-2.69), "Construction and Extraction" (aOR = 1.88, 95% CI: 1.36-2.59), "Protective Service" (aOR = 1.60, 95% CI: 1.15-2.24), and "Sales and Related" (aOR = 1.44, 95% CI: 1.22-1.69) were occupational categories most strongly associated with infection. American Indian/Alaskan Native, Black, and Hispanic/Latino participants were more likely than others to work in occupational categories with the highest odds of infection (p < 0.05). Cases were less likely than controls to report always wearing a mask (31.9% vs. 41.5%) and wearing a KN95/N95/KF94 mask (16.8% vs. 27.2%) at work. These findings emphasize the importance of occupation and workplace mask use in the COVID-19 pandemic and its disproportionate racial/ethnic impact on workers.

A dynamic ensemble model for short-term forecasting in pandemic situations.

During the COVID-19 pandemic, many hospitals reached their capacity limits and could no longer guarantee treatment of all patients. At the same time, governments endeavored to take sensible measures to stop the spread of the virus while at the same time trying to keep the economy afloat. Many models extrapolating confirmed cases and hospitalization rate over short periods of time have been proposed, including several ones coming from the field of machine learning. However, the highly dynamic nature of the pandemic with rapidly introduced interventions and new circulating variants imposed non-trivial challenges for the generalizability of such models. In the context of this paper, we propose the use of ensemble models, which are allowed to change in their composition or weighting of base models over time and could thus better adapt to highly dynamic pandemic or epidemic situations. In that regard, we also explored the use of secondary metadata-Google searches-to inform the ensemble model. We tested our approach using surveillance data from COVID-19, Influenza, and hospital syndromic surveillance of severe acute respiratory infections (SARI). In general, we found ensembles to be more robust than the individual models. Altogether we see our work as a contribution to enhance the preparedness for future pandemic situations.

Evaluation of Self-collected Saliva Samples Without Viral Transport Media for SARS-CoV-2 Testing via RT-PCR and Comparison of Amplicon Sequences Against Commonly Used Primers in Diagnostic Assays.

Mass screening for SARS-CoV-2 using nasopharyngeal swabs (NPS) is costly, uncomfortable for patients, and increases the chance of virus exposure to health care workers. Therefore, this study focused on determining if self-collected unpreserved saliva can be an effective alternative to NPS collection in COVID-19 surveillance. In this study, patients being tested for SARS-CoV-2 using NPS were asked to provide a saliva sample to compare their results. NPS samples were evaluated for SARS-CoV-2 using BioFire® FilmArray® Torch® or Cepheid® GeneXpert® systems while saliva samples were evaluated using an in-house developed reverse transcriptase polymerase chain reaction (RT-PCR) which targeted the Envelope (E) and Nucleocapsid (N) genes. Detection of SARS-CoV-2 using self-collected saliva was found to be only slightly less accurate (<5%) than testing using NPS. In addition, initial saliva RT-PCR identified 27 positive subjects, 18 of which provided amplicons sufficient for confirmatory sequencing. The sequencing data showed a genetic shift in the virus within our population sometime between 22 June and July 8, 2021 from Alpha to Delta variant. The saliva sample collection method identifies the E gene in SARS COVID-2 samples which provides an alternative specimen source to the NPS. This identifies the S gene and ORF1ab. Saliva collection is more convenient to the patient, yields comparable results to NPS collection, and potentially increases Covid-19 surveillance.

Collective Intelligence-Based Participatory COVID-19 Surveillance in Accra, Ghana: Pilot Mixed Methods Study.

Infectious disease surveillance is difficult in many low- and middle-income countries. Information market (IM)-based participatory surveillance is a crowdsourcing method that encourages individuals to actively report health symptoms and observed trends by trading web-based virtual "stocks" with payoffs tied to a future event. This study aims to assess the feasibility and acceptability of a tailored IM surveillance system to monitor population-level COVID-19 outcomes in Accra, Ghana. We designed and evaluated a prediction markets IM system from October to December 2021 using a mixed methods study approach. Health care workers and community volunteers aged ≥18 years living in Accra participated in the pilot trading. Participants received 10,000 virtual credits to trade on 12 questions on COVID-19-related outcomes. Payoffs were tied to the cost estimation of new and cumulative cases in the region (Greater Accra) and nationwide (Ghana) at specified future time points. Questions included the number of new COVID-19 cases, the number of people likely to get the COVID-19 vaccination, and the total number of COVID-19 cases in Ghana by the end of the year. Phone credits were awarded based on the tally of virtual credits left and the participant's percentile ranking. Data collected included age, occupation, and trading frequency. In-depth interviews explored the reasons and factors associated with participants' user journey experience, barriers to system use, and willingness to use IM systems in the future. Trading frequency was assessed using trend analysis, and ordinary least squares regression analysis was conducted to determine the factors associated with trading at least once. Of the 105 eligible participants invited, 21 (84%) traded at least once on the platform. Questions estimating the national-level number of COVID-19 cases received 13 to 19 trades, and obtaining COVID-19-related information mainly from television and radio was associated with less likelihood of trading (marginal effect: -0.184). Individuals aged <30 years traded 7.5 times more and earned GH ¢134.1 (US $11.7) more in rewards than those aged >30 years (marginal effect: 0.0135). Implementing the IM surveillance was feasible; all 21 participants who traded found using IM for COVID-19 surveillance acceptable. Active trading by friends with communal discussion and a strong onboarding process facilitated participation. The lack of bidirectional communication on social media and technical difficulties were key barriers. Using an IM system for disease surveillance is feasible and acceptable in Ghana. This approach shows promise as a cost-effective source of information on disease trends in low- and middle-income countries where surveillance is underdeveloped, but further studies are needed to optimize its use.

Toward better pandemic governance and preparedness: South Korea’s whole-of-nation approach to COVID-19

BackgroundThe profound impact of coronavirus disease (COVID-19) has led to an increased demand for sustainable pandemic governance practices. This study explored emerging hybrid governance practices that provide robust evidence on how to address the complex challenges of pandemics effectively. South Korea, which was severely affected by COVID-19, has implemented a novel governance approach using a whole-of-nation (WoN) model. This hybrid pandemic governance approach, which integrates both whole-of-government and whole-of-society approaches, has enabled synergistic and cohesive multi-sectoral coordination among all stakeholders (public authorities, private enterprises, and civil society) to address multifaceted challenges collectively and strengthen their resilience capacity. This study investigated South Korea’s WoN practices and their embedded context and identified key governance enablers facilitating this approach.MethodsThis study employed a case study design based on an extensive analysis of policy and program documents, drawing on South Korea’s publicly available data from January 1, 2020 to March 30, 2023. It assessed six system-level collaborative pandemic governance practices and key enablers, all of which were intended to fortify the country’s pandemic response.ResultsThe primary areas of the country’s WoN governance practices for COVID-19 control were (i) whole-of-government policy-making and response, (ii) COVID-19 testing system, (iii) digital surveillance of COVID-19, (iv) COVID-19 triage and treatment system, (v) domestic vaccine production, and (vi) community engagement. Key governance enablers for implementing the WoN model were establishing a legal foundation, ensuring decisive and strong governance and leadership, increasing public investment, applying a whole-of-health approach with augmented investment in public health, enhancing crisis communication, and mobilizing local leaders and civil society organizations in the national public health response.ConclusionsIn exploring innovative approaches to pandemic governance for increased efficacy, responsiveness, and impact, the WoN approach emerged as highly relevant. This example of emerging practice allows policy-makers to re-evaluate their governance strategies and initiatives to improve multi-agency partnerships across the country in their pandemic-preparedness planning.

Sentinel surveillance of COVID-19: the importance of epidemiologic concepts and reasoning.

Sentinel surveillance of COVID-19: the importance of epidemiologic concepts and reasoning.

Normalization of viral loads in Wastewater-Based Epidemiology using routine parameters: One year monitoring of SARS-CoV-2 in urban and tourist sewersheds

In wastewater-based epidemiology, normalization of experimental data is a crucial aspect, as emerged in the recent surveillance of COVID-19. Normalization facilitates the comparison between different areas or periods, and it helps in evaluating the differences due to the fluctuation of the population due to seasonal employment or tourism. Analysis of biomarkers in wastewater (i.e. drugs, beverage and food compounds, microorganisms such as PMMoV or crAssphage, etc.) is complex to perform, and it is not routinely monitored. This study compares the results of alternative normalization approaches applied to SARS-CoV-2 loads in wastewater using population size calculated with conventional hydraulic and/or chemical parameters (i.e. total suspended solids, chemical oxygen demand, nitrogen forms, etc.) commonly used in the routine monitoring of water quality. A total of 12 wastewater treatment plants were monitored, and 1068 samples of influent wastewater were collected in urban areas and in highly touristic areas (summer and/or winter). The results indicated that both census and population estimated with ammonium are effective and reliable parameters with which to normalize SARS-CoV-2 loads in wastewater from urban sewersheds with negligible fluctuating populations. However, this study reveals that, in the case of tourist locations, the population calculated using NH4-N loads can provide a better normalization of the specific viral load per inhabitant.

A comprehensive survey on exploring and analyzing COVID-19 mobile apps: Meta and exploratory analysis

During the current COVID-19 pandemic, many digital solutions around the world have been proposed to cope with the deadly virus but the role of mobile-based applications is dominant one. In Pakistan, during the current COVID-19 pandemic, an array of mobile health applications (apps) and platforms have been launched to grapple with the impacts of the COVID-19 situation. In this survey, our major focus is to explore and analyze the starring role of mobile apps based on the features and functionalities to tackle the COVID-19 disease, particularly in Pakistan. In this study, over fifty (50) mobile apps have been scrapped from the well-known three different sources i.e. Google Play Store, iOS Play Store, and web source. We developed our own data set after searching through the different play stores. We have designed two criteria such that the first criteria are known as eligibility criteria, while the second one is known as assessment criteria. The features and functions of each mobile app are pinpointed and discussed against the parameters of the assessment criteria. The major parameters of assessment criteria are: (i) Home monitoring; (ii) COVID-19 awareness; (iii) contact tracing; (iv) telemedicine; (v) health education; (vi) COVID-19 surveillance; (vii) self-assessment; (viii) security; and (ix) accessibility. This study conducted exploratory analysis and quantitative meta-data analysis by adopting PRISMA guidelines. This survey article is not only discussing the function and features of each COVID-19-centered app in Pakistan, but it also sheds light on the limitations of every mobile app as well. The results of this survey might be helpful for the mobile developers to review the current app products and enhance the existing mobile platforms targeted towards the COVID-19 pandemic. This is the first attempt of its kind to present a state-of-the-art survey of the COVID-19-centered mobile health apps in Pakistan.