Reported Coronavirus Disease 2019 Cases Research Articles

Demographic and epidemiological characteristics of COVID-19 epidemic in Albania.

This study provides a general overview of the coronavirus disease 2019 (COVID-19) pandemic in Albania. The aim was to assess the spread of COVID-19 in the country by describing the demographic and epidemiological characteristics. We performed a 12-month national observational, epidemiological study. The data were extracted from the national digital system of Infectious Diseases Information and included all reported COVID-19 cases. Demographic data, number of tests, number of confirmed positive cases, and number of deaths were analyzed to build a general overview of the pandemic in Albania. The database comprised 251,139 tested individuals and 250,926 were reported as confirmed cases. The average age of the tested cases was 44.83 years; 19.8% of the studied individuals were 51-60 years old and this group had the highest proportion (51.7%) of females. The estimated prevalence of COVID-19 in Albania was 51.31% (110,397 cases). Tirana, the capital of Albania, had the highest percentage of COVID-19 positive cases (44.9%) with an incidence rate 3,879.02 individuals/100,000 inhabitants. The COVID-19 curve reached its peak in February 2021 with 54,046 cases. The mortality rate was 1.18/1000 inhabitants. This demographic and epidemiological analysis provides a better understanding of the COVID-19 pandemic, and. the findings can contribute towards perception and re-investigation of the spread of COVID-19 in Albania, However, further studies covering longer periods and deeper analysis are needed for developing an epidemiological mathematical model, as a tool to develop and implement specific strategies for monitoring future pandemics.

Longitudinal wastewater-based surveillance of SARS-CoV-2 during 2023 in Ethiopia.

Although wastewater-based epidemiology (WBE) successfully functioned as a tool for monitoring the coronavirus disease 2019 (COVID-19) pandemic globally, relatively little is known about its utility in low-income countries. This study aimed to quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater, estimate the number of infected individuals in the catchment areas, and correlate the results with the clinically reported COVID-19 cases in Addis Ababa, Ethiopia. A total of 323 influent and 33 effluent wastewater samples were collected from three Wastewater Treatment Plants (WWTPs) using a 24-h composite Moore swab sampling method from February to November 2023. The virus was captured using Ceres Nanotrap® Enhancement Reagent 2 and Nanotrap® Microbiome A Particles, and then nucleic acids were extracted using the Qiagen QIAamp Viral RNA Mini Kit. The ThermoFisher TaqPath™ COVID-19 kit was applied to perform real-time reverse transcriptase polymerase chain reaction (qRT-PCR) to quantify the SARS-CoV-2 RNA. Wastewater viral concentrations were normalized using flow rate and number of people served. In the sampling period, spearman correlation was used to compare the SARS-CoV-2 target gene concentration to the reported COVID-19 cases. The numbers of infected individuals under each treatment plant were calculated considering the target genes' concentration, the flow rate of treatment plants, a gram of feces per person-day, and RNA copies per gram of feces. SARS-CoV-2 was detected in 94% of untreated wastewater samples. All effluent wastewater samples (n = 22) from the upflow anaerobic sludge blanket (UASB) reactor and membrane bioreactor (MBR) technology were SARS-COV-2 RNA negative. In contrast, two out of 11 effluents from Waste Stabilization Pond were found positive. Positive correlations were observed between the weekly average SARS-CoV-2 concentration and the cumulative weekly reported COVID-19 cases in Addis Ababa. The estimated number of infected people in the Kality Treatment catchment area was 330 times the number of COVID-19 cases reported during the study period in Addis Ababa. This study revealed that SARS-CoV-2 was circulating in the community and confirmed previous reports of more asymptomatic COVID-19 cases in Ethiopia. Additionally, this study provides further evidence of the importance of wastewater-based surveillance in general to monitor infectious diseases in low-income settings. Wastewater-based surveillance of SARS-CoV-2 can be a useful method for tracking the increment of COVID-19 cases before it spreads widely throughout the community.

Wastewater monitoring of SARS-CoV-2 gene for COVID-19 epidemiological surveillance in Tucumán, Argentina.

Wastewater-based epidemiology provides temporal and spatial information about the health status of a population. The objective of this study was to analyze and report the epidemiological dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the province of Tucumán, Argentina during the second and third waves of coronavirus disease 2019 (COVID-19) between April 2021 and March 2022. The study aimed to quantify SARS-CoV-2 RNA in wastewater, correlating it with clinically reported COVID-19 cases. Wastewater samples (n = 72) were collected from 16 sampling points located in three cities of Tucumán (San Miguel de Tucumán, Yerba Buena y Banda del Río Salí). Detection of viral nucleocapsid markers (N1 gene) was carried out using one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Viral loads were determined for each positive sample using a standard curve. A positive correlation (p < 0.05) was observed between viral load (copies/mL) and the clinically confirmed COVID-19 cases reported at specific sampling points in San Miguel de Tucumán (SP4, SP7, and SP8) in both months, May and June. Indeed, the high viral load concurred with the peaks of COVID-19 cases. This method allowed us to follow the behavior of SARS-CoV-2 infection during epidemic outbreaks. Thus, wastewater monitoring is a valuable epidemiological indicator that enables the anticipation of increases in COVID-19 cases and tracking the progress of the pandemic. SARS-CoV-2 genome-based surveillance should be implemented as a routine practice to prepare for any future surge in infections.

COVID-19 epidemiology, health services utilisation and health care seeking behaviour during the first year of the COVID-19 pandemic in Mweso health zone, Democratic Republic of Congo.

Although the evidence about coronavirus disease 2019 (COVID-19) has increased exponentially since the beginning of the pandemic, less is known about the direct and indirect effects of the pandemic in humanitarian settings. In the Democratic Republic of the Congo (DRC), most studies occurred in Kinshasa and other cities. Limited research was conducted in remote conflict-affected settings. We investigated the COVID-19 epidemiology, health service utilisation, and health care-seeking behaviour during the first year of the pandemic (March 2020-March 2021) in the Mweso health zone, North Kivu, DRC. This mixed-methods study includes a descriptive epidemiological analysis of reported COVID-19 cases data extracted from the provincial line list, interrupted time series analysis of health service utilisation using routine health service data, qualitative perceptions of health care workers about how health services were affected, and community members' health care seeking behaviour from a representative household survey and focus group discussions. The COVID-19 epidemiology in North Kivu aligns with evidence reported globally, yet case fatality rates were high due to underreporting. Testing capacity was limited and initially mainly available in the province's capital. Health service utilisation showed different patterns - child measles vaccinations experienced a decrease at the beginning of the pandemic, while outpatient consultations, malaria, and pneumonia showed an increase over time. Such increases might have been driven by insecurity and population displacements rather than COVID-19. Community members continued seeking care during the first months of the COVID-19 pandemic and visited the same health facilities as before COVID-19. Financial constraints, not COVID-19, were the main barrier reported to accessing health care. The first year of the COVID-19 pandemic in the Mweso health zone was characterised by low testing capacity and an underestimation of reported COVID-19 infections. The increase in health care utilisation should be further explored to understand the role of factors unrelated to COVID-19, such as insecurity, population displacement, and poverty, which remain major challenges to successfully providing health services and improving the population's health. Measles vaccination coverage dropped, which exacerbated the ongoing measles outbreak. Improved decentralised testing capacity will be crucial for future epidemics and enhanced efforts to maintain child vaccination coverage.

COVID-19 outcomes in patients with multiple sclerosis: Understanding changes from 2020 to 2022

Background: Epidemiologic studies on coronavirus disease 2019 (COVID-19) in patients with multiple sclerosis (pwMS) have focused on the first waves of the pandemic until early 2021. Objectives: We aimed to extend these data from the onset of the pandemic to the global coverage by vaccination in summer 2022. Methods: This retrospective, multicenter observational study analyzed COVISEP registry data on reported COVID-19 cases in pwMS between January 2020 and July 2022. Severe COVID-19 was defined as hospitalization or higher severity. Results: Among 2584 pwMS with confirmed/highly suspected COVID-19, severe infection rates declined from 14.6% preomicron wave to 5.7% during omicron wave (p < 0.001). Multivariate analysis identified age (odds ratio (OR) = 1.43, 95% confidence interval (CI) = [1.25–1.64] per 10 years), male sex (OR = 2.01, 95% CI = [1.51–2.67]), obesity (OR = 2.36, 95% CI = [1.52–3.68]), cardiac comorbidities (OR = 2.36, 95% CI = [1.46–3.83]), higher Expanded Disability Status Scale (EDSS) scores (OR = 2.09, 95% CI = [1.43–3.06] for EDSS 3–5.5 and OR = 4.53, 95% CI = [3.04–6.75] for EDSS ⩾6), and anti-CD20 therapies (OR = 2.67, 95% CI = [1.85–3.87]) as risk factors for COVID-19 severity. Vaccinated individuals experienced less severe COVID-19, whether on (risk ratio (RR) = 0.64, 95% CI = [0.60–0.69]) or off (RR = 0.32, 95% CI = [0.30–0.33]) anti-CD20. Discussion: In pwMS, consistent risk factors were anti-CD20 therapies and neurological disability, emerging as vital drivers of COVID-19 severity regardless of wave, period, or vaccination status.

COVID-19 vaccination and transmission patterns among pregnant and postnatal women during the fifth wave of COVID-19 in a tertiary hospital in Hong Kong.

Vaccination is a key strategy to control the coronavirus disease 2019 (COVID-19) pandemic. Safety concerns strongly influence vaccine hesitancy. Disease transmission during pregnancy could exacerbate risks of preterm birth and perinatal mortality. This study examined patterns of vaccination and transmission among pregnant and postnatal women during the fifth wave of COVID-19 in Hong Kong. The Antenatal Record System and Clinical Management System of the Hospital Authority was used to retrieve information concerning the demographic characteristics, vaccination history, COVID-19 status, and obstetric outcomes of women who were booked for delivery at Queen Mary Hospital in Hong Kong and had attended the booking antenatal visit from 1 July 2021 to 30 June 2022. Among 2396 women in the cohort, 2006 (83.7%), 1843 (76.9%), and 831 (34.7%) had received the first, second, and third doses of COVID-19 vaccine, respectively. Among 1012 women who had received the second dose, 684 (67.6%) women were overdue for their third dose. There were 265 (11.1%) reported COVID-19 cases. Women aged 20 to 29 years had a low vaccination rate but the highest disease rate (19.1%). The disease rate was more than tenfold higher in women who had no (20.3%) or incomplete (18.8%) vaccination, compared with women who had complete vaccination (2.1%; P<0.001). Acceptance of COVID-19 vaccination was low in pregnant women. Urgent measures are needed to promote vaccination among pregnant women before the next wave of COVID-19.

Quantitative analysis of SARS-CoV-2 RNA in wastewater and evaluation of sampling frequency during the downward period of a COVID-19 wave in Japan

Wastewater-based epidemiology (WBE) is a practical approach for detecting the presence of SARS-CoV-2 infections and assessing the epidemic trend of the coronavirus disease 2019 (COVID-19). The purpose of this study was to evaluate the minimum sampling frequency required to properly identify the COVID-19 trend during the downward epidemic period when using a highly sensitive RNA detection method. WBE was conducted using the Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids (EPISENS-S), a highly sensitive SARS-CoV-2 RNA detection method, at nine neighboring wastewater treatment plants (WWTPs). These WWTPs were in the same prefecture in Japan, and they had different sewer types, sampling methods, and sampling frequencies. The overall detection rate of SARS-CoV-2 RNA was 97.8 % during the entire study period when the geometric means of new COVID-19 cases per 100,000 inhabitants were between 3.3 and 7.7 in each WWTP. The maximum SARS-CoV-2 RNA concentration in wastewater was 2.14 × 104 copies/L, which corresponded to pepper mild mottle virus (PMMoV)–normalized concentrations of 6.54 × 10−3. We evaluated the effect of sampling frequencies on the probability of a significant correlation with the number of newly reported COVID-19 cases by hypothetically reducing the sampling frequency in the same dataset. When the wastewater sampling frequency occurred 5, 3, 2, and 1 times per week, these results exhibited significant correlations of 100 % (5/5), 89 % (8/9), 85 % (23/27), and 48 % (13/27), respectively. To achieve significant correlation with a high probability of over 85 %, a minimum sampling frequency of twice per week is required, even if sampling methods and sewer types are different. WBE using the EPISENS-S method and a sampling frequency of more than twice a week can be used to properly monitor COVID-19 wave epidemic trends, even during downward periods.

A Saliva-Based Serological and Behavioral Analysis of SARS-CoV-2 Antibody Prevalence in Howard County, Maryland.

The objective of the study was to estimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in the Howard County, Maryland, general population and demographic subpopulations attributable to natural infection or coronavirus disease 2019 (COVID-19) vaccination and to identify self-reported social behaviors that may affect the likelihood of recent or past SARS-CoV-2 infection. A cross-sectional, saliva-based serological study of 2,880 residents of Howard County, Maryland, was carried out from July through September 2021. Natural SARS-CoV-2 infection prevalence was estimated by inferring infections among individuals according to anti-nucleocapsid immunoglobin G levels and calculating averages weighted by sample proportions of various demographics. Antibody levels between BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) recipients were compared. Antibody decay rate was calculated by fitting exponential decay curves to cross-sectional indirect immunoassay data. Regression analysis was carried out to identify demographic factors, social behaviors, and attitudes that may be linked to an increased likelihood of natural infection. The estimated overall prevalence of natural infection in Howard County, Maryland, was 11.9% (95% confidence interval, 9.2% to 15.1%), compared with 7% reported COVID-19 cases. Antibody prevalence indicating natural infection was highest among Hispanic and non-Hispanic Black participants and lowest among non-Hispanic White and non-Hispanic Asian participants. Participants from census tracts with lower average household income also had higher natural infection rates. After accounting for multiple comparisons and correlations between participants, none of the behavior or attitude factors had significant effects on natural infection. At the same time, recipients of the mRNA-1273 vaccine had higher antibody levels than those of BNT162b2 vaccine recipients. Older study participants had overall lower antibody levels compared with younger study participants. The true prevalence of SARS-CoV-2 infection is higher than the number of reported COVID-19 cases in Howard County, Maryland. A disproportionate impact of infection-induced SARS-CoV-2 positivity was observed across different ethnic/racial subpopulations and incomes, and differences in antibody levels across different demographics were identified. Taken together, this information may inform public health policy to protect vulnerable populations. IMPORTANCE We employed a highly innovative noninvasive multiplex oral fluid SARS-CoV-2 IgG assay to ascertain our seroprevalence estimates. This laboratory-developed test has been applied in NCI's SeroNet consortium, possesses high sensitivity and specificity according to FDA Emergency Use Authorization guidelines, correlates strongly with SARS-CoV-2 neutralizing antibody responses, and is Clinical Laboratory Improvement Amendments-approved by the Johns Hopkins Hospital Department of Pathology. It represents a broadly scalable public health tool to improve understanding of recent and past SARS-CoV-2 exposure and infection without drawing any blood. To our knowledge, this is the first application of a high-performance salivary SARS-CoV-2 IgG assay to estimate population-level seroprevalence, including identifying COVID-19 disparities. We also are the first to report differences in SARS-CoV-2 IgG responses by COVID-19 vaccine manufacturers (BNT162b2 [Pfizer-BioNTech] and mRNA-1273 [Moderna]). Our findings demonstrate remarkable consistency with those of blood-based SARS-CoV-2 IgG assays in terms of differences in the magnitude of SARS-CoV-2 IgG responses between COVID-19 vaccines.

Detection of SARS-CoV-2 RNA in wastewater and comparison to COVID-19 cases in two sewersheds, North Carolina, USA

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be useful for monitoring population-wide coronavirus disease 2019 (COVID-19) infections, especially given asymptomatic infections and limitations in diagnostic testing. We aimed to detect SARS-CoV-2 RNA in wastewater and compare viral concentrations to COVID-19 case numbers in the respective counties and sewersheds. Influent 24-hour composite wastewater samples were collected from July to December 2020 from two municipal wastewater treatment plants serving different population sizes in Orange and Chatham Counties in North Carolina. After a concentration step via HA filtration, SARS-CoV-2 RNA was detected and quantified by reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) and quantitative PCR (RT-qPCR), targeting the N1 and N2 nucleocapsid genes. SARS-CoV-2 RNA was detected by RT-ddPCR in 100 % (24/24) and 79 % (19/24) of influent wastewater samples from the larger and smaller plants, respectively. In comparison, viral RNA was detected by RT-qPCR in 41.7 % (10/24) and 8.3 % (2/24) of samples from the larger and smaller plants, respectively. Positivity rates and method agreement further increased for the RT-qPCR assay when samples with positive signals below the limit of detection were counted as positive. The wastewater data from the larger plant generally correlated (⍴ ~0.5, p < 0.05) with, and even anticipated, the trends in reported COVID-19 cases, with a notable spike in measured viral RNA preceding a spike in cases when students returned to a college campus in the Orange County sewershed. Correlations were generally higher when using estimates of sewershed-level case data rather than county-level data. This work supports use of wastewater surveillance for tracking COVID-19 disease trends, especially in identifying spikes in cases. Wastewater-based epidemiology can be a valuable resource for tracking disease trends, allocating resources, and evaluating policy in the fight against current and future pandemics.

Leveraging Serosurveillance and Postmortem Surveillance to Quantify the Impact of Coronavirus Disease 2019 in Africa.

The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on global health, the magnitude of which appears to differ intercontinentally: For example, reports suggest that 271 900 per million people have been infected in Europe versus 8800 per million people in Africa. While Africa is the second-largest continent by population, its reported COVID-19 cases comprise <3% of global cases. Although social and environmental explanations have been proposed to clarify this discrepancy, systematic underascertainment of infections may be equally responsible. We sought to quantify magnitudes of underascertainment in COVID-19's cumulative incidence in Africa. Using serosurveillance and postmortem surveillance, we constructed multiplicative factors estimating ratios of true infections to reported cases in Africa since March 2020. Multiplicative factors derived from serology data (subset of 12 nations) suggested a range of COVID-19 reporting rates, from 1 in 2 infections reported in Cape Verde (July 2020) to 1 in 3795 infections reported in Malawi (June 2020). A similar set of multiplicative factors for all nations derived from postmortem data points toward the same conclusion: Reported COVID-19 cases are unrepresentative of true infections, suggesting that a key reason for low case burden in many African nations is significant underdetection and underreporting. While estimating the exact burden of COVID-19 is challenging, the multiplicative factors we present furnish incidence estimates reflecting likely-to-worst-case ranges of infection. Our results stress the need for expansive surveillance to allocate resources in areas experiencing discrepancies between reported cases, projected infections, and deaths.

The Impact of Public Health Intervention Policies On Locals’ Mobility Patterns During the First Half of 2020

Objective: The prevalence of coronavirus disease 2019 (COVID-19) can cause inconvenience and affect lifestyle because human movements can spread virus transmission. This study aims to investigate the impact of the government’s public health intervention policies and reported COVID-19 cases on locals’ mobility patterns. Design: Secondary data on various mobility patterns of Hong Kong people against public health intervention policies and reported COVID-19 cases were collected and analyzed from publicly available sources, including government, commercial, and news sites. Data were collected from January to July 2020. Multiple regression was applied for hypothesis testing. Results: Results showed positive and negative impacts of public health intervention policies and reported COVID-19 cases on locals’ mobility patterns. The policy of wearing facial masks negatively influenced locals’ mobility patterns. Then, the policy of closure of leisure and cultural service venues increases locals’ mobility for retail, groceries, and transit. Moreover, the policy limiting social gatherings to 50 people enhanced locals’ mobilities for retail and transit. From another aspect, the reported COVID-19 cases had a negative impact on locals’ mobility for retail, parks, and transit. Conclusions: This study presents considerable effects of public health intervention policies. With the restrictions on certain activities or behaviors, locals will transfer to another behavior, which consequently enhances travel mobilities. The reported COVID-19 cases significantly reduced local mobility patterns.

A simulation of geographic distribution for the emergence of consequential SARS-CoV-2 variant lineages

The coronavirus disease 2019 (COVID-19) pandemic has been facilitated by the intermittent emergence of consequential variant strains. This study evaluated the geographic disproportionality in the detection of consequential variant lineages across countries. As of November 2021, a total of 40 potentially consequential SARS-CoV-2 variant lineages have been identified. One-hundred repeated simulations that randomly produced consequential variants from overall COVID-19 cases worldwide were performed to evaluate the presence of geographical disproportion in the occurrence of consequential variant outbreaks. Both the total number of reported COVID-19 cases and the number of reported genome sequences in each country showed weak positive correlations with the number of detected consequential lineages in each country. The simulations suggest the presence of geographical disproportion in the occurrence of consequential variant outbreaks. Based on the random occurrence of consequential variants among COVID-19 cases, identified consequential variants occurred more often than expected in the United Kingdom and Africa, whereas they occurred less in other European countries and the Middle East. Simulations of the occurrence of consequential variants by assuming a random occurrence among all COVID-19 cases suggested the presence of biogeographic disproportion. Further studies enrolling unevaluated crucial biogeographical factors are needed to determine the factors underlying the suggested disproportionality.

Mortality Risk Factors Among Critically Ill Children With Acute COVID-19 in PICUs: A Multicenter Study From Turkish Pediatric Critical COVID-19 and MIS-C Study Group.

Background:During the coronavirus disease 2019 (COVID-19) pandemic, the world has a large number of reported COVID-19 cases and deaths. Information on characteristics and mortality rate of pediatric intensive care unit (PICU) cases with COVID-19 remains limited. This study aims to identify the risk factors for mortality related to COVID-19 in children admitted to PICU.Methods:A retrospective multicenter cohort study was conducted between March 2020 and April 2021 at 44 PICUs in Turkey. Children who were 1 month–18-year of age with confirmed COVID-19 admitted to PICU were included in the study. Children with multisystem inflammatory syndrome and asymptomatic for COVID-19 were excluded.Results:Of 335 patients with COVID-19, the median age was 6.8 years (IQR: 1.2–14) and 180 (53.7 %) were male, 215 (64.2 %) had at least one comorbidity. Age and gender were not related to mortality. Among 335 patients, 166 (49.5%) received mechanical ventilation, 17 (5.1%) received renal replacement therapy and 44 (13.1 %) died. Children with medical complexity, congenital heart disease, immunosuppression and malignancy had significantly higher mortality. On multivariable logistic regression analysis, organ failure index [odds ratio (OR): 2.1, 95 confidence interval (CI): 1.55–2.85], and having congenital heart disease (OR: 2.65, 95 CI: 1.03–6.80), were associated with mortality.Conclusions:This study presents detailed data on clinical characteristics and outcomes of patients with COVID-19 admitted to PICU in the first pandemic year in Turkey. Our study shows that having congenital heart disease is associated with mortality. In addition, the high organ failure score in follow-up predict mortality.

Modeling the relationship between SARS-CoV-2 RNA in wastewater or sludge and COVID-19 cases in three New England regions.

We aimed to compare statistical techniques estimating the association between SARS-CoV-2 RNA in untreated wastewater and sludge and reported coronavirus disease 2019 (COVID-19) cases. SARS-CoV-2 RNA concentrations (copies/mL) were measured from 24-h composite samples of wastewater in Massachusetts (MA) (daily; 8/19/2020-1/19/2021) and Maine (ME) (weekly; 9/1/2020-3/2/2021) and sludge samples in Connecticut (CT) (daily; 3/1/2020-6/1/2020). We fit linear, generalized additive with a cubic regression spline (GAM), Poisson, and negative binomial models to estimate the association between SARS-CoV-2 RNA concentration and reported COVID-19 cases. The models that fit the data best were linear [adjusted R2=0.85 (MA), 0.16 (CT), 0.63 (ME); root-mean-square error (RMSE)=0.41 (MA), 1.14 (CT), 0.99 (ME)), GAM (adjusted R2=0.86 (MA), 0.16 (CT) 0.65 (ME); RMSE=0.39 (MA), 1.14 (CT), 0.97 (ME)], and Poisson [pseudo R2=0.84 (MA), 0.21 (CT), 0.52 (ME); RMSE=0.39 (MA), 0.67 (CT), 0.79 (ME)]. Linear, GAM, and Poisson models outperformed negative binomial models when relating SARS-CoV-2 RNA in wastewater or sludge to reported COVID-19 cases.

COVID-19 contact tracing in Belgium: main indicators and performance, January \u2013 September 2021

BackgroundContact tracing is one of the main public health tools in the control of coronavirus disease 2019 (COVID-19). A centralized contact tracing system was developed in Belgium in 2020. We aim to evaluate the performance and describe the results, between January 01, 2021, and September 30, 2021. The characteristics of COVID-19 cases and the impact of COVID-19 vaccination on testing and tracing are also described.MethodsWe combined laboratory diagnostic test data (molecular and antigen test), vaccination data, and contact tracing data. A descriptive analysis was done to evaluate the performance of contact tracing and describe insights into the epidemiology of COVID-19 by contact tracing.ResultsBetween January and September 2021, 555.181 COVID-19 cases were reported to the central contact center and 91% were contacted. The average delay between symptom onset and contact tracing initiation was around 5 days, of which 4 days corresponded to pre-testing delay. High-Risk Contacts (HRC) were reported by 49% of the contacted index cases. The mean number of reported HRC was 2.7. In total, 666.869 HRC were reported of which 91% were successfully contacted and 89% of these were tested at least once following the interview. The estimated average secondary attack rate (SAR) among the contacts of the COVID-19 cases who reported at least one contact, was 27% and was significantly higher among household HRC. The proportion of COVID-19 cases who were previously identified as HRC within the central system was 24%.ConclusionsThe contact-tracing system contacted more than 90% of the reported COVID-19 cases and their HRC. This proportion remained stable between January 1 2021 and September 30 2021 despite an increase in cases in March–April 2021. We report high SAR, indicating that through contact tracing a large number of infections were prospectively detected.The system can be further improved by (1) reducing the delay between onset of illness and medical consultation (2) having more exhaustive reporting of HRC by the COVID-19 case.

Misperceptions about the Impact of Lockdown on the Number of Newly Reported COVID-19 Cases

Thailand, along with many other countries, was hit by coronavirus disease 2019 (COVID-19). The COVID-19 vaccines were known to be effective in mitigating the spread and preventing deaths. However, Thailand faced a crisis in mid-2021 before the vaccines could disseminated to the population. Thus, the Government introduced a lockdown policy to control the outbreak. However, many questioned the effectiveness of the policy as it did not immediately result in favorable outcomes. Therefore, this study aimed to unravel results of the lockdown using deterministic system dynamics and compartmental models. We found that there was a misperception surrounding the idea that the lockdown policy could reduce the number of newly reported cases within few days. In addition, the epidemic would always continue as long as there were susceptible people remaining in the system. Therefore, the Government needs to consider other supporting policies alongside the lockdown and communicate with the wider public about its objectives.

A descriptive study of a SARS-CoV-2 super spreader event: A funeral and a house-party in a district municipality of the Eastern Cape Province, South Africa, 2020

Background: Coronavirus Disease 2019 (COVID-19) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). According to the World Health Organisation, modes of transmission for SARS-CoV-2 include contact, droplet and airborne transmission has also been reported. On 16 March 2020, Nelson Mandela Bay District in the Eastern Cape Province recorded its first case of SARS-CoV-2, which was travel related. On 21 March 2020, over 1000 people attended a funeral of a healthcare worker in one of the townships within the district. This study aims to quantify the incident infections associated with a single funeral and describe the chain of transmission. Methods: This is a retrospective descriptive study that uses COVID-19 data collected between March and April 2020 in Nelson Mandela Bay district. Interviews were conducted with newly reported COVID-19 cases by healthcare professionals through telephonic platforms and face-to-face. Data obtained through the interviews included possible exposures, risk factors, and demographics. Data analysis was done using Microsoft Excel. Results: Four-hundred-and-twelve cases of COVID-19 were reported within the district during the study period. Of the first 100 cases reported within the district, 42% (42/100) were linked to the funeral; these included people who attended the funeral and those who had contact with the funeral attendees. Conclusion: While funerals cannot be avoided, adherence to COVID-19 guidelines on funerals can help reduce infection transmission rates. Platforms such as virtual funeral services and virtual memorial services can help reduce the risk of COVID-19 transmission associated with these types of events.

Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence and Reported Coronavirus Disease 2019 Cases in US Children, August 2020-May 2021.

BackgroundCase-based surveillance of pediatric coronavirus disease 2019 (COVID-19) cases underestimates the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections among children and adolescents. Our objectives were to estimate monthly SARS-CoV-2 antibody seroprevalence and calculate ratios of SARS-CoV-2 infections to reported COVID-19 cases among children and adolescents in 8 US states.MethodsUsing data from the Nationwide Commercial Laboratory Seroprevalence Survey, we estimated monthly SARS-CoV-2 antibody seroprevalence among children aged 0–17 years from August 2020 through May 2021. We calculated and compared cumulative incidence of SARS-CoV-2 infection extrapolated from population-standardized seroprevalence of antibodies to SARS-CoV-2, cumulative COVID-19 case reports since March 2020, and infection-to-case ratios among persons of all ages and children aged 0–17 years for each state.ResultsOf 41 583 residual serum specimens tested, children aged 0–4, 5–11, and 12–17 years accounted for 1619 (3.9%), 10 507 (25.3%), and 29 457 (70.8%), respectively. Median SARS-CoV-2 antibody seroprevalence among children increased from 8% (range, 6%–20%) in August 2020 to 37% (range, 26%–44%) in May 2021. Estimated ratios of SARS-CoV-2 infections to reported COVID-19 cases in May 2021 ranged by state from 4.7–8.9 among children and adolescents to 2.2–3.9 for all ages combined.ConclusionsThrough May 2021 in selected states, the majority of children with serum specimens included in serosurveys did not have evidence of prior SARS-CoV-2 infection. Case-based surveillance underestimated the number of children infected with SARS-CoV-2 more than among all ages. Continued monitoring of pediatric SARS-CoV-2 antibody seroprevalence should inform prevention and vaccination strategies.

A Simple Method to Detect SARS-CoV-2 in Wastewater at Low Virus Concentration.

Background Since its initial appearance in December 2019, coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally. Wastewater surveillance has been demonstrated as capable of identifying infection clusters early. The purpose of this study was to investigate a quick and simple method to detect SARS-CoV-2 in wastewater in Thailand during the early stages of the second outbreak wave when the prevalence of the disease and the virus concentration in wastewater were low. Methods Wastewater samples were collected from a hospital caring for patients with COVID-19 and from 35 markets, two of which were associated with recently reported COVID-19 cases. Then, samples were concentrated by membrane filtering prior to SARS-CoV-2 detection by RT-qPCR. Results SARS-CoV-2 RNA was detected in the wastewater samples from the hospital; the Ct values for the N, ORF1ab, and S genes progressively increased as the number of patients admitted to the treatment floor decreased. Notably, the ORF1ab and S genes were still detectable in wastewater even when only one patient with COVID-19 remained at the hospital. SARS-CoV-2 RNA was detected in the wastewater samples from fresh market where COVID-19 cases were reported. Conclusions Our findings suggest that wastewater surveillance for SARS-CoV-2 is sensitive and can detect the virus even in places with a high ambient temperature and relatively low prevalence of COVID-19.

Tracking the temporal variation of COVID-19 surges through wastewater-based epidemiology during the peak of the pandemic: A six-month long study in Charlotte, North Carolina

The global spread of SARS-CoV-2 has continued to be a serious concern after WHO declared the virus to be the causative agent of the coronavirus disease 2019 (COVID-19) a global pandemic. Monitoring of wastewater is a useful tool for assessing community prevalence given that fecal shedding of SARS-CoV-2 occurs in high concentrations by infected individuals, regardless of whether they are asymptomatic or symptomatic. Using tools that are part of wastewater-based epidemiology (WBE) approach, combined with molecular analyses, wastewater monitoring becomes a key piece of information used to assess trends and quantify the scale and dynamics of COVID-19 infection in a specific community, municipality, or area of service. This study investigates a six-month long SARS-CoV-2 RNA quantification in influent wastewater from four municipal wastewater treatment plants (WWTP) serving the Charlotte region of North Carolina (NC) using both RT-qPCR and RT-ddPCR platforms. Influent wastewater was analyzed for the nucleocapsid (N) genes N1 and N2. Both RT-qPCR and RT-ddPCR performed well for detection and quantification of SARS-CoV-2 using the N1 target, while for the N2 target RT-ddPCR was more sensitive. SARS-CoV-2 concentration ranged from 103 to 105 copies/L for all four plants. Both RT-qPCR and RT-ddPCR showed a significant positive correlation between SARS-CoV-2 concentrations and the 7-day rolling average of clinically reported COVID-19 cases when lagging 5 to 12 days (ρ = 0.52–0.92, p < 0.001–0.02). A major finding of this study is that RT-qPCR and RT-ddPCR generated SARS-CoV-2 data that was positively correlated (ρ = 0.569, p < 0.0001) and can be successfully used to monitor SARS-CoV-2 signals across the WWTP of different sizes and metropolitan service functions without significant anomalies.