Number Of Daily Tests Research Articles

CoBUSters: organization and deployment of a mobile Covid-19 test team for large-scale testing in collectivities as a societal outreach from a university hospital.

During the summer of 2020, government agencies in Belgium were hasty to create additional test capacity for the diagnosis of acute infections with SARS-CoV-2. Large-scale initiatives were necessary to absorb the waves of covid-19 infections throughout all levels of society.
 Within the Antwerp University Hospital (UZA) we realized that it would be impossible to perform large-scale testing of schools, universities, companies, or other collectivities if sampling capacity would not be made available within the collectivities themselves. A large number of scholars or students would not be able to move toward test centers. So, we created a mobile testing team that on-call would be sent to the collectivities to perform testing within the schools, universities, and companies. The test team started in October 2020 with a single bus which was transformed to accommodate the administrative handling of the test e-form, the scanning of the eID of the person to be tested, as well as the sampling through a nasopharyngeal itself. The structure of the bus allowed to perform the testing of 12 students every 15 minutes. Within a couple of weeks, the demand for large-scale testing increased to more than 1000 a day. Towards the end of 2020, a second bus was put into operation. During the year 2021, additional vans were put into operation as the daily number of tests further increased.
 The mobile test team remained in operation until the end of May 2022. During this lifespan, more than 200,000 nasopharyngeal samples were collected by the test team. The highest number of tests on a single day was 4,200. The mobile test team operated within three Belgian provinces, serving more than 2,500,000 people. Besides the testing teams, we created a dispatching team which was evaluable 7/7 for calls and registration of demands for new testings. Besides schools, we performed collective testing in colleges, universities, nursing homes, large companies, prisons, and hospices for homeless people. We even performed large-scale tests within municipalities at the hights of the covid waves. TAT for reporting results was less than 24 hours after sampling.
 The mobile test team, CoBUSters, became known throughout the entire country as a truly innovative social project. Many wondered why a hospital developed a service that was well outside of the regular hospital-related activities. We however did this because this aligns with our mission as a community-based hospital with a strong societal-oriented focus. Hospitals should not remain within their walls, but when society needs them they should quickly develop activities to support society as a whole. This project allowed us also to interact on an almost daily basis with healthcare providers on the first line of healthcare (GPs, home care nurses). From the hospital, we provided the services that the first line could have never mobilized like administrative and nursing staff or manipulation of huge numbers of PCR tests. Together we were able to protect our collectivities against massive outbreaks of the SARS-CoV-2 virus. Even tertiary hospitals have a fundamental societal role when called upon. We should never forget this.

Health and Economic Impacts of Lockdown Policies in the Early Stage of COVID-19 in the United States

Lockdown policies, such as stay-at-home orders, are known to be effective in controlling the spread of the novel coronavirus disease 2019. However, concerns over economic burdens of these policies rapidly propelled U.S. states to move toward reopening in the early stage of the pandemic. Decision making in most states has been challenging, especially because of a dearth of quantitative evidence on health gains versus economic burdens of different policies. To assist decision makers, we study the health and economic impacts of various lockdown policies across U.S. states and shed light on policies that are most effective. To this end, we make use of detailed data from 50 U.S. states plus the District of Columbia on various factors, including number of tests, positive and negative results, hospitalizations, ICU beds and ventilators used, residents’ mobility obtained from cellphone data, and deaths. Our analyses allow quantifying the total cost versus the total quality-adjusted life year (QALY) associated with various lockdown policies. We utilize a compartmental model with Markov chain Monte Carlo simulation to estimate the spread of disease. To calibrate our model separately for each U.S. state, we make use of empirical data on the intensity of intervention policies, age, ratio of Black/Hispanic populations, per capita income, residents’ mobility, and number of daily tests and feed them to a longitudinal mixed-effect model. Finally, we utilize a microsimulation model to estimate the total cost and total QALY for each state and perform cost-effectiveness analysis to identify policies that would have worked best. Our results show that, compared with no intervention during March–June 2020, the policies undertaken across the United States saved, on average, about 41,284.51 years’ worth of QALY (per 100K capita), incurring $164.01 million (per 100K capita). Had the states undertaken more strict policies during the same time frame than those they adopted, these values would be 44,909.41 years and $117.28 million, respectively. By quantifying the impact of various lockdown policies separately for each state, our results allow federal and state authorities to avoid following a “one size fits all” strategy and instead enact policies that are better suited for each state. Specifically, by studying the trade-offs between health gains and economic impacts, we identify the particular states that would have benefited from implementing more restrictive policies. Finally, in addition to shedding light on the impact of lockdown policies during our study period (March–June 2020), our results have important implications on curbing future fast-spreading variants of the coronavirus or other related potential epidemics.Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0321 .

Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) will have greater value as an important diagnostic tool. An in-depth analysis and understanding of the metrics derived from WWS is required to interpret and utilize WWS-acquired data effectively (McClary-Gutierrez et al., 2021; O’Keeffe, 2021). In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven cities in Canada over periods ranging from 8 to 21 months. This work demonstrates that significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing (resulting in a reduction to testing access and a reduction in the number of daily tests) in these communities, despite increases in the wastewater signal. Furthermore, the WC ratio decreased significantly in 6 of the 7 studied locations, serving as a potential signal of the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community (40–60 % allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community (40–60 % allelic proportion). Finally, a significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant's greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when community immunity was high. The WC ratio, used as an additional monitoring metric, could complement clinical case counts and wastewater signals as individual metrics in its potential ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.

The Obstacles to Combat against Covid-19 Pandemic and the Remedies: Bangladesh Scenario.

BackgroundBangladesh has been going through the austerity of the unique COVID-19 for more than a year like several other nations in the world in spite of concerted efforts taken by the government along with other concerned authorities who have advocated compulsory guidelines involving social distancing procedures accompanied by lockdown to have control over the pandemic. In this paper, the barriers faced by the government to protect people from the COVID-19 pandemic have been investigated. Also, the number of daily infected people against the number of daily tests has been underlined to comprehend the overall pandemic picture in Bangladesh.Design and MethodsA descriptive study has been carried out to investigate the obstacles to tackle the COVID-19 pandemic for this country. The intensity of the outbreaks of the pandemic in this country is stated from March 8, 2020, to February 12, 2021. Secondary data have been employed from different sources to serve the goals of the study.ResultsThe poor management in the health sector of Bangladesh has been an issue of major concern during the early stage of COVID-19 which incorporates deficiency of medical equipment, lack of facilities for testing COVID-19, poor patient management, and uncertainty in the medication system. Finally, some recommendations have been proposed for the concerned organizations to tackle the current pandemic and as well in the future.ConclusionsTo control this COVID-19 pandemic, it is necessary to find the difficulties and discover the remedies which have been done in this paper for the Bangladesh perspective.

The obstacles to combat against COVID-19 pandemic and the remedies: Bangladesh scenario

Bangladesh has been going through the austerity of the unique COVID-19 for more than a year like several other nations in the world in spite of concerted efforts taken by the government along with other concerned authorities who have advocated compulsory guidelines involving social distancing procedures accompanied by lockdown to have control over the pandemic. In this paper, the barriers faced by the government to protect people from the COVID-19 pandemic have been investigated. Also, the number of daily infected people against the number of daily tests has been underlined to comprehend the overall pandemic picture in Bangladesh. A descriptive study has been carried out to investigate the obstacles to tackle the COVID-19 pandemic for this country. The intensity of the outbreaks of the pandemic in this country is stated from March 8, 2020, to February 12, 2021. Secondary data have been employed from different sources to serve the goals of the study. The poor management in the health sector of Bangladesh has been an issue of major concern during the early stage of COVID-19 which incorporates deficiency of medical equipment, lack of facilities for testing COVID-19, poor patient management, and uncertainty in the medication system. Finally, some recommendations have been proposed for the concerned organizations to tackle the current pandemic and as well in the future. To control this COVID-19 pandemic, it is necessary to find the difficulties and discover the remedies which have been done in this paper for the Bangladesh perspective.

Analysis of the prediction of the 2021 time-evolution of the Covid-19 pandemic in Italy using a Planck's distribution.

In a previous paper, we studied the time-evolution of the Covid-19 pandemic in Italy during the first wave of 2020 using a number of distribution laws. We concluded that the best distribution law to predict the evolution of the pandemic is a distribution of the type of Planck’s law with three parameters, provided that the basic conditions of the pandemic (such as social distancing, vaccination campaigns and new Covid variants) do not appreciably change the spread of the pandemic. In our 2020 study, we did not use the number of daily positive cases in Italy but the ratio of daily positive cases per number of daily tests, ratio today sometimes referred to as: “positivity rate.” We showed that if basic conditions do not change, the Planck’s distribution with three parameters provides very good predictions of the positivity rate about 1 month in advance. In a second paper, using the Planck’s distribution with three parameters, we predicted, about 1 month in advance, the spread of the pandemic in Italy during the Christmas 2020 holidays. Here we show that indeed in our second paper the spread of the pandemic in Italy, after 1 month, was well predicted using the Planck’s distribution with an error of a few percent only. We then studied the present (October 2021) evolution of the pandemic in Italy, and we showed that the Planck’s distribution, based on the data of July and August, predicted well the evolution of the pandemic. We then show that the peak of the positivity rate was approximately reached during the middle of August. However, the end of the Italian holidays and the start of all the activities including schools, intensive use of public transportation and further distancing measures may change again the trend of the positivity rate of the pandemic.

Positivity rate: an indicator for the spread of COVID-19

Objective This paper investigates the relationship between the positivity rate and numbers of deaths and intensive care unit (ICU) patients. In addition, it explores the use of the positivity rate as an indicator for the spread of COVID-19. Methods We used COVID-19 datasets for eight countries – Canada, USA, UK, Italy, Belgium, Ireland, Colombia and South Africa – and considered two correlation cases. The first case considers the correlation of the number of confirmed cases with each of deaths and ICU patients. The second case considers the correlation of the positivity rate with each of deaths and ICU patients. When obtaining the correlation, we considered different lagging periods between the date of confirming a case and the date of its ICU admittance or death. We compared the obtained correlation coefficient values for each of the two considered cases to explore whether the positivity rate is a better indicator for the spread of the disease than confirmed cases. For each of the eight considered countries, we obtained the daily reproduction number using each of confirmed cases and positivity rate. The two obtained sets of reproduction number values for each country were statistically compared to investigate whether they are significantly different. Results When considering the daily positivity rate instead of the daily number of confirmed cases, the maximum correlation with deaths is increased by 349.9% for the USA (the country with the highest increase) and 4.5% for the UK (the country with the lowest increase), with an average increase of 60.8% considering the eight countries. Considering the daily positivity rate instead of the daily number of confirmed cases caused the maximum correlation with the number of ICU patients to be increased by 74.7% for the USA (the country with the highest increase) and 2.2% for the UK (the country with the lowest increase), with an average increase of 25% over the considered countries. The results for the daily reproduction number obtained using the positivity rate are statistically different from those obtained using daily confirmed cases. Conclusion The results indicate that positivity rate is a better indicator for the spread of the disease than the number of confirmed cases. Therefore, it is highly advised to use measures based on the positivity rate when indicating the spread of the disease and considering responses accordingly because these measures consider the daily number of tests and confirmed cases.

Association between RT-PCR Ct values and COVID-19 new daily cases: a multicenter cross-sectional study.

Proactive prediction of the epidemiologic dynamics of viral diseases and outbreaks of the type of COVID-19 has remained a difficult pursuit for scientists, public health researchers, and policymakers. It is unclear whether RT-PCR Cycle Threshold (Ct) values of COVID-19 - or any other virus - as indicator of viral load, could represent a possible predictor for underlying epidemiologic changes on a population level. The study objective is thus to investigate whether population-wide changes in SARS-CoV-2 RT-PCR Ct values over time are associated with the daily fraction of positive COVID-19 tests. In addition, this study analyses the factors that could influence RT-PCR Ct values. A retrospective cross-sectional study was conducted on 63,879 patients from May 4, 2020 to September 30, 2020, in all COVID-19 facilities in the Kingdom of Bahrain. Data collected included number of tests and newly diagnosed cases, as well as Ct values, age, sex nationality, and symptomatic status. Ct values were found to be negatively and very weakly correlated with the fraction of daily positive tests in the population r = -0.06 (CI 95%: -0.06; -0.05; p=0.001). The R-squared for the regression model (adjusting for age and number of daily tests) showed an accuracy of 45.3%. Ct Values showed an association with nationality (p=0.012). After the stratification, the association between Ct values and the fraction of daily positive cases was only maintained for the female sex and Bahraini-nationality. Symptomatic presentation was significantly associated with lower Ct values (higher viral loads). Ct values do not show any correlation with age (p=0.333) or sex (p=0.522). We report one of the first and largest studies to investigate the epidemiologic associations of Ct values with COVID-19. Although changes in Ct values showed a moderate association with daily cases, our results indicate that it may not be as predictive within a simple model. More population studies and models from global cohorts are necessary.

Estimation and prediction of doubling time for COVID-19 epidemic in Bangladesh: a study of first 14 month’s daily confirmed new cases and deaths

Background: The doubling time is a reliable indicator to estimate the rate at which the pandemic is spreading. We evaluated and predicted the doubling time for the daily COVID-19 cases and deaths in Bangladesh.Methods: Publicly available daily data on COVID-19 new cases from 8 March, 2020 to 14 February, 2021 and the daily deaths data from 18 March, 2020 to 14 February, 2021 were used to predict doubling time based on records from seven days prior. Then, short-term predictions for the next 14 days (1 to 14 February, 2021) were performed to validate the accuracy of our prediction. Finally, using the doubling time data up to 14 February, 2021, a two months (15 February- 15 April, 2021) prediction was made for both daily new COVID-19 cases and deaths.Results: The median doubling time for daily new COVID-19 cases and deaths were 90.51 and 86.02 days respectively in the entire period. The doubling period for cases was lowest in the second to third week of March, 2020 [ranged 2.33-8.43 days] and longest in the second week of March, 2021 [ranged 834-2187 days]. Our prediction suggests that the doubling time for daily confirmed new COVID-19 case will be 1310.33 days [95% CI: 854.33 - 1766.32] and deaths will be 683.04 days [556.05 - 810.03] on 15 April, 2021 in Bangladesh.Conclusion: Our prediction is based on current testing strategies. Any changes in daily number of tests or sudden changes of the dynamics of COVID-19 transmission would affect these predictions.

Suppressing the impact of the COVID-19 pandemic using controlled testing and isolation

The Corona virus disease has significantly affected lives of people around the world. Existing quarantine policies led to large-scale lock-downs because of the slow tracking of the infection paths, and indeed we see new waves of the disease. This can be solved by contact tracing combined with efficient testing policies. Since the number of daily tests is limited, it is crucial to exploit them efficiently to improve the outcome of contact tracing (technological or human-based epidemiological investigations). We develop a controlled testing framework to achieve this goal. The key is to test individuals with high probability of being infected to identify them before symptoms appear. These probabilities are updated based on contact tracing and test results. We demonstrate that the proposed method could reduce the quarantine and morbidity rates compared to existing methods by up to a 50%. The results clearly demonstrate the necessity of accelerating the epidemiological investigations by using technological contact tracing. Furthermore, proper use of the testing capacity using the proposed controlled testing methodology leads to significantly improved results under both small and large testing capacities. We also show that for small new outbreaks controlled testing can prevent the large spread of new waves. Author contributions statement: The authors contributed equally to this work, including conceptualization, analysis, methodology, software, and drafting the work.

Estimating the global reduction in transmission and rise in detection capacity of the novel coronavirus SARS-CoV-2 in early 2020

To better control the SARS-CoV-2 pandemic, it is essential to quantify the impact of control measures and the fraction of infected individuals that are detected. To this end we developed a deterministic transmission model based on the renewal equation and fitted the model to daily case and death data in the first few months of 2020 in 79 countries and states, representing 4.2 billions individuals. Based on a region-specific infection fatality ratio, we inferred the time-varying probability of case detection and the time-varying decline in transmissiblity. As a validation, the predicted total number of infected was close to that found in serosurveys; more importantly, the inferred probability of detection strongly correlated with the number of daily tests per inhabitant, with 50 % detection achieved with 0.003 daily tests per inhabitants. Most of the decline in transmission was explained by the reductions in transmissibility (social distancing), which avoided 10 millions deaths in the regions studied over the first four months of 2020. In contrast, symptom-based testing and isolation of positive cases was not an efficient way to control the spread of the disease, as a large part of transmission happens before symptoms and only a small fraction of infected individuals was typically detected. The latter is explained by the limited number of tests available, and the fact that increasing test capacity often increases the probability of detection less than proportionally. Together these results suggest that little control can be achieved by symptom-based testing and isolation alone.

Cumulative Effects of Adding a Small Group Intervention to Social Network Testing on HIV Testing Rates Among Crack Users in San Salvador, El Salvador

The present study evaluates a combination prevention intervention for crack users in San Salvador, El Salvador that included social network HIV testing, community events and small group interventions. We examined the cumulative effects of the social network HIV testing and small group interventions on rates of HIV testing, beyond the increase that we saw with the introduction of the social network HIV testing intervention alone. HIV test data was converted into the number of daily tests and analyzed the immediate and overtime impact of small group interventions during and in the twelve weeks after the small group intervention. The addition of the small group interventions to the baseline of monthly HIV tests resulted in increased rates of testing lasting 7 days after the small group interventions suggesting a reinforcing effect of small group interventions on testing rates.

Clarifying Predictions for COVID-19 from Testing Data: The Example of New York State

With the spread of COVID-19 across the world, a large amount of data on reported cases has become available. We are studying here a potential bias induced by the daily number of tests which may be insufficient or vary over time. Indeed, tests are hard to produce at the early stage of the epidemic and can therefore be a limiting factor in the detection of cases. Such a limitation may have a strong impact on the reported cases data. Indeed, some cases may be missing from the official count because the number of tests was not sufficient on a given day. In this work, we propose a new differential equation epidemic model which uses the daily number of tests as an input. We obtain a good agreement between the model simulations and the reported cases data coming from the state of New York. We also explore the relationship between the dynamic of the number of tests and the dynamics of the cases. We obtain a good match between the data and the outcome of the model. Finally, by multiplying the number of tests by 2, 5, 10, and 100 we explore the consequences for the number of reported cases.

Clarifying predictions for COVID-19 from testing data: The example of New York State.

With the spread of COVID-19 across the world, a large amount of data on reported cases has become available. We are studying here a potential bias induced by the daily number of tests which may be insufficient or vary over time. Indeed, tests are hard to produce at the early stage of the epidemic and can therefore be a limiting factor in the detection of cases. Such a limitation may have a strong impact on the reported cases data. Indeed, some cases may be missing from the official count because the number of tests was not sufficient on a given day. In this work, we propose a new differential equation epidemic model which uses the daily number of tests as an input. We obtain a good agreement between the model simulations and the reported cases data coming from the state of New York. We also explore the relationship between the dynamic of the number of tests and the dynamics of the cases. We obtain a good match between the data and the outcome of the model. Finally, by multiplying the number of tests by 2, 5, 10, and 100 we explore the consequences for the number of reported cases.

Mass testing and containment of Covid-19

Urgent public health interventions to mitigate the spread of Covid-19 curtailed most social activities and work. Meanwhile, maintaining social distancing and lockdown will cause substantial economic loss and social damage. Now the critical question is how to reopen the economy while containing the COVID-19 pandemic without social distancing restrictions? The solution is mass testing. Alternative public health intervention strategies such as shelter-in-place and social distancing policies that may halt economic activities may be curtailed if virus testing which is an effective way to contain Covid-19 is expanded. Although the U.S. still has a longer path ahead, we have estimated that if the number of daily tests is 581,000 per day, i.e., the current number of daily tests would need to be doubled in order for Covid-19 to be contained and social distancing measures to be lifted.

The relationship between quality of self-monitoring and glycemic control in romanian children with type 1 diabetes mellitus

AbstractObjectives: Optimal glycemic control is mandatory in diabetic children andadolescents for the prevention of diabetes complications, but it is difficult to beobtained due to a series of factors, including the limited availability of blood glucoseself-monitoring tests. The aim of our study was to investigate the relationshipbetween the number of daily self-monitoring tests and quality of glycemic control.Material and method: We enrolled 783 individuals previously diagnosed with Type 1Diabetes Mellitus and investigated the significance of differences in HbA1c valuesbetween groups with distinct number of blood glucose measurements at home foreach age group. Results: We found significant improvements of glycemic controlwith the increase in the number of daily tests, with some particularities among thegroups. Conclusions: Among other intrinsic and external factors, blood glucose selfmonitoringhas an important role in obtaining a good glycemic control.

Use of computer simulation to study impact of increasing routine test volume on turnaround times of STAT samples on ci8200 integrated chemistry and immunoassay analyzer.

Automated clinical chemistry analyzers were first introduced as single- and multichannel continuous flow analyzers in the 1960s (1)(2). Ongoing evolution of analyzers has been driven by two factors: ( a ) increased demand by clinicians for rapid turnaround times (TATs); and ( b ) a need to decrease the cost of producing laboratory results and to consolidate laboratory testing into fewer workstations. The diagnostic industry has responded to this need by developing analyzers that have high throughput and expanded test menus, and perform both clinical chemistry assays and immunoassays on a single platform. As a result of these technical innovations, analyzer throughput and assay TAT are now dependent on such factors as ( a ) tests ordered on samples, ( b ) the number of samples loaded on a analyzer, and ( c ) the STAT-vs-routine ratio. Selection of the most appropriate analyzer to meet laboratory TAT consistently is no longer a trivial exercise of dividing the total number of daily tests by an analyzer’s hourly throughput to determine whether it will meet laboratory needs. The goals of this study were to determine the feasibility of using MedModel 2001 (Promodel Corporation) software to develop a simulation model of the ci8200 integrated chemistry and immunoassay analyzer manufactured by Abbott Diagnostics, to estimate the percentage error in the model’s predictions of test TAT, and finally, to use the validated model to evaluate the impact of increased routine test volumes on the TAT of STAT samples. A simulation model of a clinical analyzer uses computer software specifically designed to imitate and capture an analyzer’s dynamic behavior to study its performance under different conditions. Researchers in Europe and the United States have used computer simulations to study various aspects of clinical laboratory operation, including staff assignments, evaluating the queue length, and priority handling and processing TAT for different sample types (3) …