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

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

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

Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater

Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (<5% TPR), moderate (5%-8% TPR), substantial (8%-10% TPR), and high (>10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. IMPORTANCE We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.

Bayesian sequential approach to monitor COVID-19 variants through positivity rate from wastewater

Trends in COVID-19 infection have changed throughout the pandemic due to myriad factors, including changes in transmission driven by social behavior, vaccine development and uptake, mutations in the virus genome, and public health policies. Mass testing was an essential control measure for curtailing the burden of COVID-19 and monitoring the magnitude of the pandemic during its multiple phases. However, as the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementing vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 tests reduced the demand for mass severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. This paper proposes a sequential Bayesian approach to estimate the COVID-19 positivity rate (PR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. PR estimates are used to compute thresholds for WW data using the CDC thresholds for low, substantial, and high transmission. The effective reproductive number estimates are calculated using PR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring the COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. The proposed modeling framework was applied to the City of Davis and the campus of the University of California Davis.

Comparison of Positivity in Two Epidemic Waves of COVID-19 in Colombia with FDA

We use the functional data methodology to examine whether there are significant differences between two waves of contagion by COVID-19 in Colombia between 7 July 2020 and 20 July 2021. A pointwise functional t-test is initially used, then an alternative statistical test proposal for paired samples is presented, which has a theoretical distribution and performs well in small samples. Our statistical test generates a scalar p-value, which provides a global idea about the significance of the positivity curves, complementing the existing punctual tests, as an advantage.

Factors influencing the COVID-19 pandemic situation in Indonesia, Malaysia and Taiwan in 2021

Objectives

The COVID-19 pandemic started over 2 years ago and spread rapidly throughout the world. The total number of cases and deaths is still increasing and the situation remains active across the globe. In the Asian region, COVID-19 vaccination began in early 2021; however, the COVID-19 situation remains uncertain. This study aims to compare the factors that influenced the COVID-19 pandemic situation in three countries in Asia (namely, Indonesia, Malaysia and Taiwan) throughout 2021.

Study design

This ecological study utilises the data from the 'Our World in Data' website.

Methods

In this study, the COVID-19 pandemic situation in each country is described by looking at the average daily number of deaths and cases per million population throughout 2021. A paired t-test was conducted to compare the significance of differences in the pandemic situation between 2020 and 2021. In addition, the COVID-19 vaccination profiles throughout 2021 were investigated. A multiple linear regression analysis was then performed to develop models to explain the factors influencing the COVID-19 pandemic situation in these three countries.

Results

The COVID-19 pandemic situation in Indonesia, Malaysia and Taiwan in 2021 is significantly different from 2020. Malaysia had the highest COVID-19 vaccination coverage (79.4%), followed by Taiwan (78.5%) and Indonesia (58.3%). This study found that the following three factors consistently influenced the number of deaths and cases in these three countries [1]: positivity rate [2]; number of tests per 1000 population; and [3] number of tests per case.

Conclusions

Although the severity of the COVID-19 pandemic situations in Indonesia, Malaysia and Taiwan was different, it is significantly influenced by the quality and quantity of COVID-19 testing and screening, in addition to the vaccination programmes and restriction policies implemented in each country. As a result of the ability of the SARS-CoV-2 virus to mutate, it is recommended that each country strengthen their comprehensive approach to have an effective and efficient coping strategy for the COVID-19 pandemic.

Positivity rates of SAR-CoV-2 infection in orthodontic patients at the orthodontic clinic, University of Illinois Chicago

COVID-19 has impacted and increased risks for healthcare providers, including orthodontists. There is no information regarding the potential transmission risks in the orthodontic community. This study aims to compare the positivity rate of SARS-CoV-2 infection in orthodontic patients at the University of Illinois Chicago (UIC) orthodontic clinic to the positivity rate of the local population in Chicago. All orthodontic patients who sought treatment at the UIC orthodontic clinic from June 16 to October 31, 2021, were invited to participate in the study. Three milliliters of saliva from the participants were collected in the sample collection tubes and subjected to a polymerase chain reaction (PCR) based assay to detect SAR-CoV-2. All participants’ age, sex, history of COVID-19 infection, and vaccination status were recorded. The COVID-19 positivity rates of Chicago, Cook County of Illinois, and the orthodontic clinic at UIC were compared. One thousand four hundred and thirty-seven orthodontic patients aged 6 to 70 years old (41.8% males and 58.2% females) participated in the study. Among all participants, nine participants tested positive for SARS-CoV-2 (5 males and 4 females). During the study, the average COVID-19 positivity rate at the UIC orthodontic clinic was 0.626%. All of the positive participants were asymptomatic, and two of the participants had a history of COVID-19 infection. Among all positive participants, three participants had received complete COVID-19 vaccination. An increased frequency of positive cases at the orthodontic clinic was observed during the time of high positivity rate in Chicago and Cook County. A potential risk of COVID-19 transmission from patients to orthodontic providers remains, even with asymptomatic and vaccinated patients.

Social capital's impact on COVID-19 outcomes at local levels

Over the past thirty years, disaster scholars have highlighted that communities with stronger social infrastructure—including social ties that enable trust, mutual aid, and collective action—tend to respond to and recover better from crises. However, comprehensive measurements of social capital across communities have been rare. This study adapts Kyne and Aldrich’s (Risk Hazards Crisis Public Policy11, 61–86, 2020) county-level social capital index to the census-tract level, generating social capital indices from 2011 to 2018 at the census-tract, zipcode, and county subdivision levels. To demonstrate their usefulness to disaster planners, public health experts, and local officials, we paired these with the CDC’s Social Vulnerability Index to predict the incidence of COVID-19 in case studies in Massachusetts, Wisconsin, Illinois, and New York City. We found that social capital predicted 41–49% of the variation in COVID-19 outbreaks, and up to 90% with controls in specific cases, highlighting its power as diagnostic and predictive tools for combating the spread of COVID.

The Role of SARS-CoV-2 Testing on Hospitalizations in California

The rapid spread of the new SARS-CoV-2 virus triggered a global health crisis, disproportionately impacting people with pre-existing health conditions and particular demographic and socioeconomic characteristics. One of the main concerns of governments has been to avoid health systems becoming overwhelmed. For this reason, they have implemented a series of non-pharmaceutical measures to control the spread of the virus, with mass tests being one of the most effective controls. To date, public health officials continue to promote some of these measures, mainly due to delays in mass vaccination and the emergence of new virus strains. In this research, we studied the association between COVID-19 positivity rate and hospitalization rates at the county level in California using a mixed linear model. The analysis was performed in the three waves of confirmed COVID-19 cases registered in the state to September 2021. Our findings suggest that test positivity rate is consistently associated with hospitalization rates at the county level for all study waves. Demographic factors that seem to be related to higher hospitalization rates changed over time, as the profile of the pandemic impacted different fractions of the population in counties across California.