As the United States enters its fourth month of response to the COVID-19 crisis, many are pointing to the testing of people potentially infected with the disease as the key to effective mitigation of the public health threat. Much confusion exists about testing for SARS-CoV-2, the virus that causes the disease COVID-19. For instance, you may have heard of PCR as a method, but what is PCR, and how does it differ from antibody tests, another much-touted testing modality? We spoke to Director of Licensing and Strategic Alliances Rajnish Kaushik, a virologist by training, as well as Prof. Emeritus Lawrence Wangh, a globally recognized expert on PCR, to understand more about the types of tests used to determine whether someone has been infected with the virus that causes COVID-19, or, indeed, many other infectious agents. Please note that this summary of commonly-used scientific processes is not intended as medical or public health advice. It’s meant to be a handy deeper explanation of scientific terms you may be hearing about in the news, to help you better understand news coverage. 

There are two types of tests discussed in the media: tests to determine whether someone is currently infected with a virus, and those meant to identify whether someone had the virus in the past. For the first kind of test, there are two options: PCR and isothermal amplification technologies. 

The Science of Rapid Testing 

PCR stands for polymerase chain reaction, which is a method for rapidly making copies of a strand of DNA. It’s used in a variety of scientific applications, including genetic sequencing for detecting genetic disorders, or forensic tests popularly known as genetic fingerprinting. It’s also useful in identifying the germs that may infect us. Many viruses, including SARS-CoV-2, contain an RNA genome, so they need to first be reverse-transcribed into DNA. Then, the DNA is examined by the PCR method using virus-specific primers to determine whether the trial has that specific virus. This is quantitative reverse transcription PCR (RT-qPCR), which means that this extra step was needed before PCR. With PCR, scientists compare a trial taken from a suspected patient, say, of saliva, that would contain the virus if the person is infected. They then extract the genetic material, RNA, transcribe it into DNA, then use PCR to amplify that DNA with primers specific to the genetic sequence of SARS-CoV-2. If the DNA is amplified then the machine detects the signal as positive and the patient is likely infected with the virus.

But what if a person comes into a healthcare facility showing similar symptoms, and the PCR test shows that they are not infected with the specific virus for which they were tested? They may be sent home without an answer, or more tests are needed. In a public health crisis where managing community spread of a specific virus is the main goal, PCR is a rapid, efficient way to determine who carries the one virus of concern. However, in a medical setting, and in many public health contexts, simply knowing what a patient doesn’t have is only a partial answer. Identifying the infectious agent exactly, rather than ruling out one single infectious agent, is important in many settings. Also, PCR detection can be time-consuming, expensive, needs heavy equipment, and may not be done at the testing site.

Enter isothermal nucleic acid amplification technology. It doesn’t require complex PCR machines or multiple cycles of heating and cooling the reaction mixture. While this process also requires reverse transcription of the genetic material of RNA viruses, it then uses a DNA polymerase to rapidly generate multiple copies of virus-specific DNA using virus-specific primers. The key advantage of this technology is the rapidity with which the diagnosis can be made. Also, it doesn’t require heavy equipment and can easily be used as a point of care device at the testing site. Both PCR and isothermal nucleic acid amplification technologies can be multiplexed, in other words, set up so that multiple tests run concurrently, to diagnose more than one pathogen from the same trial though these amplification assays. As a point of care device, isothermal nucleic acid amplification testing equipment delivers the result much faster. Multiplex diagnostic assays can help teams do more than rule-in or out a single infectious agent, instead giving them a faster diagnosis when symptoms, such as a cough, are common to many different infections. 

What Exactly Are Antibody Tests?

Finally, there are antibody tests. When people are infected with a virus, their immune systems produce a range of molecules to fight the infection. This includes antibodies of the type immunoglobulin M (IgM) and G (IgG). IgM is the first antibody launched in the immune response from the host after the infection followed by more long-lasting IgG antibodies. IgG forms to help the body identify an infectious agent should it encounter it again. If the virus is ever again present in the patient, the IgG antibodies will bind to it and signal the body to deploy the immune response against that infectious agent at much shorter notice. It’s this binding action that is tested for in antibody tests. Since it takes time for the body to create, it’s often, though not always, a sign of past infection. While the body produces many antibodies in a range of contexts, when you hear of “antibody tests” in the context of the public health crisis, the relevant ones are those that test for the presence of IgG or IgM in the bloodstream. This would most likely indicate a past infection. 

Much like tests for active infection, tests for antibodies come in different types. Lateral flow assays (LFA) take a small blood or saliva trial from a patient and flow it through a strip that contains virus particles. If the blood contains IgG/IgM specific to that virus, those antibodies will attach to the viral proteins. If there are enough antibodies in the blood sample, they will adhere to the viral proteins in large enough numbers to show as lines on the strip, similar to a pregnancy test. 

Antibody tests can also involve enzyme-linked immunosorbent assays (ELISA), which on the other hand, use a plate coated with proteins from a virus, as well as a substance that will turn fluorescent when it’s around molecules that have bound together. A blood or saliva trial from a patient is placed on the plate. If IgG/IgM specific to the virus is present in the sample, those antibodies will bind to the viral protein. The substance that becomes fluorescent when in the presence of bound molecules will start to glow, verifying the presence of the specific antibodies. Like isothermal amplification, LFA provides rapid on-site detection as compared to ELISA, which takes more time and can only be done in clinical labs. 

Antibody tests vary greatly in accuracy. This is for several reasons. One is human error - a faint glow or band may seem a stronger indicator of the presence of antibodies to some testers than others. Another is the type of viral protein used to make the test. Many viruses have a couple of proteins on their outer shells, including the proteins making up the shell itself, as well as the spike, a protruding structure that plays a role in helping the virus attach to its victims’ cells. Sometimes, these proteins can be similar among related viruses, meaning that a protein making up the shell of SARS-CoV-2 may be similar to the proteins encapsulating other coronaviruses, such as those which cause the common cold. If they are similar enough, antibodies for another virus may attach to proteins from SARS-CoV-2, giving false-positive results for SARS-CoV-2. Some tests may even use an incorrect protein, one from a different but similar virus. The spike proteins on SARS-CoV-2 are more distinctive than its shell proteins, so more recent tests use those spike proteins to get more accurate results, ensuring that they are identifying antibodies that truly target the novel coronavirus.

The Bottom Line

Both nucleic acid and antibody detection have their own pros and cons. While nucleic acid-based detection can be more accurate, it can only recognize the currently infected patients, whereas antibody tests can detect the infection even in people who were infected previously but now cleared of that virus. Accurate antibody tests can provide a more realistic number in terms of the percent of total infected individuals in the population who may also be immune to the virus, though more research is being done to confirm this.

Again, this is not medical advice. Please contact your healthcare practitioner if you have any questions or concerns about your own health. As questions about deploying tests become a concern for many around the world, knowing the science behind virus testing can shed light on the many logistical challenges communities face in testing members. This year, more than ever, understanding scientific terminology can help you be a more informed consumer of news reports and updates.

*Important notice: medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

In a recent study posted to the medRxiv* pre-print server, a team of researchers performed an observational study to understand the effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing behavior on the efficacy of the SARS-CoV-2 vaccines.

The efficacy of vaccines against the newly emerging SARS-CoV-2 Omicron variant of concern (VOC) has been extensively studied. Some reports state that vaccination may have little or no effect in preventing Omicron infection, but it can significantly reduce coronavirus disease 2019 (COVID-19)-related hospitalization. In contrast, other reports suggest that the vaccines may increase the probability of Omicron VOC infections. However, vaccine effectiveness reported by such observational studies may be affected by the testing behavior of a population. 

About the study

The present study investigated the differences in SARS-CoV-2 testing behavior exhibited by vaccinated and unvaccinated individuals and their effects on observational studies that examined vaccine efficacy.

Between October 2021 and November 2021, an online study about COVID testing was conducted on 1,526 Australian adults (1,064 women, 430 men, and 32 non-binary or unspecified). Self-reported vaccination status, whether unvaccinated or vaccinated with one or two doses, was collected from the participants.

Individuals vaccinated with two doses were fully vaccinated at the time of the study. The study also collected self-reported responses regarding the three measures of COVID-19 testing behavior, namely, the intention of COVID-19 testing, self-reported COVID-19-test in the past month, and self-reported COVID-19 test performed ever.  

The correlation between the nature of the intention towards COVID-19 testing and the vaccination status of the individuals was analyzed across the cross-sectional baseline data collected in the study.

Results

The results showed that the 1,526 individuals who participated in the study were of a mean age of 31 years and belonged to different Australian states. Out of these, 22% had undergone at least one COVID-19 test in the past month and 61% had been tested for COVID-19 at least once in their lifetime. In the case of vaccination statuses of the individuals studied, 17% were unvaccinated individuals, 11% were partially vaccinated or were vaccinated with one dose, while 71% were fully vaccinated with two or more doses.

The study indicated that individuals who were fully vaccinated were twice as probable to have a positive attitude towards COVID-19 testing as compared to unvaccinated individuals. Furthermore, fully vaccinated individuals reported twice the likeliness of being tested in the past month as compared to unvaccinated individuals. Partially vaccinated individuals had a more positive COVID-19 testing intention than unvaccinated individuals, but less positive as compared to fully vaccinated individuals.     

Conclusion

The current study findings show that vaccinated individuals had a positive COVID-19 testing intention as well as testing behavior as compared to unvaccinated individuals.

The difference in testing behaviors should be taken into account when assessing the efficiency of vaccines against SARS-CoV-2 based on observational studies. The researchers believe that the differences noted in testing behaviors can have long-standing implications on both testing policy and research methods.

*Important notice: medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

In Hypothesis Testing 1, you were introduced to the ideas of hypothesis testing in the context of deciding whether a coin was fair or biased in favor of heads.  In this section hypothesis testing concerning population means is explored.

	Testing H0: µ=µ0 vs. Ha: µ>µ0 When the Population Standard Deviation is Known Assumptions In all three methods it is assumed that the distribution of trial means for samples of size n is, at least, approximately normal with mean given by µ0 (assume that µ0 is 50 for the next examples) from the null hypothesis, and standard deviation sigma/Sqrt[n] (which equals 5/Sqrt[36]=5/6). Method 1--The Critical xbar Method: Method 2--The Critical Z Method: Step 1: Based on alpha and the alternative hypothesis, find a critical z-value or z-values (1.645 for the example). Step 2: Take the random trial and compute the trial mean, xbar (for example, suppose the trial xbar that you get is 52.3). Step 3: Put xbar along with mu, sigma, and n into .   The resulting z-value is called the computed z-value. (For the example, the computed z-value is 2.76).  If the computed z-value lies outside of the critical z-value found in 1, reject the null hypothesis; otherwise, fail to reject the null hypothesis.  (For the example, 2.76 lies outside of 1.645, so you would reject the null hypothesis) Method 3--The p-value Method: Step 1: Take a random trial and compute the trial mean, xbar.  (Suppose, for example, that xbar is 52.3) Step 2: Put the computed xbar along with mu, sigma, and n into .  (For the example the computed z-value is 2.76)  Find the probability in the tail beyond (beyond is determined by the alternative hypothesis) the computed z-value.  If the test is a 1-tail test, this probability is called the p-value.  (For the example the p-value=0.003). If the test is a 2-tail test, double the probability to find the p-value. Step 3: If the p-value is less than or equal to alpha, reject the null hypothesis.  If the p-value is greater than alpha, fail to reject the null hypothesis.  (For the example, since 0.003<0.05, you would reject the null hypothesis)
	Type I and Type II Errors To review, a Type I error occurs when the null hypothesis is true but the test rejects it, while a Type II error occurs when the null hypothesis is false but the hypothesis test accepts it.  P[Type I error]=Alpha and P[Type II error]=Beta.  In the next examples Type I and Type II errors will be calculated in hypothesis tests for a population mean. In all the following examples assume that a random trial of size 36 has been taken from a population with standard deviation 5.  Assume that the trial mean for this trial of size 36 is xbar=52.7.  Finally, assume that the significance level, alpha, is 0.05. Example 1: Testing H0: µ=50 vs. Ha: µ>50 Using the critical xbar method, the critical z-value is 1.645.  From the equation you get 1.645=(critical xbar-50)/(5/6), and solving this for the critical xbar results in critical xbar=51.37.  Since the trial xbar is 52.7, you would reject the null hypothesis in favor of the alternative.  What is beta, the probability of a Type II error if the population mean is in fact 52?  The computation is not shown here but in the title section of the next graph you see that beta is 0.22. Example 2: Testing H0: µ=50 vs. Ha: µ<50 This example mirrors example 1.  Again, using the critical xbar method, the critical z-value is -1.645.  From the equation you get -1.645=(critical xbar-50)/(5/6), and solving this for the critical xbar results in critical xbar=48.63.  Since the trial xbar is 52.7, you would accept the null hypothesis.  What is beta, the probability of a Type II error if the population mean is in fact 52?  The computation is not shown but beta is equal to 0.99. Example 3: Testing H0: µ=50 vs. Ha: µ<>50  (<> means not equal) In Example 3, you the alternative hypothesis leads you to reject the null hypothesis for either large or small values of xbar.  This is a two tail test.  There are two critical z-values, -1.96 and +1.96.  From the equation you get -1.96=(critical xbar-50)/(5/6), and +1.96=(critical xbar-50)/(5/6).  Solving these two equations results in the critical xbar values of 48.37 and 51.63.  Since the trial xbar is 52.7, you would reject the null hypothesis.  What is beta, the probability of a Type II error if the population mean is in fact 52?  The computation is again not but from the title section of the next graph you see that beta is 0.33.  The red shaded area is alpha, the probability of a Type I error, and the blue shading is beta, the probability of a Type II error.
	Testing H0: µ=µ0 vs. Ha: µ>µ0 When the Population Standard Deviation is Unknown In finding confidence intervals for the population mean, for small samples from a normally distributed population where the population standard deviation was unknown, you had to use Student's t-distribution to complete the solution.  The situation is the same in hypothesis testing.  Instead of using , you must use where the expression has a t-distribution with n-1 degrees of freedom. Example: A random trial taken from a normal population produced the numbers 8, 10, 9, and 8.6.  At the 5% significance level, is the population mean equal to 10? First, the null and alternative hypotheses must be stated.  They are H0: µ=10 and Ha: µ<>10 where '<>' means not equal. Since the statement of the example expresses no preference in determining whether the mean of the population is less than or greater than 10, the 'not equal' alternative hypothesis is used.  The p-value approach using the t-statistic shown above is employed.  From the random trial values, you find xbar=8.9 and s=0.84.  Substituting them into the t-statistic formula, you get calculated t=(8.9-10)/(0.84/Sqrt(4))=(-1.1)/(0.84/2)=-2.62.  Since a two tail test is indicated by the alternative hypothesis, the p-value is found by computing the area under Student's t-distribution with 3 degrees of freedom to the left of -2.62 and doubling the result.  Student's t-distribution is symmetric about zero, so instead of finding the area to the left of -2.62, you can find the area to the right of 2.62.  In the row of the 't-table' corresponding to 3 degrees of freedom, you find 2.353 and 3.182.  The number that you seek, 2.62 lies between these two numbers.  Then the area under the Student's t-distribution with 3 degrees of freedom to the right of 2.62 must lie between 0.025 and 0.05, the numbers at the top of the columns corresponding to 2.353 and 3.182.  The p-value lies between 2*0.025=0.05 and 2*0.05=0.10.  The p-value is larger than 0.05 so the null hypothesis is accepted.

In a recent review published in Nature Reviews Bioengineering, researchers assessed the changing landscape of lateral flow tests (LFTs), and the development of next-generation LFTs based on lessons learnt from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic.

Background

LFT feasibility and acceptability for large-scale SARS-CoV-2 testing to Strengthen population health have been observed during the coronavirus disease 2019 (COVID-19) pandemic. However, there are several limitations of LFTs, and roadblocks in developing next-generation LFTs. Identifying and working on the drawbacks and bottlenecks could accelerate LFT development and expand the diagnostic landscape of viral infections, such as COVID-19, and diseases of epidemic potential, especially those caused by antibiotic-resistant causative microbes.

About the review

In the present review, researchers described the evolution of LFT testing, the advantages and disadvantages of LFTs, and identified roadblocks in developing next-generation LFTs based on the lessons learnt from the COVID-19 pandemic.

The changing landscape of LFT diagnostics

LFTs were initially used as radiological immunoassays and latex agglutination tests and enabled paper-based dipstick tests for urinary glucose quantification. Eventually, the tests were used for pregnancy-related, serological assessments and diagnosis of human immunodeficiency virus (HIV) infections.

The COVID-19 period accelerated LFT development, during which cases of unknown-cause pneumonia were reported in Wuhan, genetic sequencing information was shared, and the World Health Organization (WHO) released interim guidelines on rapid antigen test usage.

Eventually, rapid antigen testing was performed in and beyond health service settings and beyond in conjunction with reverse-transcription-polymerase chain reaction (RT-PCR) analysis. However, only 0.40% of three billion SARS-CoV-2 tests performed until the middle of 2022 were conducted in low-resource settings, raising ethical concerns and affecting the collective pandemic response abilities.

Pre-COVID-19 pandemic LFTs used HIV antibodies and targeted antigens and antibodies. The tests were used for self-testing, clinical diagnosis, and screening at homes, clinics, and community-based settings, using gold nanoparticles and latex beads, with manually captured results. During COVID-19, LFTs used SARS-CoV-2 antigens, and their use was expanded to surveillance efforts. Ever since, LFTs have been used in additional settings such as mass gatherings, schools, borders, and workplaces, using quantum dots in addition to other materials. However, the method of result capture remained manual.

Lessons learnt from the SARS-CoV-2 pandemic

LFT sensitivity is reportedly lesser than RT-PCR analysis, in the range of 34.0% to 88.0% in detecting SARS-CoV-2 and 99.6% specificity. One lakh to 10 lakh SARS-CoV-2 genome copies/ml, can be detected by rapid antigen testing. In contrast, molecular techniques like RT-PCR analysis can identify 1.0 to 100.0 genome copies/ml, and SARS-CoV-2 presence in one to two days before LFT. However, LFTs were used successfully during COVID-19 due to the high SARS-CoV-2 transmissibility and short incubation periods to assess the infectiousness or transmission risks of SARS-CoV-2.

Molecular tests, although highly accurate, are unfavourable for mass-scale use and require centralized testing and greater duration to generate reports, whereas LFTs can provide rapid testing with high scalability but low sensitivity. In 2021, LFTs were rapidly adopted in England, surpassing PCR usage. Major roadblocks in LFT development and use include the lack of accessibility to well-characterized samples for testing and validation, low sensitivity, limited digital connectivity, scarce cost-efficacy evidence, delays in regulatory processes, and centralized manufacture of materials.

Multiplexing and using quantum- and nano-materials, nucleic-acid-based LFTs, machine learning, and CRISPR (clustered regularly interspaced short palindromic repeats) could Strengthen LFT connectivity and accuracy. Observations from the pandemic have shown that LFT self-testing at a large scale could offer several benefits, such as early identification and prompt self-isolation, increased accessibility to diagnostic tests, increased frequency of testing, increased compliance with public health measures, curtailed viral transmission, and facilitated early recovery. However, access to self-tests is inequitable, with considerably lower adoption by low-income and middle-income nations.

Bioengineering efforts could Strengthen specimen preparation, incorporating nucleic acid-based amplification and detection, enabling multiplexing, digital connection with mobile-Health (m-Health) databases and healthcare systems, and green manufacture of products to create simple next-generation LFTs with high accuracy, ease of use, cost-effectiveness, and rapid testing with improved digital connectivity, especially for identifying infections caused by antibiotic-resistant microbes.

Next-generation LFTs would target antigens, antibodies, and molecules, with AMR (antimicrobial resistance) panels and quick response (QR) codes, for use not only for self-testing, clinical diagnosis, screening and surveillance testing but also for environmental monitoring, using ultra-sensitive materials such as enzymatic nanoparticles, and nanodiamonds, with digitalized result capturing. The data could be linked to healthcare systems. Incorporating molecular detection would enhance diagnostic accuracy. Cas-based reactions could be combined with enzyme-amplified LFTs, and spin-enhanced quantum nanodiamond sensing and background subtraction could be used for ultra-sensitive detection. Machine learning would enable the automatic classification of LFT findings.

To conclude, based on the review findings, next-generation of next-generation LFTs could provide means for rapid and decentralized testing with high sensitivity and specificity at a mass scale. However, incorporating bioengineering approaches, machine learning, digitization, and multiplexing would require global collaborative efforts, regulatory harmonization, and an increase in funding to Strengthen research infrastructure and increase the availability of suitable reagents. Efforts must be made to overcome LFT shortcomings, such as high false-negative rates, to provide next-generation diagnostic LFTs, with an equitable distribution, across the globe, to Strengthen global preparedness against pathogens.

► New 992-generation Porsche 911
► Pictured on test
► All you need to know in detail 

Stuttgart’s unending evolution of the 911 range will continue with this – the 992.2. Spotted by our spy photographers on test in both convertible and couple forms, the facelifted 911 will be available in the near future, and could bring an interesting U-turn on the powertrain front.

Returning to naturally-aspirated

It’s possible the refreshed 992.2 will be returning with a 4.0-litre nat-asp flat-six. Take a close look at the exhaust system on the new 992.2 mules shown here, and you’ll see they clearly resemble the system on the current Cayman GT4 and GTS 4.0. No turbos in those.

GT-models aside, the 911 has been turbocharged for a while, with a forced-induction 3.0-litre engine in the back of most models. However, CAR understands that buyer demand for a larger, purer engine has meant Stuttgart has reintroduced a non-turbocharged option after all. The fact the Cayman had a larger displacement powerplant than the 911 could have been an issue, too. It’s likely this NA engine could exist as a seperate model or in the GTS only, sitting alongside the current turbocharged lump we see in the majority of the range.

The move will surely generate increased demand for the new 911, and it won’t cost much in R&D either. It seems Stuttgart engines will essentially be using the same engine already seen in the Cayman. 

It looks similar to the outgoing 992?

Of course it does! Variation between different 911 models such as the 992 and 991 are subtle at best, and differences between facelift models are even more glacial. Still, there are differences – look closer and you’ll see a range of styling tweaks that’ll provide Porsche fans an instant idea of whether they’re looking at a 992.1 or 992.2.

The DRLs appear to be slightly different this time round, and there’s also a new rear light arrangement – visible even with Porsche’s camouflage coating. A reworked bumper and new air intakes also surround the aforementioned central-mounted exhausts.

Check out our Porsche reviews

Thunder Bay, Ont. — Volunteer Hal Lightwood spent his entire day Wednesday cycling on the spot to demonstrate communication technology for Amik Technology Inc. while doing double duty to raise funds for the United Way.

I’ll probably cycle around 200-300 kilometres by the end of the day,” he said, while pedalling in front of an elaborate screen setup at the Valhalla Inn during the Prosperity Northwest business building conference and trade show.

“The conference closes at 7 p.m., so until they kick me out, I’ll just keep going,” he said.

Lightwood, who calls himself “just an avid cyclist,” trains regularly and said he is “always happy to spend a day on the bike for the United Way while showing off some (technology) equipment here as well.”

Donors were encouraged to use either an online link or call the United Way office to donate.

Lightwood said Amik Technology Inc. built the virtual cycling system for him.

“They asked me to demonstrate it here and I said I would do it, but I wanted to raise money for the United Way while doing that,” he said.

Bob Angell, who co-owns the Amik company with Ross McCubbin, described the technique as a Polycom system.

“It looks like a virtual student in a classroom,” Angell said.

Amik Technology builds and integrates wireless products together over cellular data that allows people to use equipment without having to be technologically trained.

“Our goal is to try to serve people in a convenient, easy way for video conferencing, streaming, zooming, and that kind of communication service,” he said. “Hal is streaming out to the internet now and everybody on the internet is donating money. I see that he’s doing a great job and this is what the customer sees.”

Angell says their company ties in well with the mining and energy sectors who use their services for virtual meetings, Zoom meetings, and team meetings. He described the technology’s use of a “video bar” that enables Zoom calls without the use of microphones and other equipment.

Story continues

“You need the bar, monitor and cart and you can put it in whatever room you want. It’s wireless data,” Angell explained. “You just have to turn on the power, make your call and talk.”

Angell says their company has a lot of their systems east of Thunder Bay in remote First Nation communities and other corporations, and minimally in the mining sector.

“But that’s why we’re here,” he said, noting that the Prosperity Northwest trade show aspect of the conference provides a great opportunity to showcase the technology to the many mining, forestry and energy organizations also attending the event.

The company has manufactured more than 80 systems that are being distributed across Canada.

Meanwhile, as exhibitors packed up at the end of the day on Wednesday, Lightwood raised more that $1,100 for the United Way.

Sandi Krasowski, Local Journalism Initiative Reporter, The Chronicle-Journal

We analyzed 1,292,165 SARS-CoV-2 test results from residents and employees of 361 long-term care facilities in Florida, USA. A 1% increase in testing resulted in a 0.08% reduction in cases 3 weeks after testing began. Increasing SARS-CoV-2 testing frequency is a viable tool for reducing virus transmission in these facilities.

Residents of long-term care facilities (LTCFs) in the United States have suffered a disproportionate number of deaths from SARS-CoV-2.^[1] Testing frequency and result turnaround times may be more relevant than test sensitivity for infection control,^[2,3] information that might be used to guide infection control efforts in congregate living facilities.^[4] Semimonthly testing for SARS-CoV-2 was mandated in Florida, USA, for all employees and residents of skilled nursing, elder care, and assisted living facilities beginning June 7, 2020.^[5] Comparing data from before and after the mandate took effect, we evaluated the effect of testing frequency on weekly SARS-CoV-2 case rates in a real-world setting.

We analyzed deidentified test results from Florida LTCFs during June 2020–April 2021, aggregated with the Nursing Home Provider Information dataset,^[6] which includes the number of facility beds and staff and average aid hours per resident. We further combined our dataset with Johns Hopkins University SARS-CoV-2 time-series data on rates of hospitalization and death.^[7] For the duration of the study period, only care facility staff were permitted entry to the facilities to limit potential sources of infection.

We used a generalized linear mixed regression model with weekly cases as a negative binomial random count variable to assess how the independent variables affected test positivity. We created a naive model based on frequency of SARS-CoV-2 on the day before the start of semimonthly testing to establish a baseline from which to forecast change. We then regressed weekly positive cases from the date semimonthly testing began (given heterogeneity in compliance, modeled as percentage increase in number of tests above baseline), cases from the preceding week, new cases per 100,000 persons in the county, total tests per occupied bed (a surrogate for compliance with semimonthly testing), number of certified beds in the facility, total nurse staffing hours per resident per day (as a surrogate for quality of care), and whether the date of infection was after January 1, 2021, to control for vaccination effects. We analyzed the date that semimonthly testing began as distinct weeks preceding any change in average weekly SARS-CoV-2 cases to investigate a potential time-delay between onset of testing and any potential reduction in case rate. We log transformed all variables except number of cases in the preceding week. We applied a random effect for each nursing home. We performed all analyses using R statistical software (https://www.r-project.org). Review of deidentified data was deemed exempt from institutional review.

We analyzed 1,292,165 SARS-CoV-2 RNA test results from residents and employees among 361 facilities located across 247 Florida postal codes. The average age (±SD) of the study population was 49 years (±31). The average number of new cases among all LTCFs in Florida was 187.9 per week (±148.7), an estimated 0.7 tests/week/occupied bed (±16.2). The average test turnaround time from laboratory receipt was 17.1 hours (±10.4). The average number of tests completed per week was 31,454 (±10,926).

In multivariable analysis, a 1% increase in testing frequency resulted in a 0.08% reduction in SARS-CoV-2 cases (Table). On the basis of the coefficients from the multivariable model, we predicted that a 10% increase in testing frequency would result in a 1% reduction in the weekly long-term care facility case rate among residents. Assuming generalizability on the basis of similar characteristics between LTCFs in our dataset and those reported by the Nursing Home Provider Information dataset,^[6] that reduction would result in 126 fewer cases per week, or 6,552 fewer cases per year, among LTCF residents across the United States.

Our findings suggest that even a 1% increase in testing might be an effective strategy for combating the SARS-CoV-2 pandemic among LTCF residents, although results likely would not emerge until ≈3 weeks after the increase. In the initial 1–2 weeks after semimonthly testing began, isolation and contact tracing interventions likely had not had sufficient time to substantially reduce viral transmission. Conversely, increased testing frequency >3 weeks before data collection was likely too remote to affect case estimates for a given week. A similar finding has been reported; a SARS-CoV-2 infection outbreak in a 135-bed facility was contained predominantly through serial testing of all residents and staff every 3–5 days.^[8] Routine testing is furthermore necessary to identify presymptomatic and asymptomatic cases, which could account for up to 40% of new infections^[9] and contribute substantially to transmission. Additional studies should evaluate the cost per case prevented of strategies employing varying frequencies of testing to guide use of testing programs among LTCFs.

Our study was limited by the absence of details on interventions started in response to positive tests or whether test samples were from employees or residents. We also were unable to account for concomitant local or statewide interventions such as mask mandates, resident spacing, or indoor ventilation, which might have confounded the effects of testing frequency. Thus, our findings cannot definitively attribute the reduction in case rates to increased testing frequency. However, the large trial size and number of LTCFs included in the analysis, which controlled for several notable confounders, lend credibility to our findings. We advocate increasing SARS-CoV-2 testing frequency as a viable tool for reducing transmission in LTCFs.