Understanding the duration of viral shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as well as how it relates to a positive or negative PCR test, is crucial to the implementation of effective public health efforts aimed towards controlling the spread of the virus.
Throughout this ongoing process, infected individuals, who may not yet be experiencing any of the viral symptoms, are shedding viral particles while they talk, exhale, eat, and perform other normal daily activities.
Under normal circumstances, viral shedding will not persist for more than a few weeks; however, as researchers gain a more in-depth understanding of the viral clearance of SARS-CoV-2, they have found that certain populations will shed this virus for much longer durations.
The duration of viral shedding can be used to categorize the infectivity of a person; therefore, this information is crucial in implementing effective infection prevention strategies, such as appropriate quarantine durations and mask requirements.
Currently, SARS-CoV-2 infection is confirmed with a positive polymerase chain reaction (PCR) test that can be conducted regardless of whether an individual is experiencing symptoms. Through such PCR tests, viral shedding of SARS-CoV-2 has been found to have a median duration of 12 to 20 days, with a persistence that can reach up to 63 days after initial symptom onset.
Whereas about 90% of mild cases have been found to clear the virus within an average of 10 days after symptom onset, individuals who have recovered from the severe disease have been found to have prolonged viral RNA shedding with a median duration of 31 days.
In addition to symptom severity being a predictive factor of viral shedding duration, the sampling location also appears to determine when peak viral loads occur. Within the upper respiratory tract (URT), for example, peak viral load appears to occur between days 4 and 6 following the onset of symptoms, within a week of symptom-onset, whereas peak viral loads within the lower respiratory tract appear to arise later.
The viral shedding of SARS-CoV-2 also occurs within the gastrointestinal (GI) tract in the form of stool for up to 33 days after a negative PCR test; however, these viral loads appear to be less as compared to those identified within the respiratory tract and occur at a later time. Notably, the viral shedding of SARS-CoV-2 from the GI tract does not appear to have any correlation with disease severity.
Even among presymptomatic patients, the higher level of SARS-CoV-2 viral shedding from the URT is a key factor in its high transmissibility, particularly when compared to its genetically similar predecessor SARS, which mainly occurred within the lower respiratory tract.
Aside from the viral shedding that occurs in asymptomatic and/or presymptomatic individuals, this time-sensitive characteristic of SARS-CoV-2 can also assist in a wide range of public health surveillance efforts.
We tested viral shedding (in terms of viral copies per sample) in nasal swabs, throat swabs, respiratory droplet samples and aerosol samples and compared the latter two between samples collected with or without a face mask (Fig. 1). On average, viral shedding was higher in nasal swabs than in throat swabs for each of coronavirus (median 8.1 log10 virus copies per sample versus 3.9), influenza virus (6.7 versus 4.0) and rhinovirus (6.8 versus 3.3), respectively. Viral RNA was identified from respiratory droplets and aerosols for all three viruses, including 30%, 26% and 28% of respiratory droplets and 40%, 35% and 56% of aerosols collected while not wearing a face mask, from coronavirus, influenza virus and rhinovirus-infected participants, respectively (Table 1b). In particular for coronavirus, we identified OC43 and HKU1 from both respiratory droplets and aerosols, but only identified NL63 from aerosols and not from respiratory droplets (Supplementary Table 2 and Extended Data Fig. 3).
The primary outcome of the study was virus generation rate in tidal breathing of participants infected by different respiratory viruses and the efficacy of face masks in preventing virus dissemination in exhaled breath, separately considering the respiratory droplets and aerosols. The secondary outcomes were correlation between viral shedding in nose swabs, throat swabs, respiratory droplets and aerosols and factors affecting viral shedding in respiratory droplets and aerosols.
Sometimes underlying endocrine disorders such as hypothyroidism or congenital problems such as follicular dysplasia can cause excessive shedding. It is also possible your dog may have allergies, and this can cause skin and shedding problems.
Otherwise, the most effective method to combat shedding is to remove dead hair with regular brushing, combing, and the use of pore- and follicle-dilating shampoos and baths. Some dogs can even be vacuumed!
Shedding load may occur if there is a shortage of electricity supply, or to help prevent power lines from becoming overloaded. Several factors can lead to load shedding, including extreme weather, sharply increased electric demand, unplanned generation plant outages, transmission constraints, unexpected damage to equipment, unavailability of purchased power or a combination of these situations.
Since shedding load is always a last resort, MISO, for example, has other measures it may take to try and overcome a power shortfall, such as importing more power from other resources or tapping into emergency reserves. It can also order its members, like Entergy, to make a public appeal to customers to voluntarily reduce their energy consumption to prevent load shed.
Technologies exist that allow energy to be stored for future supply, but to date have not been cost-effective compared to alternatives. As these technologies continue to emerge, and in the future become more cost-effective alternatives to other types of investments to enhance resilience, they may play a role in preventing shedding load.
During tests, we make sure to measure client-perceived availability and latency in addition to server-side availability and latency. When client-side availability begins to decrease, we push the load far beyond that point. If load shedding is working, goodput will remain steady even as offered throughput increases well beyond the scaled capabilities of the service.
In terms of visibility, when load shedding rejects requests, we make sure that we have proper instrumentation to know who the client was, which operation they were calling, and any other information that will help us tune our protection measures. We also use alarms to detect whether the countermeasures are rejecting any significant volume of traffic. When there is a brownout, our priority is to add capacity and to address the current bottleneck.
If misconfigured, load shedding can disable reactive automatic scaling. Consider the following example: a service is configured for CPU-based reactive scaling and also has load shedding configured to reject requests at a similar CPU target. In this case, the load shedding system will reduce the number of requests to keep the CPU load low, and reactive scaling will never receive or get a delayed signal to launch new instances.
We are also careful to consider load shedding logic when we set automatic scaling limits for handling Availability Zone failures. Services are scaled to a point where an Availability Zone's worth of their capacity can become unavailable while preserving our latency goals. Amazon teams often look at system metrics like CPU to approximate how close a service is to reaching its capacity limit. However, with load shedding, a fleet might run much closer to the point at which requests would be rejected than system metrics indicate, and might not have the excess capacity provisioned to handle an Availability Zone failure. With load shedding, we need to be extra sure to test our services to breakage to understand our fleet's capacity and headroom at any point in time.
In the beginning of this article, I described a challenge from my time on the Service Frameworks team. We were trying to provide Amazon teams with a recommended default for maximum connections to configure on their load balancers. In the end, we suggested that teams set maximum connections for their load balancer and proxy high, and let the server implement more accurate load shedding algorithms with local information. However, it was also important for the maximum connections value to not exceed the number of listener threads, listener processes, or file descriptors on a server, so the server had the resources to handle critical health check requests from the load balancer.
In some cases, a server runs out of resources to even reject requests without slowing down. With this reality in mind, we look at all the hops between a server and its clients to see how they can cooperate and help shed excess load. For example, several AWS services include load shedding options by default. When we front a service with Amazon API Gateway, we can configure a maximum request rate that any API will accept. When our services are fronted by API Gateway, an Application Load Balancer, or Amazon CloudFront, we can configure AWS WAF to shed excess traffic on a number of dimensions.
MISO projects a 5-gigawatt deficit in meeting its forecasted summer peak of 124-gigawatts. This projected deficiency is due to contributing factors such as predicted above-average temperatures and aging or recently retired thermal resources. Therefore, in times of extreme electric demand with all other options exhausted, MISO may direct the Zeeland BPW and neighboring utilities to implement load-shedding.
MISO has warned that power shortages exist in our region. As we approach the hot summer months, there may be times when the supply of available electric power is unable to meet customer demand. To avoid uncontrolled blackouts, emergency load-shedding may become necessary as