The 2019 pandemic of coronavirus infection (COVID-19) is now well into its second worldwide wave. It is important to know when a national uprising will begin and when it is expected to end, so that public health guidelines and non-pharmacological interventions (NPIs) – such as travel restrictions and national or regional locks – implemented appropriately to reduce their economy. damage.
A recent study by researchers in France shows how differences in the history of infection and patterns of viral transmission are important factors in predicting the temporal course of local infection of SARS-CoV-2 .
The team has published a preview of their results on the medRxiv * server.
Epidemiological prediction or conclusion is most reliable when pushed by large numbers, as this compensates for or eliminates the interspecific differences that the -resident present. Most models of infectious disease rely on the definitive approach, assuming that an initial large number of infectious hosts cross the threshold of a revolution. ‘This is, of course, not true at the beginning and end of epilepsy.
Study objectives
The current study examined outbreaks of COVID-19 outside China, to allow some cases to be detected before the report began, as a result of imports. Latent secretory transmission may occur before the major disease is seen, as evidenced by genome sequence data obtained in Washington state, USA.
The team also analyzed the length of time that strict control measures would be required to bring the frequency down to below the required thresholds. This should be measured, including superstitious events and other sources of heterogeneity, to accurately predict the end of disease.
The pandemic has revealed several mathematical models, which depend on the basic reproduction number R0, and individual differences. Stochastic modeling is also important to examine the role of superstitious events, and how this affects control measures.
Study modules
The original stochastic (DS) model proposed by the present study describes individual differences in host distribution on the same day after infection, using distributed serial intervals rather than one. value. Variability in distribution patterns is also reported. The researchers aimed to understand how these factors influenced the estimated start and end dates of a national revolution, taking France as an example.
The researchers used several different models to test their parameters, in addition to the DS model. This included a non-Markovian SEIRHD model, and a Markovian model of classical certainty.
The origin of the epidemic wave
The researchers found that the median delay from the first case was 67 days to 100 deaths per day, indicating that, in such a case, 93% of outbreaks would continue until them this stage. The impact of superstitious events was a small reduction in median delay to 64 days, but about 75% of the symbolic events died before reaching a level where 100 deaths occurred in a day.
With the SEIRHD model, the median delay was 63 days, both for definitive extraction and stochastic delivery. As would be expected, however, this model cannot capture the data properly. A definitive non-Markovian model also produced the same moderate delay.
A significant reduction in the distribution of serial intervals does not significantly affect the estimates. However, the delay is reduced by eight days if the number of cases initially admitted is increased from one to five on the same day, but not if, more reasonably, the spread on spread over some days.
The researchers found no significant change in the time required for epilepsy to a rate of 100 deaths per day, either with stochasticity or non-Markovian changes. Dissemination events could include some delays, as well as the number of cases initially referred.
End of epilepsy
The researchers also found that the 95% probability of extinction was reached in just 7.6 months of locking, if superstitious events are neglected. When individual heterogeneity is considered, the duration is slightly reduced to 6.9 months. Transmission heterogeneity is the parameter indicating non-transmission by the majority of people with disease.
Risk of relapse
Describing the length of time a person with a new disease can cause secondary cases, the researchers found that once a lockout is slowed, the likelihood of new cases is quickly reduced. Here again, transmission heterogeneity reduces the risk of relapse by limiting the number of possible transitions.
Changing lock start date
The researchers found that the progression of the lockout one month after the onset of the epidemic wave would reduce the extinction time by 96 days and 92 days – that is, almost 50% – without and with transmission heterogeneity, by leth. Therefore, the earlier the intervention, the greater the impact.
If this had only progressed within two weeks, the extinction time was 95% likely to be within 188 days, without transmission heterogeneity, and 169 days with it, leading to a reduction. of 41 days of locking.
As the restrictions relax after the first 55 days of full lock-in, the estimated times increase. For example, the extinction time increases much less if the lockout start date is February 17, compared to two weeks later or a month later. Since the spread of the pandemic would not be so widespread in this case, the most important period is the first 55 days of the lockout.
What is the impact?
The randomness of the model is likely to have the greatest random effect at the beginning and end of disease, due to the low incidence of the disease and the effect of stochasticity on viral transmission patterns. The study concludes that the first wave of epilepsy began on January 16, which agrees with the data obtained from genomic classification. The researchers point out that the latter estimate is also uncertain, and sampling incomplete, in France.
The effect of superstition was to accelerate the first stage of epilepsy, with the start date of January 19, because of the role that superstition plays in promoting chronic epilepsy.
The study confirms that cases could be detected long in advance with the earliest transmission chain associated with epilepsy. The ability to estimate the duration of lockout restrictions required to eradicate the disease can also help public health authorities to formulate appropriate policies, and early intervention.
Finally, it draws attention to the risk of relapse in relation to the lockout period. Further studies will help to understand the impact of superstitious events in the distribution of COVID-19.
* Important message
medRxiv publish preliminary scientific reports that are not peer-reviewed and, therefore, should not be seen as final, guiding health-related clinical practice / behavior, or be treated as information established.