The 2019 coronavirus disease pandemic (COVID-19) is now reaching its second global wave. It is important to know when a national outbreak begins and when it can be expected to end, so that public health guidelines and non-pharmaceutical interventions (NPIs), such as travel restrictions and national or regional closures, are properly implemented to minimize its economy. damage.
A recent study by researchers in France shows how differences in the history of infections and viral spread patterns are important factors in predicting the temporal course of a localized SARS-CoV-2 epidemic.
The team has published a preview of their findings in the medRxiv * server.
Any epidemiological prediction or inference is more reliable when reinforced with large amounts, as this compensates for or nullifies the interindividual differences that are always present. Most models of infectious diseases are based on the deterministic approach, assuming that a large initial number of infected hosts cross the “outbreak threshold”. This, of course, is not true at the beginning and end of an epidemic.
Objectives of the study
The current study examined an outbreak of COVID-19 outside of China, to allow the detection of some cases before the reported onset, due to import. Hidden viral spread can also occur before the largest epidemic is observed, as seen from genome sequencing data obtained in Washington state, USA.
The team also analyzed the period during which strict control measures would be needed to reduce the prevalence below the required thresholds. This should be estimated, including superextension events and other sources of heterogeneity, to accurately predict the end of an epidemic.
The pandemic has led to the publication of several mathematical models, which depend on the basic number of reproduction R0 and individual differences. Stochastic modeling is also important to examine what role superextension events play and how this affects control measures.
The discrete original stochastic (DS) model proposed by the current study explains the individual differences in host transmission on the same day after infection, using a distribution interval rather than a single value. Variability in transmission patterns is also taken into account. The researchers tried to understand how these factors affected exactly the estimated start and end dates of a national outbreak, taking France as an example.
The researchers used several different models to test their parameters, in addition to the DS model. This included the non-Markovian SEIRHD model and a classical deterministic Markovian model.
Origin of the epidemic wave
The researchers found that the average delay from the first imported case to the occurrence of 100 deaths a day was 67 days, indicating that in this case, 93% of outbreaks would continue to spread until they reach this threshold. The impact of overexploitation events was a small reduction in the average delay to 64 days, but about 75% of the simulated outbreaks disappeared before reaching a point where 100 deaths per day occurred.
With the SEIRHD model, the mean delay was 63 days, for both deterministic and stochastic implementation. As expected, however, this model cannot capture the data correctly. A non-Markovian deterministic model also produced the same mean delay.
Estimates are not significantly affected by a reduction in the distribution of serial intervals. However, the delay is reduced by eight days if the number of cases imported at the beginning increases from one to five on the same day, but not if, more realistically, the increase is spread over a few days.
The researchers found no significant change in the period required for an epidemic to reach a point of 100 deaths a day, either with stochasticity or non-Markovian changes. Overextension events can introduce a small delay, as can the number of cases imported at first.
The end of the epidemic
The researchers also found that the 95% probability of extinction was only achieved with a minimum of 7.6 months of blockade, if the events of overextension are ignored. When individual heterogeneity is considered, the period is slightly reduced to 6.9 months. Transmission heterogeneity is a parameter that indicates the non-transmission of most infected people.
Given the period during which a newly infected individual can cause secondary cases, the researchers found that once the blockage is relaxed, the likelihood of new cases is sharply reduced. Again, transmission heterogeneity reduces the risk of bounce by limiting the number of possible transmissions.
Changing the start date of the lock
The researchers found that advancing the blockade one month after the start of the epidemic wave would reduce the extinction time by 96 days and 92 days, that is, by almost 50%, without and with transmission heterogeneity, respectively. Thus, the sooner the intervention is done, the greater the impact.
If this was only two weeks ahead, the extinction time was likely to be 95% within 188 days, with no transmission heterogeneity, and 169 days, which would reduce 41 days of blockage.
As the restrictions relax after the first 55 days of total lockout, the estimated periods increase. For example, the extinction time increases much less if the start date of the lock is February 17, compared to two weeks later or a month later. As the spread of the epidemic would not be so extensive in this case, the most important period would be the first 55 days of closure.
What are the implications?
The greatest impact of chance on model parameters is likely to occur at the beginning and end of an epidemic, due to the low prevalence of infection and the effect of stochastics on propagation patterns. viral. The study estimates that the first wave of the epidemic began on January 16, which is consistent with data obtained from genomic sequencing. The researchers point out that the latter estimate is also uncertain and sampling is incomplete in France.
The effect of the overshoot was to accelerate the initial phase of the epidemic, with the date of origin on January 19, due to the role that overshadowing played in triggering persistent outbreaks.
The study confirms that cases can be detected well in advance of the oldest transmission chain related to an epidemic. The possibility of estimating the period of blocking restrictions required for the eradication of the epidemic can also help public health authorities formulate appropriate policies and intervene early.
Finally, it draws attention to the risk of rebound in relation to the blocking period. Further studies will help to understand the impact of superextension events on the spread of COVID-19.
* Important news
medRxiv publishes preliminary scientific reports that are not peer-reviewed and therefore should not be considered conclusive, guide clinical practice / health-related behavior, or treated as established information.