Finding the “zero” patient, the first case of covid-19 , is crucial to find out the origin of the pandemic. Knowing where and why you could contract the disease will give us the necessary clues to avoid the risks of future outbreaks, re-emergencies and new pandemics. Of course, preserving their identity, essential to avoid blaming and stigmatizing.
How to trace the origin of an epidemic
Any investigation of fait accompli, like police detectives, leads us to look back to connect the dots. We have the declaration of 27 cases of pneumonia of unknown cause by the Wuhan health services on December 31, 2019, with the appearance of symptoms on the 7th of that same month. Now we know that the presymptomatic latency period of covid-19 is about two weeks, so we can venture to place it in November 2019.
They probably did not appear suddenly and in all patients at the same time, but you should think about days ago. Already in March 2020 it was ventured that the first case could have arisen in the province of Hubei on November 17, 2019.
After the expansion in China, in January cases began to appear in hospitals in Europe, the United States, Korea … Another fact to take into account is the recognition in April of last year by the CCAES that, when the first cases were detected in Spain, SARS-CoV-2 was already among us. Data subsequently verified by sequencing studies of the genetic material of the virus and its monitoring.
Until now, several prediction models have appeared to find the zero patient and locate and specify the origin of the first contagion. One of the newest has been published very recently in the journal PLOS Pathogens in a three-way collaboration between the University of Kent (UK), Chicago (USA) and the Center for Biology of the Czech Republic.
Are mathematical models reliable to describe the evolution of an epidemic?
In order to understand this novel research, we must first familiarize ourselves with the most commonly used methods. Mathematical models play an important role in the design of possible disease control strategies by focusing on the key aspects of these, determining the threshold for disease survival and evaluating the effect of the designed strategies, among other aspects.
The basic simulation is the SIR model, developed by Sir Ronald Ross and Anderson Gray McKendrick. This type of model is based on the fact that the population can be classified into three independent compartmentalized groups: susceptible individual (S), infected individual (I) and recovered individual (R). From this model, both the number and type of groups can be modified to reflect the behavior of each disease, as shown in Figure 1.
For example, the SEIR model includes an intermediate state, exposed individual (E) with which it is tried to simulate the incubation period. Or the SITR model, where some people or individuals are treated (T), understood as “treated” isolated or vaccinated, thus preventing possible infections from being generated from them. Or the SIRS models, which are endemic models used for diseases such as seasonal flu, where recovered people can lose their immunity and be susceptible to falling ill again.
The development of the models has two phases, the first of adjustment or training and the second of prediction. The first determines the function or mathematical model and the values of the parameters that are compatible with the data observed with the evolution of the epidemic (Bayesian approach).
Once we have the model that best fits the data, we move on to the second phase, that of prediction. The problem, on some occasions, as in this pandemic, has been the lack of data. In the first months of the pandemic, the real number of infected was unknown, so the uncertainty was very high. However, as data has been obtained, mathematical models have helped us to understand how this epidemic is developing and to design strategies to control it effectively.
Know how it disappears to find out how it appeared
The models that we have described in the previous section can be useful to predict the evolution of the disease, but not to trace back the appearance of the first case. To achieve this objective, the researchers of our article of interest have used in an innovative way a mathematical model developed for the extinction of species.
Originally, the model helps predict when a species will disappear based on the number of organisms detected. But in this case a clever approach has been used. The model has been inverted so that it works in the opposite direction to reach the first case, taking into account those that were detected later and even those that went unnoticed at first.
The conclusions of this article coincide with those of other previously published studies: the authors once again indicate the date of November 17, 2019 to which we alluded previously.
If they are correct, when the WHO reacted at the end of January 2020, the infection was already in half the world . The epidemic emerged earlier than estimated and also spread more quickly.
There are two factors that could influence: (1) transmission between asymptomatic individuals was unknown and (2) aerosol transmission was unknown. As we have discussed, data was missing.
Everything converges in November 2019
The fact that both investigations into specific cases and a mathematical model indicate a certain date for the appearance of the first case of covid-19 is very encouraging and indicates that this detective investigation is on the right track.
A possible weakness of the model could be the existence of some type of bias when detecting cases at the beginning of the pandemic, especially when dealing with a single country. Perhaps in the future it will be possible to obtain more information, or to create a model that takes this aspect into account. Or maybe we really have already found the original case.
In any case, after the commotion created by the health crisis, we now glimpse the stage in which we will look back more calmly and we will be able to write the history of the pandemic.
Matilde Cañelles López, Scientific Researcher. Science, Technology and Society, Institute of Philosophy (IFS-CSIC); María Mercedes Jiménez Sarmiento, CSIC Scientist. Systems Biochemistry of the bacterial division. Scientific communicator, Margarita Salas Biological Research Center (CIB – CSIC) and Nuria Eugenia Campillo, Senior Scientist. Medicinal Chemistry, Margarita Salas Biological Research Center (CIB – CSIC)
This article was originally published on The Conversation. Read the original.
