Measures taken in the first six months of the pandemic have kept us from the catastrophic spread of Covid19 that some had predicted. But we are by no means in control, as the numbers are showing an alarming increase.
In principle, the pandemic will die out if new infections are prevented for just three weeks. It can be possible when there is “herd immunity”, which can come about with the help of a vaccine, or when everybody stays disciplined.
Discipline seems out of question, as many are yet to understand what we are facing.
As for the vaccine, it is, so far, not a reality. The toll, while we await herd immunity, is what we would like to keep down. Philippe Carmona and Sylvain Gandon, from Université de Nantes and the nuclear research centre at Univ Montpellier, in France, undertook a study, completed in October 2019 before Sars-CoV-2 raised its head, of how observing the behaviour of a pathogen could help optimise the intervention to contain its spread.
The study, just published in the journal, PLOS Computational Biology, identifies ups and downs, or seasonality, in how an infection flourishes, and identifies a period, which is called “the winter” when conditions for the pathogen are not favourable.
The study defines a value, “l” (the Greek letter lambda), called the “birth rate”, the rate at which the infection grows, which depends on the rate of transmission and how many susceptible persons surround an infected person. And then, there is a value, “µ” (the Greek letter mu), called the “death rate”, which is the rate at which the number of infected persons reduces, when infected persons get well, or die.
The ratio of the two rates, l/µ, is the now well-known R0, or the reproduction rate. The simplest understanding is that the infection will spread if R0 is greater than one, and will go extinct if it is less than one.
This understanding, however, the paper points out, is true provided the number of infections to start with is reasonably high. How the population of pathogens rises or falls, the paper says, is sensitive to the conditions in the environment of the infection and could be driven to extinction, during patches when the conditions are right, even when the value of R0 is above one.
The paper cites studies that show how the pathogen that causes malaria, or the viruses that cause dengue, West Nile fever, tick-borne brain inflammation, hepatitis, shingles, chicken pox, influenza, all have their “seasonal window of occurrence” and are affected by the temperature and humidity.
The factors, land µ are then considered to be variable with seasons, and the paper develops a procedure to work out the probability of the emergence of epidemics.
The working shows that even if infection occurs in a community at a time when it could flourish, if a period of low survival (of the infection) were to follow within a short time, the infection would not be able to spread. Such low survival spells are called “winter” for the pathogen and the effect on outbreaks that occur just before such a spell is called the “winter is coming” effect.
The effect is perceptible, the study says, when the time span over which the environment changes is large, compared to the time the infection lasts, that is the time it takes for a person to get well. “Understanding this effect allows us to identify the optimal deployment of control strategies minimising the average probability of pathogen emergence in seasonal environments,” the paper says.
The paper, which was written before Covid-19 broke, has been tested with the emergence of Zika, which, unlike Covid-19, is spread by mosquitoes, apart from contact. The paper also speaks of timing “pulse vaccination”, to reduce emergence, and measures of using drugs to accelerate recovery.
Such avenues are not available with Covid-19. The only strategic action we can take is to detect seasonal effects on the transmission of SarsCoV-2 and tailor behaviour patterns – like allowing and restricting travel and economic activity.
In the short months that Covid-19 has been with us, it has not been possible, so far, to discern seasonal variation, and the only effect popularly seen is rapid spread. Nevertheless, there is now considerable data, of the prevailing conditions, behavioural as well as climatic, day-to-day and in different parts of the world. Analysis of this data can reveal patterns –ones that could guide administrative strategy, to gain footholds and create the “demographic traps” where the pandemic could be blocked.
These are the same methods – artificial intelligence and predictive analysis – that are routinely used by marketing consultants, and web services like Google, to optimise different objectives. It is clear that the current pandemic will stay for a considerable period – even if a vaccine emerges, it would be many months before a significant number are protected. In 1918, the viral, influenza pandemic infected 500 million people and caused 50 million deaths. The present tally is 16 million cases and over half a million dead.
However, and at a rate of doubling every 30 days, we could reach 500 million by the year-end. The difference is that we now know the virus causing the pandemic and understand its transmission in detail that was unthinkable in 1918. This is the difference that we need to leverage to do significantly better. Reports about the outbreak of 1918 (it started in the United States) contain instances where local administrations permitted unfettered commercial activity. “…the broader pattern that emerged was dismissal, dissemblance and outright deception on the part of public officials who either did not perceive the severity of the threat or who would not acknowledge it, for fear of political consequence.
What mayor or governor, after all, wanted to go to war with local businesses, which in every city vocally opposed forced shutdowns?” says a report about what went wrong in 1918. This time round we need to manage the crisis, not just its consequences.
We know that some trade and business must continue. But we need to put the scientist, statistician and manager in charge, and ensure that the conditions needed to minimise risk are objectively discovered and then implemented. The analysis by counts of Susceptible, Exposed, Infected, Recovered (the SEIR scheme) has been found to be a reliable guide of when to loosen and tighten social controls. We now have sophisticated information gathering and processing tools.
We need to bring those into play, share information with the populace, ensure participation and acceptance. Create an awareness that, for some time, at any rate, self interest lies in thinking of the common good.