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The worldwide coronavirus pandemic has raised questions about a phenomenon known as ‘herd immunity’ and raised hopes that it could help slow or end the outbreak. Re-immunity occurs when a large part of a community – the herd – develops some degree of immunity to a virus, making the spread of a disease from person to person less likely. As a result, the entire community receives protection, not just those who are immune.
Evidence to date collected by epidemiologists suggests that a person infected with COVID-19 transmits the virus to on average between two and three others. Here’s what infection looks like in seven generations, and how immunity can disrupt the chain.
There are two pathways to herd immunity – natural infections and vaccines. It is not yet clear whether natural infections alone will lead to widespread immunity; While most people who have COVID-19 have developed antibodies, it is too early to know how much protection they offer and for how long. A vaccine can deliver immunity faster and more reliably.
The simulations below use a mathematical model to illustrate how the virus can spread at different rates across the same community, depending on levels of vaccination. The Reuters model reflects projections by experts that the COVID-19 vaccines currently under development will be around 70% effective, meaning that they will protect 70% of vaccinated people against infection by the virus or from malignancies. disease when they become infected.
Each simulation consists of a community of 9,000 people, with individuals represented by fields. Each community has a different number vaccinated individuals, before the same number infection individuals is introduced.
The simulations use important epidemiological parameters, such as the period that a person is infected, to demonstrate how the virus can spread. In each scenario, infected people have “contact” with other fields, which may or may not be infected. Each frame of the animation consists of a day.
There are some limitations to modeling real-life interactions. For example, the model assumes that each person will have the same number of contacts, but in reality some people are more social than others, which would affect how likely they are to spread the disease. There is also much that is still unknown about some of the epidemiological parameters of COVID-19, but the simulations include current knowledge and provide an honest comparison across the various vaccination scenarios.
Experts believe that if no other measures are taken, immunity to herds can come in if between 50% and 70% of a population gets immunity through vaccination.
“It depends on whether the vaccine is 100% effective, but the number that most people see (for herd immunity) is around 60-65%,” said Catherine Bennett, chair of Epidemiology at Deakin University’s Faculty of Public Health. of Melbourne.
To obtain a robust indication when the threshold immunity threshold is reached, Reuters conducted 1,000 random simulations over each vaccination scenario. Here is what this series of simulations shows.
Infections subside
dramatic
somewhere here
Results of
thousands
simulations
Infections subside
dramatic
somewhere here
Results of thousands
of simulations
Infections subside
dramatic
somewhere here
Results of
thousands
simulations
Balancing fax distribution
The way a vaccine is distributed has implications for its effectiveness. If the uneven is shared in a community, for example if people in affluent areas have greater access than those in poorer locations, then that creates safe clusters but leaves large areas of sensitive people.
“We need to make sure we distribute the vaccine evenly across the population,” said Joel Miller, a senior lecturer in applied mathematics at La Trobe University in Melbourne, who uses mathematical models to help government and non-governmental -profit organization to create policy for the control of infection.
In the early stages of spreading a new vaccine, higher priority may be given to healthcare professionals and other people at the forefront, than those considered the most vulnerable, a process known as ‘target vaccination’. Miller said it is essential that people who can be considered superspreaders, such as public transport employees, also receive the vaccine quickly.
“An ideal vaccination campaign will ensure that the vaccine goes to the groups at highest risk, but also to those most responsible for spreading infection,” Miller said.
Movement restrictions
The movement of humans also has implications for the spread of a virus. The basic Reuters model works with the assumption that a small percentage of people travel to communities outside their immediate environment.
In the graph below, we compare two populations: the first group has people who mix and travel widely and the second group has relatively static citizens. The difference in the speed of spread of a virus is clear.
At lower vaccination levels, the number of people infected ends up in both groups, but the spread is much slower in the static population, which keeps the number of cases at a manageable level for hospitals and healthcare providers. That scenario is reflected in the exclusionary measures and travel restrictions imposed by many countries in the coronavirus pandemic to try to ‘flatten the curve.’
If one in four people travel
0 the day
Even if a high percentage of the population is vaccinated, the number of infected people can be further reduced if people do not travel.
If one in four people travel
0% infection
If no one is traveling
0% infection
Vaccination vs natural infection
Re-immunity can also be achieved when a large number of people have contracted a disease and are recovering. However, the jury is still out on what kind of protection a natural infection with this new coronavirus offers, and surely more people would die waiting for the linking effect than if a vaccine had been produced.
“The risk is unacceptable,” Bennett said. “We can not allow ourselves to infect humans to achieve herd immunity if we know so little about the long-term effects.”
While we wait
There is currently no vaccine for COVID-19, although studies are underway at various stages around the world. It usually takes several years for a vaccine to identify, test, produce, and eventually reach the consumer market. Fax manufacturers hope to dramatically compress this timeline for COVID-19 through rapid trials and by scale production even before the products have proven successful.
In the meantime, social distance, wearing masks, hand hygiene and other interventions can reduce transmissions and contribute to creating the coupling effect. The new coronavirus is primarily spread via droplets expelled when a person coughs or sneezes, and aerosol particles expelled as we speak.
The World Health Organization (WHO) says that masks can be used as part of a comprehensive package of prevention and control measures that limit the spread of respiratory viral diseases. A study commissioned by the WHO and published in the Lancet in June said that wearing a mask “may result in a significant reduction in the risk of infection” of COVID-19.
If we apply the use of masks and / or social distance to our previous model, without immunity, a slower spread can be seen. The point at which herd immunity kicks in can also be reduced.
With masks
Distance between
some contacts
Masks and distance
The overall goal is to reduce the effective reproduction level of the virus to less than 1, at which point an outbreak would be worse if infected people transmitted the virus on average to less than one other person.
Epidemiologists largely agree that a combination of vaccination and ongoing social responsibility measures such as physical distance provide the strongest chance of achieving that goal. The combined approach is critical, given early faxes to the market probably did not bring 100% effectiveness.
“It’s about adding layers,” Bennett said. “It gives us extra protection against the spread of the community. The situation is much better in places where a combination of measures is used. ”
The model
Use the sliders to enter your own parameters in the Reuters model and see a simulation of the spread.
Methodology
The simulations begin by creating a predetermined number of fields. Each square represents a person. It initializes the population with 5 infected persons and a percentage of vaccinated persons. Both the infected and the vaccinated were randomly selected. A percentage of the vaccinated persons (100% – effectiveness of vaccines) is randomly selected and marked sensitive. To explain: If the efficiency of the vaccine is 70%, 30% of the vaccinated squares will be marked as sensitive.
For the purpose of this illustrative simulation, each infected cell has “contact” with 8 immediate neighbors. A fraction of randomly selected cells also has an additional ‘long-distance neighbor’. Such a consistent number of contacts may be unrealistic, but this provides a fair comparison across different fax scenarios.
These neighbors can be infected, vaccinated, or susceptible.
The simulation runs for many “frames”. Each frame represents one day.
Initial initial parameters are: R0 = 2.5, days of infection = 7, transfer calculated using SIR formula.
The average contact percentage used in the formula is 8, because each infected cell has 8 neighbors.
Every day, the simulation runs for each infected cell. The infected cell is made to make contact with each of its 8 neighbors with the following conditions:
- if the neighbor has been vaccinated or is already infected, continue
- if the neighbor is infected, continue
- if the neighbor is sensitive, generate a random chance and check if it falls into transferability. If it does, infect the neighbor. The “generation” of this neighbor is = generation source + 1
A cell remains infected for the parameter “Days of infectiousness” above = 7 days, if 7 “frames” of the simulation. After 7 days, the cell is moved to the “removed” category.
Notes
The graph, which simulates the impact of wearing masks, uses a 44% risk reduction figure as indicated in a study commissioned by the WHO and published in the Lancet in June.
Sources
Reuters calculations; Dr Joel Miller, La Trobe University, Melbourne
By Manas Sharma and Simon Scarr
Written by Jane Wardell
Additional report by Christine Soares
Edited by Tiffany Wu
