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Breaking out of the outbreak April Edition 2020


Introduction Now more than ever the eyes of politicians, healthcare workers, businessmen and the general public are looking up to a small group of professionals to help them in grasping the ongoing pandemics, guide their steps and give the prognosis of how it will develop. We are talking about epidemiologists, professionals in the field of epidemiology, a branch of medicine which deals with the incidence, distribution, and possible control of diseases and other factors relating to health. The aim of the April Edition of Science! Monthly to give you insight into this very interesting field of science and how it helps to overcome a disease outbreak

Josef Kunrt EPSA Science Coordinator 2019/2020

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Using modern technologies to track the disease In an outbreak of any disease, one of the key tasks to help to slow down or even stop its spread is to track the chain of infections all the way to patient zero, the first known infected person. Knowing the person with whom the outbreak and their history, can help researchers to determine how and when the outbreak started. So-called “shoe-leather” epidemiology is not far from the work of police detectives. Epidemiologists are going out of their offices and laboratories to the field, interviewing the infected patients, tracking places they visited and people who they met. Unfortunately, it is not always an easy task. People forget places they have been to, especially when the incubation period is long or even dying sooner than they can be interviewed. Luckily there are other novel approaches to track people’s and the disease’s movement. (1)(2) Pathogens, such as viruses and bacteria vary in complexity, but their foundations are the same - their DNA and/or RNA. The process of nucleic acid replication is not errorless and each error can lead to mutation which is the way of their evolution by natural selection. The lifespan of bacterium or virus is short and they replicate astonishingly fast. That means we can observe evolutionary changes in a very short period of time. These changes can be observed by genome sequencing of the pathogen in different people and comparing how similar or different they are. The idea is that infections with similar sequences will come from the same location at the same time. This way we can determine the origin of the infection. In combination with knowledge about the speed of mutation in the certain pathogen, we can even ascertain the time frame during which the infection was spreading. (3)(4) Epidemiologists can also use the data from what people are searching on the internet. The aim is to comb through billions of search entries and look for small increases in disease-related terms such as symptoms of vaccine availability. This should uncover the incidence of the illness almost in real-time. In 2008 Google launched the Flu Trends algorithm with the aim to monitor the seasonal flu and even predict its onset. Unfortunately, just after a few months after the launch, it completely missed the start of the 2009 N1N1 pandemic. This outbreak proved another weak spot of this approach that even healthy people during active pandemic status are looking for disease-related information, making the algorithm present false results. Although the algorithm was shut down by Google in 2015, it opened a completely new field for epidemiologists how to monitor a disease outbreak. (5) Almost everybody is using social media nowadays. On March 14, 2014, HealthMap, an online database created to collect clues about disease occurrence from various online sources, notified scientists of an article about “strange fever” in Macenta, Guinea. Nine days later, the WHO officially announced an Ebola outbreak in that area. The spread of the outbreak was subsequently monitored through posts on Twitter, containing the term “ebola, or evidence of specific symptoms. Such tweets are flagged by a machinelearning algorithm and added to

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information from other sources such as news articles and official reports and all of it is put into a map using Twitter geolocation feature. This helps track the outbreak and even predict future cases in certain areas. It’s not a perfect way to track an outbreak. The researchers have to vary in choosing the right key-words to track on social media, so they will not be receiving false signals. (6) If you’re not actively using social networks, you still have your phone with you. The epidemiologists can use the data from phones to trace the contacts and movement of people. The principle of digital contact tracking is simple. Phones are constantly logging their own location, be it with GPS or mobile network. When the owner of the phone tests positive for the disease a record of the patient’s movement is shared with health officials. They then inform the owner of any other phones that have been close to the infected patient’s phone. This approach was used in the tracking of the COVID-19 outbreak in South Korea. A major caveat of this approach is the privacy concerns. This kind of tracking could be giving dangerous precedent to future use of such data. (7) As you can see, there are many ways to track an outbreak of a disease. Each approach has its advantages and disadvantages. The true power is in combining different methods to yield optimal results.

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The complexity of vaccine design against emerging viral pathogens Immunisation is among the most effective interventions in modern medicine. The ability to control many viral pathogens by vaccination is doubtlessly one of the biggest achievements of medicine. However, during the past decade, we have seen several viruses, such as MERS, Ebola or H1N1 flu, to come out of shadows and start threatening global health. Each new threat calls for rapid response to halt the spread of the disease in an immunologically naive population especially as other treatment options are limited to non-existent. Classical approaches to vaccine development such as using living-attenuated or dead pathogens might still be used when designing a vaccine for novel infections agents. But more often than not, modern approaches need to be employed. The problem with unknown pathogens is that we don’t know which sub-division of the immune system will provide protection to the pathogen. First of all, scientists need to understand the pathogenesis mechanism, develop appropriate “animal-challenge” models, and be able to screen, test, and generate the proof of concept for new antigens and delivery platforms. Overcoming this obstacle is just the first step in a complex process that continues with the development of a robust and scalable manufacturing process and preparations for clinical trials, which pose just another challenge. During the outbreak, there is not enough time to conduct traditional phase III clinical trials due to ethical considerations and the scale and unpredictable nature of the outbreak. (8) The researchers often employ modern approaches such as genomics. It is the genome sequencing that offers the scientists to obtain information about the pathogen. Once the complete genome sequence is known, the screening for vaccine target molecules can begin. Not only genomics but also other -omics disciplines can be used for research. Functional genomics is used to link genotype to phenotype. Proteomics reveals potential vaccine candidates and can make a basis for in silico models. It also can pinpoint the proteins or epitopes that play a role in interaction with the host’s immune system. (9) The process of developing a vaccine during local or global disease outbreak needs a very high degree of cooperation between different parties, from researchers and manufacturers to logistics and education of healthcare professionals. That is the reason why the Coalition for Epidemic Preparedness Innovations was established in 2017. Its goal is to gather donations from public, private, philanthropic and civil society organisations to finance research projects to develop vaccines against emerging infectious diseases, especially those on World Health Organisation’s “blueprint priority diseases”. (10) As you can see the development of a suitable vaccine for emerging pathogens. But the effort of scientists and use of the latest scientific advances and technologies might not be enough. There is a need for a highly coordinated approach to cover the whole process of development. After all, it is a matter of people’s health.

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When viruses are let loose Animals have been valuable companions for humans since a long time ago. Many people interact with domestic animals in their daily lives as a part of their job or at home. There are even more wild animals in nature that we might come into contact with during travelling through rural areas of our own countries as well as exotic ones. However, animals more often than not carry unknown pathogens (termed zoonoses), be it viruses, bacteria, fungi or protists. In many cases, the pathogens are co-existing with its original host without causing any serious sickness. The problem emerges when such pathogens are introduced to the human population. They might cause an illness ranging from mild inconvenience such as cold to serious and life-threatening conditions like sepsis. It was estimated that more than 6 out of every 10 known infectious diseases in people can be spread from animals, and 3 out of every 4 new or emerging infectious illnesses in people come from animals. (11) Zoonotic spillover is a term for a phenomenon when a pathogen is transmitted from a vertebrate animal to humans. The process of spillover transmission requires several successive steps and the risk of happening this is determined by the link of ecological dynamics of infection in reservoir hosts, the requirements for pathogen survival outside the host, the behaviour of people in situations with a risk of exposure and the susceptibility for infection of recipient host. Most common pathways of spillover are through animal excretion, during animal slaughter and food preparation and through vectors, such as ticks, mosquitoes and fleas. The pathways also determine the dose and route of exposure and thus the probability of infection. (12) Some parts of the world have a higher risk of being the starting point of a disease outbreak. Scientists are trying to predict the places where the spillover might occur and give healthcare organizations in such areas a chance to closely monitor any emerging infection that might get out of hand quickly. The regions with higher human population density and greater wildlife diversity are areas with the highest chance of a disease outbreak of a wildlife origin. Such regions are China and India, tropical countries of Africa and South America, but also some urban areas of Europe and Northern America. (13) One great reason why zoonotic infections are emerging more often in the past several decades is the human environmental impact and climate change. Higher temperatures and rising rainfall are creating an almost perfect environment for activity of pathogens and its vectors. Deforestation is making habitat for many species smaller and sprawling urban areas diminish buffer zones between wildlife and people. Culture and customs are a big driving force for the spread of novel pathogens. Bushmeat hunting, poaching, illegal animal trade, wet markets as well as consumption of exotic and endangered species are also major risk factors in the appearance of previously unknown pathogens. (14)(15)

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The transmission of pathogens to non-original populations is not just from animals to humans but also the other way around. It is known that great apes can contract measles and other paramyxoviruses from human tourists, researchers and poachers. There are several recorded outbreaks of measles in chimpanzees at a research site in Cote d’Ivoire between 1999 and 2006. Other contractible diseases are influenza, herpes simplex virus, methicillin-resistant staphylococcus aureus (MRSA) and mycobacterium tuberculosis. (16)

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Modelling the outbreak The fear of the unknown and uncertain is a deeply rooted part of human nature. Infectious diseases are just that, with their concealed and apparently unpredictable nature. Various diseases have been a source of fear and anxiety since the beginning of human civilization. The less we know about the enemy we are facing the greater the feeling of dread and panic is. We are often looking for ways to lower the uncertainty. One of the main aims of epidemic modelling is to help us understand the spread of disease both in space and time. There are three basic mathematical models used in epidemiology - SIS endemic model, SIR epidemic model and SIR endemic model. SIS (susceptible, infected, susceptible) endemic model is the simplest of the three in which infection does not grant immunity. This model is useful for bacterial infections such as gonorrhoea, meningitis and streptococcal tonsillitis. The SIR (susceptible, infected, recovered) epidemic model is most commonly used for outbreaks happening in a short period of time so the model doesn’t have vital dynamics such as births and deaths. The SIR endemic model is almost the same as the previous one except it does count in births and deaths. The models can be modified with the inclusion of passively-immune groups (newborns protected by mother’s antibodies or people with passive vaccination) and exposed groups of people (in the latent period before being infectious). (17) Mathematical models are inaccurate from a principle as they are often theoretical and only provide an estimate rather than an actual value. Mathematicians often work with estimates of different variables that cannot be measured directly and models assume perfect or almost perfect conditions. Scientists are constantly trying to update the models to bring them closer to real life. Such change is the inclusion of networks into the models. In classical models, it assumed that all people are fully mixed and the probability for spreading the disease is always the same. In reality, diseases are spreading in the population through heterogeneous networks (such as colleagues, schoolmates and religious groups). This makes any two outbreaks, even being caused by the same pathogen, might have different progression and outcomes. (18) The models can be used to test different approaches to outbreak management. A study done by researchers from the University of Stirling focused on modelling different outcomes when social distancing is employed in the management of an outbreak. They have taken into account the economical impacts of the measures as well as individual attitudes of people to follow the regulations. They found out that if social distancing is too weak or based upon inaccurate knowledge the outcomes might be worse than doing nothing. (19) Another group of researchers from Belgium focused on the impact of school closure during an ongoing disease outbreak. They used data from eight European countries to study the real change in the basic reproduction number (i.e. how many persons can one sick person infect) on weekdays versus weekends and during regular versus holiday periods. The results concluded that there is a decrease in the basic

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reproduction by about 21% in the case of week/weekend comparison and 17% in case of regular/holiday period. Such knowledge can be used in future mathematical models to predict the outcomes of different measures better. It is also important to assess how much are the epidemiological models used in public health decision making. A study among public health practitioners found out that around half of respondents use models in their work but only one fifth reported direct communication with those who create the models. Over 50% of the respondents said that an increased frequency of communication between public health practitioners and models creators are needed to reach the full potential as a public health tool. (21) As you can see, epidemic modelling is a big part of disease management and has a big potential to be used as an important tool for stakeholders to make decisions.

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References: 1. https://www.livescience.com/48285-ebola-patient-zero.html 2. https://academic.oup.com/aje/article/172/6/737/99608 3. https://theconversation.com/genetic-detectives-how-scientists-use-dna-to-track-diseaseoutbreaks-57462 4. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5614305/ 5. https://www.nature.com/articles/nature07634 6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4591071/ 7. https://www.sciencemag.org/news/2020/03/cellphone-tracking-could-help-stem-spreadcoronavirus-privacy-price# 8. https://www.sciencedirect.com/science/article/pii/S0264410X19301264?via%3Dihub 9. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2752168/ 10. https://link.springer.com/content/pdf/10.1007/s00103-019-03061-2.pdf 11. https://www.cdc.gov/onehealth/basics/zoonotic-diseases.html 12. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5791534/ 13. https://www.nature.com/articles/s41467-017-00923-8 14. https://www.acmicrob.com/microbiology/the-impact-of-climate-change-and-otherfactors-on-zoonotic-diseases.php?aid=220 15. https://wwwnc.cdc.gov/eid/article/11/12/04-0789_article 16. https://onlinelibrary.wiley.com/doi/epdf/10.1002/msj.20140 17. http://pdfs.semanticscholar.org/9e78/0bf10519e512c0975325a93d5d0c8211a56e.pdf 18. https://www.sciencedirect.com/science/article/pii/S0022519304003510 19. https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-12-679#Abs1 20. https://bmcinfectdis.biomedcentral.com/articles/10.1186/1471-2334-9-187 21. https://www.nature.com/articles/s41598-018-30378-w

Picture sources: 1. https://img.jakpost.net/c/2020/03/12/2020_03_12_89013_1583994402._large.jpg 2. https://upload.wikimedia.org/wikipedia/commons/f/fd/Crowd_of_people_with_phones.jpg 3. https://upload.wikimedia.org/wikipedia/commons/c/cc/Scientists_Model_Immune_Variatio n_and_Responses_to_Flu_Vaccination_%2814165434229%29.jpg 4. https://upload.wikimedia.org/wikipedia/commons/8/89/Wet_market_in_Singapore.jpg 5. https://upload.wikimedia.org/wikipedia/commons/8/89/Pure-mathematicsformul%C3%A6-blackboard.jpg

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Further reading 1. Detecting the emergence of novel, zoonotic viruses pathogenic to humans The potential for emerging zoonotic viral pathogens to cause a large scale outbreak is undoubtedly high. It is absolutely crucial to detect and monitor any previously unknown pathogen and predicts its level of threat to people. This article focuses on the assessment of how the monitoring is done and what are the weak points of current practice. Source

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4629502/

2. Mathematical Modeling of Infectious Diseases Dynamics Mathematical modelling is a complex area of research, but it is also a very interesting one. If you are interested in different aspects of modelling the following article will give you insight into it. Source

https://www.mivegec.ird.fr/images/stories/PDF_files/0507.pdf

3. Modelling epidemics: the math behind disease outbreaks More accessible article about mathematical modelling in epidemiology with a focus on the use of new technologies in this field. Source:

https://www.elsevier.com/life-sciences/journals/modelling-epidemics-the-maths-behinddisease-outbreaks

4. Choices in vaccine trial design in epidemics of emerging infections As mentioned in the main Science! Monthly article, the design of clinical trials for the testing of vaccines. This article will give you insight into different possible trial designs that can be used during ongoing epidemics. Sources:

https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1002632&type=pri ntable

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Science plays a very important part of the education of pharmaceutical students. It represents one of the main aspects of pharmaceutical education. Thing is, there are many aspects of pharmacy and pharmaceutical sciences, we as EPSA, want to enlighten our students with. Want to know more about them? In that case, visit LLeaP – Lifelong Learning Platform and be in charge of your education! All you need to do is REGISTER and start creating your own lifelong learning journey by filling this submission form and winning your Science Monthly Medal! Further, many interesting activities and medals are coming up this year!

So stay tuned and take a LLeaP of faith!

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EPSA Science! Monthly April Edition 2020  

EPSA Science! Monthly April Edition 2020  

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