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The Corona Research Super Project has been completed after four months of hard work by all students and staff involved. All of the teams within the project have researched and designed together and many great findings and scientific contributions have been developed. While some groups are still pursuing scientific publication or further research into their topics, preparations have started for the organisation of a symposium on September 21st. This symposium will be an opportunity for the students and staff involved in the CRSP as well as for all others that are interested in the topics discussed during the project or in the novel and innovative ways of education that have developed under our noses.

The result, at least on paper, of the CRSP is one paper produced by each of the 10 research teams. Although each paper can be read separately, they form a larger narrative throughout the project. Some teams focussed on describing and researching the currently available means of diagnosing, treating and vaccinating against COVID-19. These teams based their research methods mostly on literature research. Other teams used extensive statistical mathematical modelling techniques to model and predict the progression of SARS-CoV-2 through societies or within a single patient. Lastly some teams used a design-based approach to describe and develop tools to better understand and implement preventive regulations and their effectiveness in society. 

All of these teams interacted with one another and learned together, this makes the core of the Corona Research Super Project the concept of Collaborative learning. 

After completion of the research done by each team, papers were written. Abstracts of all these papers can be found below. Full papers can be requested by contacting the CRSP team through

Diagram collaboratief leren (CRSP)

Below are the results yielded by literature research.

By: Hannah Hulsewé, Polina Kostina, Flip Jansen, Lucas Hofmans, Anouk Huijsmans & Rochelle Niemeijer
Supervision: Aljoscha Wahl1 & Andrea Conidi (Erasmus MC)

The pandemic in 2019 caused by the zoonosis of SARS-CoV-2 showed that the world is currently insufficiently prepared for properly containing outbreaks, highlighting a need for rapid antiviral discovery, Recent advancements in the fields of proteomics, metabolomics and metagenomics among others have enabled more precise measurements that allow the structures and dynamics of the viral life cycle to be mapped in much greater detail. During this developmental period, new antiviral targets have been identified. In order to establish attractive potential antiviral targets for SARS-CoV-2, the interference points in the viral life cycle of the pathologically relevant Influenza A virus (IAV) have been reviewed. This review provides in-depth insight into the models of entry, replication, release and influence on the host cell metabolic pathway of SARS-CoV-2 and IAV. This review of different points in the viral life cycle has elucidated points of interference and corresponding potential antivirals. Further research into viral life cycle interference points is a crucial part of a structural framework enabling more rapid antiviral discovery. As the given viruses have similarities, the knowledge of IAV can contribute to the rapid development of antivirals for SARS-CoV-2. This research emphasizes the importance of experiences gained by extensive research on IAV. This allows the development of a roadmap towards more rapid antiviral discovery with applications for future viral outbreaks.

By: Ussama Choudhry, Berber Keane, Kirsty Kwok, Selma Oueddan & Aaron Wenteler.
Supervision: Inês Machado (Erasmus MC)

A novel coronavirus, SARS-CoV-2, probably of zoonotic origin, was first detected from a cluster of pneumonia cases in Wuhan, China in December 2019. SARS-CoV-2 has spread worldwide quickly in early 2020 causing a pandemic with over 9 million confirmed cases and more than 470,000 deaths at the time of writing. A vaccine is crucial for containing the further spread of SARS-CoV-2 and preventing future outbreaks. In this review, we look at the current advances in the research and development of vaccine candidates against SARS-CoV-2. We discuss the target of the vaccines, how several vaccine techniques work and at last vaccine candidates currently in clinical trials.

By: Staf Bauer, Giovi Duivenvoorden, Gerarda van de Kamp, Kavish Kohabir & Ngoc Mai Phuong Nguyen
Supervision: Willy Baarends (Erasmus MC)

The SARS-CoV-2 outbreak and spread in the beginning of 2020 led to a rapid onset of a COVID-19 pandemic. Proper identification and quarantining of infected individuals is necessary to contain viral spreading. Hence, there is a need for quick, sensitive and specific diagnostic methods that preferably identify patients in an early stage of the disease. This review explores different diagnostic methods and aims to advocate which method suits best for diagnosis of which stages of COVID-19 and for what purpose. We state that diagnosing COVID-19 based on symptoms remains challenging, as most symptoms of COVID-19 are not specific and the specific ones only occur in late stages of the disease. Therefore, to detect infection in an early stage, a molecular diagnostic method that has a high specificity and sensitivity, a short turnaround time, low costs and enables “point-of-care testing” (POCT), should be used. Compared to antigen detection, viral RNA detection has been demonstrated to be more sensitive, with a larger detection window, allowing for early stage diagnosis. The current golden standard detection by RT-qPCR is suboptimal, but at this stage the best established method available. Isothermal alternatives including RT-LAMP, RT-RPA and RT-RAMP deserve development efforts to allow for quicker, cheaper, more sensitive and POCT-friendly testing procedures in possible future pandemics. Likewise, a comparison of serological assays for herd immunity monitoring at late stages, or after recovery from COVID-19, suggests lateral flow assays deserve efforts to increase testing sensitivity.

Below are the results yielded by research based on mathematical models.

By: Yasha Tenhagen, Mark Olthof, Sten de Schrijver, Selin Kocer, Rahman Fakhry & Marijn den Hartog
Supervision: Andrea Conidi1 & David van de Vijver (Erasmus MC)

Background The COVID-19 pandemic has had a tremendous effect worldwide. The world’s nations typically take countermeasures based on deterministic models. Stochastic modelling may be more suitable for taking into account the inherent randomness of the disease. This study tries to determine if stochastic and deterministic models are similar and tries to make predictions about the development of COVID-19 in Lombardy and the Netherlands.
Method We use an SEIR model, to which an additional hospitalized (H) compartment and two deceased (Dc and Du) compartments have been added, to estimate the spread of COVID-19. We calculate the parameters for this model by using data from two different datasets. We utilized a dataset managed by the Italian Civil Protection department (DPC) for Lombardy and a dataset from Rijksinstituut voor Volksgezondheid en Milieu (RIVM) for the Netherlands. In order to estimate these parameters, we adopted a Monte Carlo Markov Chain (MCMC) method. In order to simulate the lockdown and the release of measures, we also let the contact rate vary over time using a sigmoidal function. After the parameter estimations, we implement the results into a deterministic, as well as a stochastic model and compare the results.
Results The data shows a peak of people infected with COVID-19 by the end of March, with 13328 patients hospitalized at its maximum. In our simulations, the peak in hospitalizations occurs around the same moment as happened in reality. The height of the peak is similar as well, with 13838 hospitalized patients. However, the number of patients in the hospital does seem to die down a bit earlier than is really the case. Our results also show a smaller curve of infected patients than the curve of the exposed patients. Furthermore, the number of recovered patients is larger by the end of June compared to the real data. In our model, no second wave would occur if the contact rate stays low. We also find similar courses of the epidemic in the aforementioned results proposed by the deterministic and the stochastic models.
Conclusion Stochastic models are more suitable than deterministic models for predicting the course COVID-19. Deterministic models only propose one certain course, whereas stochastic models propose a similar course but paired with ‘error bands’. This extended information helps us take into account the heterogeneity of the future course of the epidemic, thereby aiding organisations in the prevention of and preparation for possible future outbreaks.

By: Manon Vleeming, Anna le Clercq, Mana Nejabat & Sabine Haspels
Supervision: Aljoscha Wahl (Erasmus MC)

The novel coronavirus (SARS-CoV-2) causing coronavirus disease (COVID-19) has led to a global pandemic with more than 9 million confirmed cases as of June 25th 2020. Understanding the transmission dynamics and assessing the effects of control strategies is essential in order to reduce transmission in case of a second wave. In this paper, through simulation with an extended SIR model, we test the effects of different mitigation strategies on the course of the epidemic. The effects of a mild or heavy lockdown at different time points in the epidemic and increased testing are examined using a SIADR (susceptible, infectious, ailing, diagnosed, recovered) model. It became clear that inducing a heavy lockdown at an early time point together with widespread testing is the most effective measure to decrease the peak value of diagnosed individuals.

By: Wieke Joustra, Disha Vadgama, Ward de Ridder, Ward Peeters, Enzo Kingma & Friso Douma.
Supervision: David van de Vijver1 & Rob Gruters (Erasmus MC)

Vaccines for the SARS-Cov-2 virus causing the 2020 pandemic are being developed by many research groups in the world. When this vaccine is finally found, chances are high that it would not be possible to vaccinate the whole population at once and with that the question arises; how will we divide these vaccines among the different inhabitants of the Netherlands? One might say that random vaccination works well enough, but perhaps a vaccination strategy targeted to a certain demographic has a higher impact. This research aims to find and formulate the optimal vaccination strategy, with as principal goal to minimize the total mortality. To obtain this, a mathematical model is used that is an extended version of a standard SIR-model. In this model, a vaccine is applied, assuming the vaccine to be available one year after the start of the simulation, for a 50%, 70% or 90% efficacy of the vaccine, vaccinating 10%, 30% or 50% of the population. In this simulation, targeted vaccination strategies that were based on 4 demographics in the Netherlands were used; young people, middle aged people, old people and people working in healthcare. We find that for each vaccine efficiency and for each amount of available vaccine, the strategy of vaccinating the elderly is most effective. When looking at the effect on herd immunity and total mortality in younger demographics, we find that vaccinating the elderly is still preferable. Although our approach on determining an optimal vaccination strategy is not infallible, it opens up a discussion in society on how we should approach the vaccination process, should a vaccine become available.

By: Anneloet Broerze, Lukas van den Heuvel & Enzo Nio
Supervision: Dimphna Meijer2 & Kasper Spoelstra (TU Delft)

For the development of treatments against COVID-19, a quantitative description of a SARS-CoV-2 infection inside a patient is essential. Here, we combine a simple model based on quasispecies theorem with a bioinformatics-based fitness landscape specific to SARS-CoV-2. The model, which describes viral load and evolution inside a host, is solved stochastically with the Gillespie algorithm. We propose that changing the model’s parameters for mutation, infection, replication and clearance rate leads to conclusions about the effectiveness of antivirals against the disease. Although there is much room for improvement, the model quantitatively describes the influence of four classes of antivirals and gives insight in the SARS-CoV-2 evolutionary dynamics.

Below are the results yielded by design research.

By: Jorrit Bakker, Reda Rhellab, Georgiana Spatariu & Hanna Vermeer
Supervision: Damiano La Zara (TU Delft)

In this project, we examine the strategies implemented worldwide in order to contain the spread of the SARS-CoV-2 virus emerged in late 2019. First, we suggest a new parameter to evaluate the effectiveness of countries’ measures, that is the number of deaths over population density. Next, we provide an overview of short-term and long-term measures adopted by different countries and their efficacy. In the early phase of an epidemic, taking swift action is key. Based on our findings we determine that countries which reached a stringency index - a lockdown index which weighs various mitigation measures - of 72 at <0.3 deaths per people/km2 ended up having fewer deaths per people/km2. While lockdowns have been proven as an effective measure in containing the spread of the virus, the economic and social consequences can be significant. Widespread testing and contact tracing, which turned out to be effective in several countries such as South Korea and China, have less economic consequences, and are therefore crucial strategies to pursue in the long run. In countries with limited testing capacity such as developing countries, other measures such as physical distancing should be widely employed. In addition, wearing face masks is also effective in reducing viral transmission, as respiratory droplets containing the virus are trapped and prevented to spread. Lastly, clear public health communication is of great importance, as it influences the compliance to government measures, thereby affecting the course of the outbreak.

By: Thijn Hoekstra, Emmy Thans, Daniël Sommers, Janneke Bok, Laurien Westra & Nima Nejabat
Supervision: Maarten van der Sanden

Communities worldwide face the difficult task of making well- informed decisions on how to approach the SARS- CoV- 2 pandemic. We have identified two categories of policy types seen during this pandemic: "Outbreak Management" and "Impact Management". We have also devised a metaphor we can use to discuss virus- management strategies.
Using this language, we conceptualized a tool (e.g. a mobile app) that could be used by communities (e.g. nations) to master "Outbreak- " and "Impact Management" through play. This design process was facilitated by making use of frameworks such as the "Quadruple Helix" and "Octalysis", but also through extensive use of images, drawings and prototypes.

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