Publications
Peer-reviewed articles, preprints and book chapters, newest first. Click a title for the publisher's page, or expand the abstract. *equal contribution · †corresponding author. Also on Google Scholar and ORCID.
Journal articles
Mechanics of pandemics
Seba Contreras†, Philipp Dönges, Laura Müller, Piklu Mallick, Sydney Paltra, Ulrik Hvid, Robyn Kettlitz, Andreas Reitenbach, Rodrigo Amaral Lind, Maíra Aguiar, P. Bechtle, André Calero Valdez, Ronja Gronemeyer, Manuela Harries, Veronika K. Jaeger, André Karch, Carolina Judith Klett-Tammen, Peter Klimek, Mirjam E. Kretzschmar, Kai Nagel, Bjarke Frost Nielsen, Barbara Prainsack, Isabella M. Radhuber, Lone Simonsen, Kim Sneppen, Janik Suer, Viola Priesemann†
Abstract
COVID-19 and previous pandemics have shown how diseases can disrupt, threaten, and transform daily life. Since pathogens and societies are continuously evolving, every pandemic is different. However, certain fundamental principles of disease transmission appear to hold true across different outbreaks. These "mechanisms" are grounded in natural laws or the very structure of our biology and societies. This paper compiles ten fundamental mechanisms, curated by a multidisciplinary team with backgrounds spanning public health, medicine, epidemiology, political science, mathematics, physics, and psychology. These mechanisms, although perhaps underappreciated, substantially shape how pandemics unfold and are controlled. The better we succeed in understanding these mechanisms and establishing this knowledge in our societies, the better we will be able to prepare for future pandemics and respond appropriately when they occur.
Impact of Non‐Pharmaceutical Interventions Targeted at COVID‐19 Pandemic on Influenza Burden—A Systematic Review and Meta‐Analysis
Laura-Inés Böhler, Lisa Köppel, Stefan Weber, Mary Gaeddert, Katharina Thielemann, Kerstin Glaser, Horeya M. Ismail, Ulrich Reinacher, Veronika K. Jaeger, Julia Böhnke, Antonia Bartz, Maja Pavić, Manuela Harries, Christina Kuczewski, Torben Heinsohn, Olga Hovardovska, Sin‐Yin Huei, Chao Xu, Cornelia Gottschick, Seba Contreras, Maciej Filiński, Maurizio Grilli, Berit Lange, RESPINOW Study Group, Claudia M. Denkinger
Abstract
Seasonal influenza imposes a substantial global health burden. The COVID-19 pandemic, accompanied by widespread non-pharmaceutical interventions (NPIs), profoundly disrupted respiratory virus circulation, offering a unique opportunity to assess their broader epidemiological impact. This study aimed to quantify changes in influenza burden between the pre-pandemic and intra-pandemic periods. Within the RESPINOW project, we conducted a systematic review following PRISMA guidelines. Studies reporting absolute influenza case counts before and during the pandemic were included. Data extraction and quality assessment were performed using a modified NHLBI tool for before-after studies. Influenza cases were normalized by reporting period length, and relative changes were estimated using incidence rate ratios. Subgroup analyses explored age, setting, hemisphere, human development index, influenza transmission zones, WHO regions, and viral strains. Of 20,676 screened records, 115 studies from 98 countries were included. Globally, influenza incidence declined by -92% (95% CI: -94 to -90) following the onset of the pandemic. Reductions varied geographically, ranging from near-elimination in several countries to more modest declines (e.g., South Korea: -61%, 95% CI: -79 to -26). Decreases were observed across all transmission zones and WHO regions, with descriptively larger reductions observed in high-income countries (-96%, 95% CI: -98 to -94) than in low-income settings (-82%, 95% CI: -88 to -73). Age-specific declines appeared smaller among young children (< 6 years: -66%, 95% CI: -79 to -45) compared with adults aged 18-64 years (-80%, 95% CI: -90 to -63). Influenza A appeared to decline more strongly than influenza B. The COVID-19 pandemic was associated with an unprecedented global reduction in influenza incidence. Data limitations and the need for robust epidemiological indicators highlight the importance of strengthened, integrated global surveillance to inform future pandemic responses.
Stability and bifurcations of a minimal model for the effect of PrEP-related risk compensation in epidemics of sexually transmitted infections
Piklu Mallick, Laura Müller, Antonio B. Marín-Carballo, Philipp Dönges, Seba Contreras†
Abstract
HIV pre-exposure prophylaxis (PrEP) drastically reduces the risk of HIV infection if taken as prescribed, providing almost perfect protection even during unprotected sexual intercourse. Although this has been transformative in reducing new HIV infections among high-risk populations, it has also been linked to an increase in risk practices – a phenomenon known as risk compensation – thereby favoring the spread of other sexually transmitted infections (STIs) deemed less severe. In this paper, we study a minimal compartmental model describing the effect of risk awareness and risk compensation due to PrEP on the spread of other STIs among a high-infection-risk group of men who have sex with men (MSM). The model integrates three key elements of risk-mediated behavior and PrEP programs: (i) HIV risk awareness drives self-protective behaviors (such as condom use and voluntary STI screening); (ii) individuals on PrEP are subject to risk compensation, but (iii) are required to screen for asymptomatic STIs frequently. We derived the basic reproduction number of the system, $R_0$, and found a transcritical bifurcation at $R_0=1$, where the disease-free equilibrium becomes unstable and an endemic equilibrium emerges. This endemic equilibrium is asymptotically stable wherever it exists. We identified critical thresholds in behavioral and policy parameters that separate these regimes and analyzed typical values for plausible parameter choices. Beyond the specific epidemiological context, the model serves as a general framework for studying nonlinear interactions between behavioral adaptation, preventive interventions, and disease dynamics, providing insights into how feedback mechanisms can lead to non-trivial responses in epidemic systems. Finally, our model can be easily extended to study the effect of interventions and risk compensation in other STIs.
Testing paradox may explain increased observed prevalence of bacterial STIs among MSM on HIV PrEP: A modeling study
Laura Müller, Piklu Mallick, Antonio B. Marín-Carballo, Philipp Dönges, Robyn Kettlitz, Carolina Judith Klett-Tammen, Mirjam Kretzschmar, Viola Priesemann, Seba Contreras†
Abstract
HIV pre-exposure prophylaxis (PrEP) is transforming global HIV prevention, but its implementation coincides with observations of rising bacterial sexually transmitted infection (STI) rates among men who have sex with men, raising questions about whether PrEP is preventing one epidemic while facilitating others. To reconcile this apparent contradiction, we developed a minimal dynamical model of the simultaneous transmission of HIV and chlamydia (as an example of a curable STI). The model integrates three key mechanisms: 1) risk-mediated self-protective behavior, 2) reduction in condom use among PrEP users, and 3) PrEP-related asymptomatic STI screening. We show that these mechanisms can generate a "testing paradox:" True STI prevalence may decline while observed trends rise. This paradox emerges because increased PrEP uptake amplifies screening intensity, which can lower transmission but simultaneously inflate detection. By systematically mapping the parameter space of PrEP uptake, screening frequency, and risk perception, we identify broad and plausible conditions under which the paradox arises. Our findings reconcile conflicting epidemiological evidence and remark that the net effect of PrEP on STI dynamics depends critically on asymptomatic screening strategies. These results highlight the potential dual role of PrEP programs in reducing both HIV and bacterial STI incidence, while emphasizing the need to align screening and treatment policies to maximize benefits and minimize risks, e.g., antimicrobial resistance.
Self-reported poliomyelitis vaccination and documentation in adults indicates high uptake: a digital German epidemic panel, December 2024
Robyn Kettlitz, Manuela Harries, Seba Contreras, Jannik Reinecke, Maren Sophia Wieder, Thomas von Lengerke, Stefanie Castell, Berit Lange, Carolina Judith Klett-Tammen, PCR-4-ALL study group, Xavier Casadevall i Solvas, Emine Kayahan, Mariam Mohamed Abdelsatta Bayoumi, Gregor Fritz, Zhiyuan Ma, Jeroen Lammertyn, Dragana Spasic, Lorenz Van Hileghem, Robin De Groote, Javier Martinez-Picado, Elisabet Fernández-Rosas, Sara Morón-López, Maria C. Puertas, Maria C. Garcia-Guerrero, Catia Nicodemo, Alessandro Bucciol, Stefano Landi, Chiara Leardini, Giulia Montresor, Khalidwa Shomali, Isti Rodiah, Felix Jenniches Helmholtz, Daniel Alexander Schulze, Vanessa Melhorn, MuSPAD study group, Monika Strengert, Alex Dulovic, Nicole Schneiderhan-Marra, Jana-Kristin Heise, Gérard Krause, Pilar Hernandez, Daniela Gornyk, Monike Schlüter, Tobias Kerrinnes, Gerhard Bojara, Kerstin Frank, Knut Gubbe, Torsten Tonn, Oliver Kappert, Winfried V. Kern, Thomas Illig, Norman Klopp, Gottfried Roller, Michael Ziemons
Abstract
BACKGROUND: On 12 December 2024, the Standing Committee on Vaccination (STIKO) recommended universal polio catch-up vaccination for children and adolescents up to 16, urging parents to check their children's immunization status following detections of vaccine-derived poliovirus in wastewater. The Robert Koch Institute (RKI) also advised healthcare professionals to ensure vaccination coverage in priority groups. Regional health authorities, called on all citizens to review their vaccination records to address any immunization gaps. We investigated vaccine uptake (documented / recalled) to improve estimates of immunity against poliovirus among the German population and gain insights into the proportion of undocumented vaccines. METHODS: We conducted a survey in December 2024 using the eResearch System PIA (Prospective Monitoring and Management-App) to collect data on self-reported vaccine uptake among a German cohort. We calculated the frequency of vaccinations that were documented and undocumented, as well as the types of vaccines and the number of doses received. Vaccination status was classified as received ≤ 2 doses versus ≥ 3 doses of any polio-containing vaccine. We applied survey weights to calculate frequencies according the general German population (by age, sex, region) and logistic regression to examine the relationships between the vaccinations that were not documented but recalled, and the factors associated with these undocumented vaccinations. RESULTS: Among 1,124 participants who completed the survey on vaccination uptake, 1,097 (96.9%) participants stated to have a vaccination record. A total of 823/1,124 (74.3%) reported having a vaccination record, where at least one poliomyelitis vaccine was documented, whereas 233 (19.0%) participants recalled at least one poliomyelitis vaccination without documentation or vaccination record. Of 1,124, 68 participants (6.7%) did not report any polio vaccination neither documented nor recalled without documentation. Among the 823 participants with documented vaccination and at least one vaccination, 592 (75.1%) received at least three doses of a poliomyelitis vaccine, with a decline in older age groups, less than three doses were reported by 164 (17.6%), and the remaining 7.3% (n = 67) did not have information on the number of doses administered. Of 2,768 documented vaccine doses, 898 (29.9%) were oral poliovirus vaccines (OPV) and 704 (26.2%) were inactivated poliovirus vaccines (IPV). In 1,166 vaccines (43.9%), the type could not be derived by the participants from the vaccination record. The odds of having a recalled vaccination (not documented) was higher in male and the older age groups compared to females and younger participants. DISCUSSION: We found similar poliomyelitis vaccination uptake compared to other data sources e.g., of the Robert Koch Institute (RKI). Vaccine-derived immunity to poliomyelitis may be underestimated based on vaccination records only. There is a need to address potential gaps in health literacy and vaccination records. Efforts should be made to conduct continuous seroprevalence surveys in the population in response to emerging public health threats and deduce parameters to inform modelling infection dynamics in specific outbreak scenarios. TRIAL REGISTRATION: The PCR-4-ALL cohort was registered in the German Clinical Trials Register on the 3rd of September 2024 (DRKS00034763).
Risk-mediated dynamic regulation of effective contacts de-synchronizes outbreaks in metapopulation epidemic models
Henrik Zunker, Philipp Dönges, Patrick Lenz, Seba Contreras†, Martin J. Kühn†
Abstract
Metapopulation epidemic models help capture the spatial dimension of infectious disease spread by dividing heterogeneous populations into separate but interconnected communities, represented by nodes in a network. In the event of an epidemic, an important research question is, to what degree is the spatial information (i.e., regional or national) relevant for mitigation and (local) policymakers? This study investigates the impact of different levels of information on nationwide epidemic outcomes, modeling the reaction to the measured hazard as a feedback loop reducing contact rates in a metapopulation model based on ordinary differential equations (ODEs). Using COVID-19 and high-resolution mobility data for Germany of 2020 as a case study, our model revealed two markedly different regimes depending on the maximum contact reduction. In the first regime of (modest) mitigation , gradually increasing maximum contact reduction from zero to moderate levels delayed and spread out the onset of infection waves while gradually reducing the peak values. This effect was more pronounced when the contribution of regional information was low relative to national data. In the opposite suppression regime, the feedback-induced contact reduction is strong enough to extinguish local outbreaks and decrease the mean and variance of the peak day distribution, thus regional information was more important. When suppression or elimination is impossible, ensuring local epidemics are desynchronized helps to avoid hospitalization or intensive care bottlenecks by reallocating resources from less-affected areas. • Metapopulation model with dynamic contact regulation on different spatial scales. • Dynamic contact regulation yields two regimes: disease mitigation and suppression. • Modest regulation (mitigation) desynchronizes and delays the onset of epidemic waves. • High regulation (suppression) extinguishes local outbreaks but synchronizes them. • Mitigation benefits from regional data; suppression, from national data.
Societal self-regulation induces complex infection dynamics and chaos
Joel Wagner, Simon Bauer, Seba Contreras, Luk Fleddermann, Ulrich Parlitz, Viola Priesemann
Abstract
Classically, endemic infectious diseases are expected to display relatively stable, predictable infection dynamics. Accordingly, basic disease models such as the susceptible-infected-recovered-susceptible model display stable endemic states or recurrent seasonal waves. However, if the human population reacts to high infection numbers by mitigating the spread of the disease, then this delayed behavioral feedback loop can generate infection waves itself, driven by periodic mitigation and subsequent relaxation. We show that such behavioral reactions, together with a seasonal effect of comparable impact, can cause complex and unpredictable infection dynamics, including Arnold tongues, coexisting attractors, and chaos. Importantly, these arise in epidemiologically relevant parameter regions where the costs associated to infections and mitigation are jointly minimized. By comparing our model to data, we find signs that COVID-19 was mitigated in a way that favored complex infection dynamics. Our results challenge the intuition that endemic disease dynamics necessarily implies predictability and seasonal waves and show the emergence of complex infection dynamics when humans optimize their reaction to increasing infection numbers.
Advances in machine learning for tumour classification in cancer of unknown primary: A mini-review
Karen Y. Oróstica, Felipe Mardones, Yanara A Bernal, Samuel Molina, Marcos Orchard, Ricardo A. Verdugo, Daniel Carvajal‐Hausdorf, Katherine Marcelain, Seba Contreras†, Ricardo Armisén†
Abstract
Cancers of unknown primary (CUP) are a heterogeneous group of aggressive metastatic cancers where standardised diagnostic techniques fail to identify the organ where it originated, resulting in a poor prognosis and resistance to treatment. Recent advances in large-scale sequencing techniques have enabled the identification of mutational signatures specific to particular tumour subtypes, even from liquid biopsy samples such as blood. This breakthrough paves the way for the development of new cost-effective diagnostic strategies. This mini-review explores recent advancements in Machine Learning (ML) and its application to tumour classification methods for CUP patients, identifying its weaknesses and strengths when classifying the tumour type. In the era of multi-omics, integrating several sources of information (e.g., imaging, molecular biomarkers, and family history) requires important theoretical advancements: increasing the dimensionality of the problem can result in lowering the predictive accuracy and robustness when data is scarce. Here, we review and discuss different architectures and strategies for incorporating cutting-edge machine learning into CUP diagnosis, aiming to bridge the gap between theory and clinical practice. • Cancers of Unknown Primary (CUP) are a diagnostic and clinical challenge in oncology. • Mutational signatures can differentiate tumour tissue and subtypes even in CUP. • Machine Learning leverages hidden and complex patterns in CUP mutational data. • We review recent advancements in ML applied to CUP diagnosis and classification.
Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides
David Medina-Ortiz, Seba Contreras, Diego Fernández, Nicole Soto-García, Iván Moya-Barría, Gabriel Cabas-Mora, Álvaro Olivera-Nappa
Abstract
Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has substantially increased, allowing the application of more complex computational models to study the relations between the peptide composition and function. This work introduces AMP-Detector, a sequence-based classification model for the detection of peptides' functional biological activity, focusing on accelerating the discovery and de novo design of potential antimicrobial peptides (AMPs). AMP-Detector introduces a novel sequence-based pipeline to train binary classification models, integrating protein language models and machine learning algorithms. This pipeline produced 21 models targeting antimicrobial, antiviral, and antibacterial activity, achieving average precision exceeding 83%. Benchmark analyses revealed that our models outperformed existing methods for AMPs and delivered comparable results for other biological activity types. Utilizing the Peptide Atlas, we applied AMP-Detector to discover over 190,000 potential AMPs and demonstrated that it is an integrative approach with generative learning to aid in de novo design, resulting in over 500 novel AMPs. The combination of our methodology, robust models, and a generative design strategy offers a significant advancement in peptide-based drug discovery and represents a pivotal tool for therapeutic applications.
Early mutational signatures and transmissibility of SARS-CoV-2 Gamma and Lambda variants in Chile
Karen Y. Oróstica*, Sebastian B. Mohr*, Jonas Dehning*, Simon Bauer, David Medina-Ortiz, Emil N. Iftekhar, Karen Mujica, Paulo C. Covarrubias, Soledad Ulloa, Andrés Castillo, Anamaria Daza-Sánchez, Ricardo A. Verdugo, Jorge Fernández, Álvaro Olivera-Nappa, Viola Priesemann, Seba Contreras†
Abstract
Genomic surveillance (GS) programmes were crucial in identifying and quantifying the mutating patterns of SARS-CoV-2 during the COVID-19 pandemic. In this work, we develop a Bayesian framework to quantify the relative transmissibility of different variants tailored for regions with limited GS. We use it to study the relative transmissibility of SARS-CoV-2 variants in Chile. Among the 3443 SARS-CoV-2 genomes collected between January and June 2021, where sampling was designed to be representative, the Gamma (P.1), Lambda (C.37), Alpha (B.1.1.7), B.1.1.348, and B.1.1 lineages were predominant. We found that Lambda and Gamma variants' reproduction numbers were 5% (95% CI: [1%, 14%]) and 16% (95% CI: [11%, 21%]) larger than Alpha's, respectively. Besides, we observed a systematic mutation enrichment in the Spike gene for all circulating variants, which strongly correlated with variants' transmissibility during the studied period (r = 0.93, p-value = 0.025). We also characterised the mutational signatures of local samples and their evolution over time and with the progress of vaccination, comparing them with those of samples collected in other regions worldwide. Altogether, our work provides a reliable method for quantifying variant transmissibility under subsampling and emphasises the importance of continuous genomic surveillance.
From emergency response to long-term management: the many faces of the endemic state of COVID-19
Seba Contreras, Emil N. Iftekhar, Viola Priesemann
Abstract
On May 5th 2023, the World Health Organisation declared that COVID-19 does not constitute a Public Health Emergency of International Concern (PHEIC) anymore, advising that it is time to transition to long-term management of the COVID-19 pandemic. With the PHEIC being over, will COVID-19 become endemic, and what does that imply? As SARS-CoV-2 is highly transmissible and immunity against it wanes, one expects that COVID-19 incidence worldwide will settle to a nonzero level. The precise "endemic equilibrium" is determined by the waning immunity and transmission rate of the predominant variants, vaccination rates, seasonality, and human behaviour (including active mitigation responses). In other words, the endemic equilibrium is a result of an implicit balance between the maximum incidence that societies accept and the maximum feasible extent of mitigation they tolerate. Classically, one expects for endemicity either relatively constant incidence, or seasonal waves. However, if moderate mitigation responses remain necessary and interact with seasonality, complex and even chaotic dynamics can emerge even in the endemic state.
Impact of the Euro 2020 championship on the spread of COVID-19
Jonas Dehning*, Sebastian B. Mohr*, Seba Contreras, Philipp Dönges, Emil N. Iftekhar, Oliver Schulz, Philip Bechtle, Viola Priesemann
Abstract
Large-scale events like the UEFA Euro 2020 football (soccer) championship offer a unique opportunity to quantify the impact of gatherings on the spread of COVID-19, as the number and dates of matches played by participating countries resembles a randomized study. Using Bayesian modeling and the gender imbalance in COVID-19 data, we attribute 840,000 (95% CI: [0.39M, 1.26M]) COVID-19 cases across 12 countries to the championship. The impact depends non-linearly on the initial incidence, the reproduction number R, and the number of matches played. The strongest effects are seen in Scotland and England, where as much as 10,000 primary cases per million inhabitants occur from championship-related gatherings. The average match-induced increase in R was 0.46 [0.18, 0.75] on match days, but important matches caused an increase as large as +3. Altogether, our results provide quantitative insights that help judge and mitigate the impact of large-scale events on pandemic spread.
Model-based assessment of sampling protocols for infectious disease genomic surveillance
Seba Contreras, Karen Y. Oróstica, Anamaria Daza-Sanchez, Joel Wagner, Philipp Dönges, David Medina-Ortiz, Matias Jara, Ricardo A. Verdugo, Carlos Conca, Viola Priesemann, Álvaro Olivera-Nappa
Abstract
Genomic surveillance of infectious diseases allows monitoring circulating and emerging variants and quantifying their epidemic potential. However, due to the high costs associated with genomic sequencing, only a limited number of samples can be analysed. Thus, it is critical to understand how sampling impacts the information generated. Here, we combine a compartmental model for the spread of COVID-19 (distinguishing several SARS-CoV-2 variants) with different sampling strategies to assess their impact on genomic surveillance. In particular, we compare adaptive sampling, i.e., dynamically reallocating resources between screening at points of entry and inside communities, and constant sampling, i.e., assigning fixed resources to the two locations. We show that adaptive sampling uncovers new variants up to five weeks earlier than constant sampling, significantly reducing detection delays and estimation errors. This advantage is most prominent at low sequencing rates. Although increasing the sequencing rate has a similar effect, the marginal benefits of doing so may not always justify the associated costs. Consequently, it is convenient for countries with comparatively few resources to operate at lower sequencing rates, thereby profiting the most from adaptive sampling. Finally, our methodology can be readily adapted to study undersampling in other dynamical systems.
Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients
Karen Y. Oróstica, Juan Saez Hidalgo, Pamela Santiago, Solange Rivas, Seba Contreras, Gonzalo Navarro, Juan A. Asenjo, Álvaro Olivera-Nappa, Ricardo Armisén
Abstract
BACKGROUND: Recently, extensive cancer genomic studies have revealed mutational and clinical data of large cohorts of cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), summarises the mutational and clinical profiles of different subtypes of Lung Cancer (LC). Mutational and clinical signatures have been used independently for tumour typification and prediction of metastasis in LC patients. Is it then possible to achieve better typifications and predictions when combining both data streams? METHODS: In a cohort of 1144 Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LSCC) patients, we studied the number of missense mutations (hereafter, the Total Mutational Load TML) and distribution of clinical variables, for different classes of patients. Using the TML and different sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classification models that calculate the likelihood of developing metastasis. RESULTS: We found that LC patients different in age, smoking status, and tumour type had significantly different mean TMLs. Although TML was an informative feature, its effect was secondary to the "tumour stage" feature. However, its contribution to the classification is not redundant with the latter; models trained using both TML and tumour stage performed better than models trained using only one of these variables. We found that models trained in the entire dataset (i.e., without using dimensionality reduction techniques) and without resampling achieved the highest performance, with an F1 score of 0.64 (95%CrI [0.62, 0.66]). CONCLUSIONS: Clinical variables and TML should be considered together when assessing the likelihood of LC patients progressing to metastatic states, as the information these encode is not redundant. Altogether, we provide new evidence of the need for comprehensive diagnostic tools for metastasis.
Generalized property-based encoders and digital signal processing facilitate predictive tasks in protein engineering
David Medina-Ortiz, Seba Contreras†, Juan Amado-Hinojosa, Jorge Torres-Almonacid, Juan A. Asenjo, Marcelo Navarrete, Alvaro Olivera-Nappa†
Abstract
Computational methods in protein engineering often require encoding amino acid sequences, i.e., converting them into numeric arrays. Physicochemical properties are a typical choice to define encoders, where we replace each amino acid by its value for a given property. However, what property (or group thereof) is best for a given predictive task remains an open problem. In this work, we generalize property-based encoding strategies to maximize the performance of predictive models in protein engineering. First, combining text mining and unsupervised learning, we partitioned the AAIndex database into eight semantically-consistent groups of properties. We then applied a non-linear PCA within each group to define a single encoder to represent it. Then, in several case studies, we assess the performance of predictive models for protein and peptide function, folding, and biological activity, trained using the proposed encoders and classical methods (One Hot Encoder and TAPE embeddings). Models trained on datasets encoded with our encoders and converted to signals through the Fast Fourier Transform (FFT) increased their precision and reduced their overfitting substantially, outperforming classical approaches in most cases. Finally, we propose a preliminary methodology to create de novo sequences with desired properties. All these results offer simple ways to increase the performance of general and complex predictive tasks in protein engineering without increasing their complexity.
Describing a landscape we are yet discovering
Seba Contreras†, Jonas Dehning, Viola Priesemann
Abstract
At the beginning of the COVID-19 pandemic, very little was known about both the disease and the virus that caused it. As more information became available, the public awareness reached unprecedented scales; epidemiological terms such as incidence or reproduction number infected our daily conversations. Consequently, high pressure fell on the shoulders of policymakers, who were expected to point us to the way out of this global health threat. However, whom do we ask when we all are still learning? We would say, “let us build a model!”.
New year, new SARS-CoV-2 variant: Resolutions on genomic surveillance protocols to face Omicron
Karen Y. Oróstica, Seba Contreras†, Anamaria Sanchez-Daza, Jorge Fernandez, Viola Priesemann, Alvaro Olivera-Nappa
Abstract
The emergence of SARS-CoV-2 variants with enhanced transmissibility and partial immune escape (also known as Variants of Concern, VOC) has characterised the second year of the COVID-19 pandemic. Genomic surveillance allows identifying hidden, fast-spreading lineages and VOCs before they become a public health threat. The Chilean Public Health Institute (Instituto de Salud Pública, ISP) has led SARS-CoV-2 genomic surveillance initiatives in Chile. Using sequencing data, the ISP has signalled the introduction of VOCs and characterised their transmissibility and mutational signatures, contributing to a better understanding of the local features of the pandemic.
Interplay Between Risk Perception, Behavior, and COVID-19 Spread
Philipp Dönges*, Joel Wagner*, Seba Contreras*, Emil N. Iftekhar*, Simon Bauer, Sebastian B. Mohr, Jonas Dehning, André Calero Valdez, Mirjam Kretzschmar, Michael Mäs, Kai Nagel, Viola Priesemann
Abstract
Pharmaceutical and non-pharmaceutical interventions (NPIs) have been crucial for controlling COVID-19. They are complemented by voluntary health-protective behavior, building a complex interplay between risk perception, behavior, and disease spread. We studied how voluntary health-protective behavior and vaccination willingness impact the long-term dynamics. We analyzed how different levels of mandatory NPIs determine how individuals use their leeway for voluntary actions. If mandatory NPIs are too weak, COVID-19 incidence will surge, implying high morbidity and mortality before individuals react; if they are too strong, one expects a rebound wave once restrictions are lifted, challenging the transition to endemicity. Conversely, moderate mandatory NPIs give individuals time and room to adapt their level of caution, mitigating disease spread effectively. When complemented with high vaccination rates, this also offers a robust way to limit the impacts of the Omicron variant of concern. Altogether, our work highlights the importance of appropriate mandatory NPIs to maximise the impact of individual voluntary actions in pandemic control.
Patient-Wise Methodology to Assess Glycemic Health Status: Applications to Quantify the Efficacy and Physiological Targets of Polyphenols on Glycemic Control
Alvaro Olivera-Nappa†, Seba Contreras†, María Florencia Tevy, David Medina-Ortiz, Andrés Leschot, Pilar Vigil, Carlos Conca
Abstract
A growing body of evidence indicates that dietary polyphenols could be used as an early intervention to treat glucose-insulin (G-I) dysregulation. However, studies report heterogeneous information, and the targets of the intervention remain largely elusive. In this work, we provide a general methodology to quantify the effects of any given polyphenol-rich food or formulae over glycemic regulation in a patient-wise manner using an Oral Glucose Tolerance Test (OGTT). We use a mathematical model to represent individual OGTT curves as the coordinated action of subsystems, each one described by a parameter with physiological interpretation. Using the parameter values calculated for a cohort of 1198 individuals, we propose a statistical model to calculate the risk of dysglycemia and the coordination among subsystems for each subject, thus providing a continuous and individual health assessment. This method allows identifying individuals at high risk of dysglycemia—which would have been missed with traditional binary diagnostic methods—enabling early nutritional intervention with a polyphenol-supplemented diet where it is most effective and desirable. Besides, the proposed methodology assesses the effectiveness of interventions over time when applied to the OGTT curves of a treated individual. We illustrate the use of this method in a case study to assess the dose-dependent effects of Delphinol® on reducing dysglycemia risk and improving the coordination between subsystems. Finally, this strategy enables, on the one hand, the use of low-cost, non-invasive methods in population-scale nutritional studies. On the other hand, it will help practitioners assess the effectiveness of an intervention based on individual vulnerabilities and adapt the treatment to manage dysglycemia and avoid its progression into disease.
Rethinking COVID-19 vaccine allocation: it is time to care about our neighbours
Seba Contreras, Alvaro Olivera-Nappa, Viola Priesemann
Abstract
The COVID-19 pandemic changed nearly every aspect of our lives. The rapid spread of the disease exposed several layers of inequality that it has exploited to propagate preferentially. We find these layers in different contexts and levels, such as the impossibility to self-isolate and do remote work (at the individual level) or economic constraints to deploy a fast vaccination program (at the country level). In the context of vaccination programmes, resources must be optimised to alleviate the pandemic burden where it is needed the most.
Low case numbers enable long-term stable pandemic control without lockdowns
Seba Contreras*, Jonas Dehning*, Sebastian Mohr*, Paul F Spitzner*, Simon Bauer*, Viola Priesemann*
Abstract
The traditional long-term solutions for epidemic control involve eradication or population immunity. Here, we analytically derive the existence of a third viable solution; a stable equilibrium at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events and only moderate restrictions remain necessary. In this equilibrium, daily cases stabilize around ten or fewer new infections per million people. However, stability is endangered if restrictions are relaxed or case numbers grow too high. The latter destabilization marks a tipping point beyond which the spread self-accelerates. We show that a lockdown can reestablish control and that recurring lockdowns are not necessary given sustained, moderate contact reduction. We illustrate how this strategy profits from vaccination and helps mitigate variants of concern. This strategy reduces cumulative cases (and fatalities) four times more than strategies that only avoid hospital collapse. In the long term, immunization, large-scale testing, and international coordination will further facilitate control.
Relaxing restrictions at the pace of vaccination increases freedom and guards against further COVID-19 waves
Simon Bauer*, Seba Contreras*, Jonas Dehning, Matthias Linden, Emil Iftekhar, Sebastian Mohr, Alvaro Olivera-Nappa, Viola Priesemann
Abstract
Mass vaccination offers a promising exit strategy for the COVID-19 pandemic. However, as vaccination progresses, demands to lift restrictions increase, despite most of the population remaining susceptible. Using our age-stratified SEIRD-ICU compartmental model and curated epidemiological and vaccination data, we quantified the rate (relative to vaccination progress) at which countries can lift non-pharmaceutical interventions without overwhelming their healthcare systems. We analyzed scenarios ranging from immediately lifting restrictions (accepting high mortality and morbidity) to reducing case numbers to a level where test-trace-and-isolate (TTI) programs efficiently compensate for local spreading events. In general, the age-dependent vaccination roll-out implies a transient decrease of more than ten years in the average age of ICU patients and deceased. The pace of vaccination determines the speed of lifting restrictions; Taking the European Union (EU) as an example case, all considered scenarios allow for steadily increasing contacts starting in May 2021 and relaxing most restrictions by autumn 2021. Throughout summer 2021, only mild contact restrictions will remain necessary. However, only high vaccine uptake can prevent further severe waves. Across EU countries, seroprevalence impacts the long-term success of vaccination campaigns more strongly than age demographics. In addition, we highlight the need for preventive measures to reduce contagion in school settings throughout the year 2021, where children might be drivers of contagion because of them remaining susceptible. Strategies that maintain low case numbers, instead of high ones, reduce infections and deaths by factors of eleven and five, respectively. In general, policies with low case numbers significantly benefit from vaccination, as the overall reduction in susceptibility will further diminish viral spread. Keeping case numbers low is the safest long-term strategy because it considerably reduces mortality and morbidity and offers better preparedness against emerging escape or more contagious virus variants while still allowing for higher contact numbers (freedom) with progressing vaccinations.
Risking further COVID-19 waves despite vaccination
Seba Contreras, Viola Priesemann
Abstract
The sudden outbreak and global spread of COVID-19 took the world by surprise. Policy makers started to work side-by-side with theoreticians, as there were and still are many unknowns, especially regarding properties of virus variants, and the subsequent future development of the pandemic. In times of uncertainty, mathematical models have shed light on the evolution of the pandemic to the best of current scientific knowledge.
The challenges of containing SARS-CoV-2 via test-trace-and-isolate
Seba Contreras*, Jonas Dehning*, Matthias Loidolt*, Johannes Zierenberg, Paul F Spitzner, Jorge Urrea-Quintero, Sebastian Mohr, Michael Wibral, Viola Priesemann
Abstract
Without a cure, vaccine, or proven long-term immunity against SARS-CoV-2, test-trace-and-isolate (TTI) strategies present a promising tool to contain its spread. For any TTI strategy, however, mitigation is challenged by pre- and asymptomatic transmission, TTI-avoiders, and undetected spreaders, which strongly contribute to “hidden” infection chains. Here, we study a semi-analytical model and identify two tipping points between controlled and uncontrolled spread; (1) the behavior-driven reproduction number of the hidden chains becomes too large to be compensated by the TTI capabilities, and (2) the number of new infections exceeds the tracing capacity. Both trigger a self-accelerating spread. We investigate how these tipping points depend on challenges like limited cooperation, missing contacts, and imperfect isolation. Our results suggest that TTI alone is insufficient to contain an otherwise unhindered spread of SARS-CoV-2, implying that complementary measures like social distancing and improved hygiene remain necessary.
Real-time estimation of Rt for supporting public-health policies against COVID-19
Seba Contreras, H Andrés Villavicencio, David Medina-Ortiz, Claudia P Saavedra, Alvaro Olivera-Nappa
Abstract
In the absence of a consensus protocol to slow down the spread of SARS-CoV-2, policymakers need real-time indicators to support decisions in public health matters. The Effective Reproduction Number (Rt) represents the number of secondary infections generated per each case and can be dramatically modified by applying effective interventions. However, current methodologies to calculate Rt from data remain somewhat cumbersome, thus raising a barrier between its timely calculation and application by policymakers. In this work, we provide a simple mathematical formulation for obtaining the effective reproduction number in real-time using only and directly daily official case reports, obtained by modifying the equations describing the viral spread. We numerically explore the accuracy and limitations of the proposed methodology, which was demonstrated to provide accurate, timely, and intuitive results. We illustrate the use of our methodology to study the evolution of the pandemic in different iconic countries, and to assess the efficacy and promptness of different public health interventions.
DMAKit: A user-friendly web platform for bringing state-of-the-art data analysis techniques to non-specific users
David Medina-Ortiz, Seba Contreras, Cristofer Quiroz, Juan A Asenjo, Alvaro Olivera-Nappa
Abstract
Tremendous advances in different areas of knowledge are producing vast volumes of data, a quantity so large that it has made necessary the development of new computational algorithms. Among the algorithms developed, we find Machine Learning models and specific data mining techniques that might be useful for all areas of knowledge. The use of computational tools for data analysis is increasingly required, given the need to extract meaningful information from such large volumes of data. However, there are no free access libraries, modules, or web services that comprise a vast array of analytical techniques in a user-friendly environment for non-specific users. Those that exist raise high usability barriers for those untrained in the field as they usually have specific installation requirements and require in-depth programming knowledge, or may result expensive. As an alternative, we have developed DMAKit, a user-friendly web platform powered by DMAKit-lib, a new library implemented in Python, which facilitates the analysis of data of different kind and origins. Our tool implements a wide array of state-of-the-art data mining and pattern recognition techniques, allowing the user to quickly implement classification, prediction or clustering models, statistical evaluation, and feature analysis of different attributes in diverse datasets without requiring any specific programming knowledge. DMAKit is especially useful for users who have large volumes of data to be analyzed but do not have the informatics, mathematical, or statistical knowledge to implement models. We expect this platform to provide a way to extract information and analyze patterns through data mining techniques for anyone interested in applying them with no specific knowledge required. Particularly, we present several cases of study in the areas of biology, biotechnology, and biomedicine, where we highlight the applicability of our tool to ease the labor of non-specialist users to apply data analysis and pattern recognition techniques. DMAKit is available for non-commercial use as an open-access library, licensed under the GNU General Public License, version GPL 3.0. The web platform is publicly available at https://pesb2.cl/dmakitWeb . Demonstrative and tutorial videos for the web platform are available in https://pesb2.cl/dmakittutorials/ . Complete urls for relevant content are listed in the Data Availability section.
Statistically-based methodology for revealing real contagion trends and correcting delay-induced errors in the assessment of COVID-19 pandemic
Seba Contreras, Juan Pablo Biron-Lattes, H Andrés Villavicencio, David Medina-Ortiz, Nyna Llanovarced-Kawles, Alvaro Olivera-Nappa
Abstract
COVID-19 pandemic has reshaped our world in a timescale much shorter than what we can understand. Particularities of SARS-CoV-2, such as its persistence in surfaces and the lack of a curative treatment or vaccine against COVID-19, have pushed authorities to apply restrictive policies to control its spreading. As data drove most of the decisions made in this global contingency, their quality is a critical variable for decision-making actors, and therefore should be carefully curated. In this work, we analyze the sources of error in typically reported epidemiological variables and usual tests used for diagnosis, and their impact on our understanding of COVID-19 spreading dynamics. We address the existence of different delays in the report of new cases, induced by the incubation time of the virus and testing-diagnosis time gaps, and other error sources related to the sensitivity/specificity of the tests used to diagnose COVID-19. Using a statistically-based algorithm, we perform a temporal reclassification of cases to avoid delay-induced errors, building up new epidemiologic curves centered in the day where the contagion effectively occurred. We also statistically enhance the robustness behind the discharge/recovery clinical criteria in the absence of a direct test, which is typically the case of non-first world countries, where the limited testing capabilities are fully dedicated to the evaluation of new cases. Finally, we applied our methodology to assess the evolution of the pandemic in Chile through the Effective Reproduction Number Rt, identifying different moments in which data was misleading governmental actions. In doing so, we aim to raise public awareness of the need for proper data reporting and processing protocols for epidemiological modelling and predictions.
A multi-group SEIRA model for the spread of COVID-19 among heterogeneous populations
Seba Contreras, H Andrés Villavicencio, David Medina-Ortiz, Juan Pablo Biron-Lattes, Alvaro Olivera-Nappa
Abstract
The outbreak and propagation of COVID-19 have posed a considerable challenge to modern society. In particular, the different restrictive actions taken by governments to prevent the spread of the virus have changed the way humans interact and conceive interaction. Due to geographical, behavioral, or economic factors, different sub-groups among a population are more (or less) likely to interact, and thus to spread/acquire the virus. In this work, we present a general multi-group SEIRA model for representing the spread of COVID-19 among a heterogeneous population and test it in a numerical case of study. By highlighting its applicability and the ease with which its general formulation can be adapted to particular studies, we expect our model to lead us to a better understanding of the evolution of this pandemic and to better public-health policies to control it.
Country-Wise Forecast Model for the Effective Reproduction Number Rt of Coronavirus Disease
David Medina-Ortiz, Seba Contreras, Yasna Barrera-Saavedra, Gabriel Cabas-Mora, Alvaro Olivera-Nappa
Abstract
Due to the particularities of SARS-CoV-2, public health policies have played a crucial role in the control of the COVID-19 pandemic. Epidemiological parameters for assessing the stage of the outbreak, such as the Effective Reproduction Number (Rt), are not always straightforward to calculate, raising barriers between the scientific community and non-scientific decision-making actors. The combination of estimators of Rt with elaborated Machine Learning-based forecasting techniques provides a way to support decision-making when assessing governmental plans of action. In this work, we develop forecast models applying logistic growth strategies and auto-regression techniques based on Auto-Regressive Integrated Moving Average (ARIMA) models for each country that records information about the COVID-19 outbreak. Using the forecast for the main variables of the outbreak, namely the number of infected (I), recovered (R), and dead (D) individuals, we provide a real-time estimation of Rt and its temporal evolution within a timeframe. With such models, we evaluate Rt trends at the continental and country levels, providing a clear picture of the effect governmental actions have had on the spread. We expect this methodology of combining forecast models for raw data to calculate Rt to serve as valuable input to support decision-making related to controlling the spread of SARS-CoV-2.
A new statistically-based methodology for variability assessment of rheological parameters in mineral processing
Seba Contreras, Claudia Castillo, Álvaro Olivera-Nappa, Brian Townley, Christian F. Ihle
Abstract
If variability of input data for rheological measurements is not adequately included, their associated uncertainty and subsequent modelling can be underrated. Mineral pulp rheology determination is commonly done through triplicate tests, with such variability reported as multiples of a standard deviation, with the potential for underestimation. In the present work, a novel statistically-based methodology for the estimation of uncertainty in the rheological characterization of mineral suspensions — and other parametric models — is proposed. From the variability of the experimental measurements and the analytical propagation of errors, a set of rheological profiles are generated using Monte Carlo simulations within a variability frame. The corresponding inverse problem for curve-fitting is solved individually, resulting in distributions of fitted parameters, which were statistically analyzed to obtain representative values for both the parameter and its true variability. The methodology proposed herein has been used to explore the applicability and limitations of the Herschel-Bulkley and Bingham models under specific experimental and data analysis protocols, where the relevance of including low-shear-rate measurement points or yield stress measurements using alternative methods is exposed. Additionally, we present a case study on the effect of the concentration of NaCl on the rheological response of synthetic tailings consisting of quartz suspensions doped with kaolinite, bentonite and kaolinite-bentonite blends, using the proposed methodology with a concentric cylinder rheometer. Results show predominantly decreasing trends in yield stress as salt concentration increases, with non-monotonical behavior and strongest variability associated to the quartz-bentonite blend.
A novel synthetic model of the glucose-insulin system for patient-wise inference of physiological parameters from small-size OGTT data
Seba Contreras, David Medina-Ortiz, Carlos Conca, Alvaro Olivera-Nappa
Abstract
Existing mathematical models for the glucose-insulin (G-I) dynamics often involve variables that are not susceptible to direct measurement. Standard clinical tests for measuring G-I levels for diagnosing potential diseases are simple and relatively cheap, but seldom give enough information to allow the identification of model parameters within the range in which they have a biological meaning, thus generating a gap between mathematical modeling and any possible physiological explanation or clinical interpretation. In the present work, we present a synthetic mathematical model to represent the G-I dynamics in an Oral Glucose Tolerance Test (OGTT), which involves for the first time for OGTT-related models, Delay Differential Equations. Our model can represent the radically different behaviors observed in a studied cohort of 407 normoglycemic patients (the largest analyzed so far in parameter fitting experiments), all masked under the current threshold-based normality criteria. We also propose a novel approach to solve the parameter fitting inverse problem, involving the clustering of different G-I profiles, a simulation-based exploration of the feasible set, and the construction of an information function which reshapes it, based on the clinical records, experimental uncertainties, and physiological criteria. This method allowed an individual-wise recognition of the parameters of our model using small size OGTT data (5 measurements) directly, without modifying the routine procedures or requiring particular clinical setups. Therefore, our methodology can be easily applied to gain parametric insights to complement the existing tools for the diagnosis of G-I dysregulations. We tested the parameter stability and sensitivity for individual subjects, and an empirical relationship between such indexes and curve shapes was spotted. Since different G-I profiles, under the light of our model, are related to different physiological mechanisms, the present method offers a tool for personally-oriented diagnosis and treatment and to better define new health criteria.
Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets
David Medina-Ortiz, Seba Contreras, Cristofer Quiroz, Alvaro Olivera-Nappa
Abstract
In highly non-linear datasets, attributes or features do not allow readily finding visual patterns for identifying common underlying behaviors. Therefore, it is not possible to achieve classification or regression using linear or mildly non-linear hyperspace partition functions. Hence, supervised learning models based on the application of most existing algorithms are limited, and their performance metrics are low. Linear transformations of variables, such as principal components analysis, cannot avoid the problem, and even models based on artificial neural networks and deep learning are unable to improve the metrics. Sometimes, even when features allow classification or regression in reported cases, performance metrics of supervised learning algorithms remain unsatisfyingly low. This problem is recurrent in many areas of study as, per example, the clinical, biotechnological, and protein engineering areas, where many of the attributes are correlated in an unknown and very non-linear fashion or are categorical and difficult to relate to a target response variable. In such areas, being able to create predictive models would dramatically impact the quality of their outcomes, generating an immediate added value for both the scientific and general public. In this manuscript, we present RV-Clustering, a library of unsupervised learning algorithms, and a new methodology designed to find optimum partitions within highly non-linear datasets that allow deconvoluting variables and notoriously improving performance metrics in supervised learning classification or regression models. The partitions obtained are statistically cross-validated, ensuring correct representativity and no over-fitting. We have successfully tested RV-Clustering in several highly non-linear datasets with different origins. The approach herein proposed has generated classification and regression models with high-performance metrics, which further supports its ability to generate predictive models for highly non-linear datasets. Advantageously, the method does not require significant human input, which guarantees a higher usability in the biological, biomedical, and protein engineering community with no specific knowledge in the machine learning area.
Preprints
Household size can explain 40% of the variance in cumulative COVID-19 incidence across Europe
Seba Contreras†, Philipp Dönges, Maciej Filiński, Joel Wagner, V. A. Bezborodov, Marcin Bodych, Barbara Pabjan, Franciszek Rakowski, Jan Pablo Burgard, Tyll Krueger, Viola Priesemann
Abstract
Household size impacts the spread of respiratory infectious diseases: Larger households tend to boost transmission by acquiring external infections more frequently and subsequently transmitting them back into the community. Furthermore, mandatory interventions primarily modulate contagion between households rather than within them. We developed an approach to quantify the role of household size in epidemics by separating within-household from out-household transmission, and found that household size explains 41% of the variability in cumulative COVID-19 incidence across 34 European countries (95% confidence interval: [15%, 46%]). The contribution of households to the overall dynamics can be quantified by a boost factor that increases with the effective household size, implying that countries with larger households require more stringent interventions to achieve the same levels of containment. This suggests that households constitute a structural (dis-)advantage that must be considered when designing and evaluating mitigation strategies.
Novel multiplex tools in an epidemic panel improve prediction of RSV infection dynamics and disease burden – a RESPINOW analysis
Manuela Harries, Carolina Judith Klett-Tammen, Isti Rodiah, Alex Dulovic, Veronika K. Jaeger, Jessica Krepel, Seba Contreras, Katrin Maak, Patrick Marsall, Annette Möller, Jana-Kristin Heise, Stefanie Castell, RESPINOW study group, Nicole Schneiderhan-Marra, André Karch, Berit Lange
Abstract
Respiratory Syncytial Virus (RSV) is one of the leading causes of morbidity and mortality among infants and adult risk groups worldwide. Substantial case-underdetection and gaps in the understanding of reinfection dynamics of RSV limit reliable projection estimates. Here, we use a novel RSV multiplex serological assay in a population-based panel to estimate season and age-specific probability of reinfection and combine it with sentinel and notification data to parameterize a mathematical model tailored to project RSV dynamics in Germany from 2020 to 2023. Our reinfection estimates, based on a 20% post-F and a 45% N antibody increase in the assay over consecutive periods, were 5·7% (95%CI: 4·7-6·9) from 2020 to 2022 and 12·7% (95%CI: 10·5-15·2) from 2022 to 2023 in adults. In 2021, 30-39 year olds had a higher risk of reinfection, whereas in 2022, all but the 30-39 age group had an increased risk of reinfection. This suggests age-differential infection acquisition in the two seasons, e.g. due to still stronger public health measures in place in 2021 than in 2022. Model-based projections that include the population-based reinfection estimations predicted the onset and peak for the 23/24 RSV season better than those only based on surveillance estimates. Rapid, age-specific reinfection assessments and models incorporating this data will be critical for understanding and predicting RSV dynamics, especially with changing post-pandemic patterns and new prevention strategies e.g. monoclonal antibody. Helmholtz Association, EU Horizon 2020 research and innovation program, Federal Ministry of Education and Research, and German Research supported this work.
Book chapters
COVID-19 Modeling Under Uncertainty: Statistical Data Analysis for Unveiling True Spreading Dynamics and Guiding Correct Epidemiological Management
Anamaria Sanchez-Daza, David Medina-Ortiz, Álvaro Olivera-Nappa, Seba Contreras