Integrative Within-Host and Between-Hosts Modeling for Preparedness Against Infectious Diseases

Tuesday, June 15 at 02:15am (PDT)
Tuesday, June 15 at 10:15am (BST)
Tuesday, June 15 06:15pm (KST)

SMB2021 SMB2021 Follow Monday (Tuesday) during the "MS05" time block.
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Esteban Hernandez-Vargas (Instituto de Matematicas, UNAM, Unidad Juriquilla, Queretaro, Mexico., Mexico), Jorge X. Velasco-Hernandez (Instituto de Matematicas, UNAM, Unidad Juriquilla, Queretaro, Mexico., Mexico)


Mathematical models for the spread of diseases have played a central role in epidemics, providing a cost-effective way of assessing disease transmission as well as targets for preventing disease and control [1-3]. The spread of pathogens between infectious and susceptible hosts can be orchestrated via close physical interactions or by droplets. Understanding disease transmission remains a central vexation for science as it involves several complex and dynamic processes. The link between the infection dynamics within an infected host and the susceptible population-level transmission is widely acknowledged [4,5] - but further efforts are needed for a full comprehension of disease transmissions. At the frontiers of different disciplines, the goal of the this mini-symposium is to bring experts to develop and maturate a within-host and between-host modeling approach as a new paradigm for a better preparedness to infections and epidemics. The different talks will assess key components for predictively simulating disease transmission across scales - from the infected host-dynamics, population level and the coupling between the scales. References [1] Rose, M. A. et al. The epidemiological impact of childhood influenza vaccination using live-attenuated influenza vaccine (LAIV) in Germany: predictions of a simulation study. BMC Infect. Dis. 14, 40 (2014). [2] Ferguson, N. M. et al. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437, 209–214 (2005). [3] Tanser, F., Baernighausen, T., Graspa, E., Zaidi, J. and Newell, M.-L. High Coverage of ART Associated with. Science (80-. ). 339, 966–972 (2013). [4] Feng, Z., Velasco-Hernandez, J. X., Tapia-Santos, B., and Leite, M. C. a. A model for coupling within-host and between-host dynamics in an infectious disease. Nonlinear Dynamics, 68(3), 401–411 (2011). [5] Nguyen, V. K., Mikolajczyk, R. and Hernandez-Vargas, E. A. High-resolution epidemic simulation using within-host infection and contact data, BMC Public Health, 18(1) (2018)

Jan Fuhrmann

(Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany, Germany)
"Modeling the COVID-19 epidemic in Germany"
The novel corona virus SARS-CoV-2 that causes the disease COVID19 was first identified in Hubei province, China, in 2019 and has since spread around the globe. Its virulence and the morbidity associated with has caused the WHO to declare this new respiratory disease a pandemic in March 2020. From a modeling perspective this pandemic poses several challenges. As with most new infectious diseases, many parameters are not particularly well known. Alarmingly high numbers of known infectious and COVID-19 related deaths led to contact reductions among the population, partly by increased caution, partly mandated by authorities. And the infection often leading to mild, non-specific symptoms - or even no symptoms at all - makes it all but impossible to estimate the ratio of detected cases among all infections. We shall discuss some of the data available from public domain sources and show how ordinary differential equation models can be used to reproduce these data, generate short term forecasts, and simulate possible further courses of the epidemic for different scenarios. Particular emphasis will be put on the relevance of detection ratios and how they are affected by test strategies and case numbers.

Lubna Pinky

(University of Tennessee Health Science Center, Memphis, TN 38163, USA, USA)
"Quantifying Dose-, Strain-, and Tissue-Specific Kinetics of Parainfluenza Virus Infection"
Human parainfluenza viruses (HPIVs) are a leading cause of acute respiratory infection hospitalization in children, yet little is known about how dose, strain, tissue tropism, and individual heterogeneity affects the processes driving growth and clearance kinetics. Longitudinal measurements are possible by using reporter sendai viruses, murine parainfluenza counterpart, that express luciferase, where the insertion location yields a wild-type-like or attenuated phenotype. Bioluminescence measurements from individual animals infected with either strain suggests that there is a rapid increase in expression followed by a peak, biphasic clearance, and resolution. However, these kinetics vary between individuals and with dose, strain, and whether the infection was initiated in the upper and/or lower respiratory tract. To quantify the differences, we translated the bioluminescence measurements from the nasopharynx, trachea, and lung into viral loads and used a mathematical model together with nonlinear mixed effects approach to define the mechanisms distinguishing each scenario. The results confirmed a higher rate of virus production with the wild-type-like virus compared to its attenuated counterpart, and suggested that low doses result in disproportionately fewer infected cells. The analyses indicated faster infectivity and infected cell clearance rates in the lung and that higher viral doses, and concomitantly higher infected cell numbers, resulted in more rapid clearance. Infected cell clearance was also highly variable amongst individuals, which was particularly evident during infection in the lung. These critical differences provide important insight into distinct HPIV dynamics, and show how bioluminescence data combined with quantitative analyses can be used to dissect host-, virus-, and dose-dependent effects.

Fernando Saldaña

(Instituto de Matematicas UNAM at Juriquilla, Mexico, Mexico)
"A model for vaccine escape under unequal vaccine access"
Currently, there are concerns that without adequate vaccine distribution, the SARS-CoV-2 variants will grow and mutate, curbing the progress that has been made since the vaccine has been made available. In this work, we present a mathematical model to study vaccine escape evolution in structured host populations. We find that vaccine escape mutants are less likely to come from vaccinated regions where there is a strong selection pressure for vaccine escape and more likely to come from a neighboring unvaccinated region where there is no selection for escape.

Suneet Singh Jhutty

(Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany., Germany)
"Mapping of Influenza Infection from Blood Data with Machine Learning Methods"
Seasonal and pandemic influenza causes enormous economic loss, health complications and death. The measurement of clinical markers for influenza and its respective immune responses is time-consuming and almost impossible to perform. Here, we show for first time the proof applicability and implementation of machine learning algorithms to infer the viral load and immune markers in the lung compartment based on hematology data of mice infected with influenza H1N1. Our results show that even with high variability in the data, the model prediction to track the infection in the host is possible. Platelets and granulocytes play an essential role to track influenza infection. The proposed in silico tool paved the way towards a better prognosis of influenza infections and possibly other respiratory diseases.

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Virtual conference of the Society for Mathematical Biology, 2021.