High-Resolution Epidemic Simulation Using Within-Host Infection and Contact Data

Abstract

Background: Transmission in epidemics of infectious diseases is characterized by a high level of subject-specific elements. These include heterogeneous infection conditions, time-dependent transmission potential, and age-dependent contact structure. These insights are often lost in epidemic models using population data. Here we submit an approach that can capture these details, paving the way for studying epidemics in a more mechanistic and realistic way. Methods: Using experimental data, we formulated mathematical models of a pathogen infection dynamics from which we can simulate its transmission potential mechanistically. The models were then embedded in our implement of an age-specific contact network structure that allows to express all elements relevant to the transmission process. This approach is illustrated here with an example of Ebola virus (EBOV). Results:The results showed that within-host infection dynamics can capture EBOV's transmission parameters as good as approaches using population data. Population age-structure, contact distribution and patterns can also be captured with our network generating algorithm. This framework opens vast opportunities for the investigations of each element involved in the epidemic process. Here, estimating EBOV's reproduction number revealed a heterogeneous pattern among age-groups, prompting questions on current estimates which are not adjusted for this factor. Assessments of mass vaccination strategies showed that a time window from five months before to one week after the start of an epidemic appeared to be effective. Noticeably, compared to a non-intervention scenario, a low vaccination coverage of 33% could reduce number of cases by ten to hundred times as well as lessen the case-fatality rate. Conclusions: This is the first effort coupling directly within-host infection model into an age-structured epidemic network model, adding more realistic elements in simulating epidemic processes. Experimental data at the within-host infection are shown able to capture upfront key parameters of a pathogen; the applications of this approach will give us more time to prepare for potential epidemics. Population of interest in epidemic assessments could be modeled with an age-specific contact network without exhaustive amount of data. Further assessments and adaptations for different pathogens and scenarios are underway to explore multilevel aspects in infectious diseases epidemics.Keywords: high-resolution; epidemic; simulation; within-host infection; age-structure; contact network CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/133421 doi: bioRxiv preprint first posted online Nguyen et al. Page 2 of 22 BackgroundEpidemics of infectious diseases are listed among the potential catastrophes and can be potentially misused as mass destruction weapons [1]. Overwhelming research efforts have been developed to early predict the danger ...

In-host Mathematical Modelling of COVID-19 in Humans

“…The race to develop the first vaccine to tackle COVID-19 has started with the first clinical trial just 60 days after the genetic sequence of the virus. Modelling work developed in this paper paves the way for future mathematical models of COVID-19 to reveal prophylactic and therapeutic interventions at multi-scale levels ( Almocera, Hernandez-Vargas, 2019 , Feng, Cen, Zhao, Velasco-Hernandez, 2015 , Feng, Velasco-Hernandez, Tapia-Santos, Leite, 2012 , Handel, Rohani, 2015 , Nguyen, Mikolajczyk, Hernandez-Vargas, 2018 , Parra-Rojas, Nguyen, Hernandez-Mejia, Hernandez-Vargas, 2018 ). Further insights into immunology and pathogenesis of SARS-CoV-2 will help to improve the outcome of this and future pandemics.…”

Mathematical modeling of contact tracing as a control strategy of Ebola virus disease

“…These people may have been exposed to the infection and do not know it, or they may have acquired the disease pathogen but do not show symptoms [31,48]. Therefore, those individuals in the Q-class who do not show symptoms after 21 days (the maximun latent period for EVD [3,22,55]) will be considered susceptibles [30,63], whereas those who are tested positive will be hospitalized. • U (t): Unidentified individuals who have been in contact with infectives.…”

A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics: influenza A as a case study

“…Multi-scale models have been used to connect epidemiological dynamics at the patientlevel (within-host) to those at the population-scale (between-host) [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. These models (sometimes referred to as nested models [19,30]) tend to assume a specified relationship between the level of infection within a patient and the rate at which the patient transmits the pathogen to susceptible individuals [18,19,35].…”

“…While one reason for this is the lack of widely available datasets [18,36], we contend that another contributing factor is that previous multi-scale modelling frameworks have been complex, making them challenging to implement other than by highly specialist mathematical modellers. Such frameworks have often employed integro-differential equations (IDEs) [19,24,27,30,31,33,35,37], although alternatives such as individual-based stochastic models [12,23,25,26,38] have also been considered. IDEs are challenging to solve, requiring bespoke numerical methods [28].…”