High-resolution epidemic simulation using within-host infection and contact data

Abstract

BackgroundRecent epidemics have entailed global discussions on revamping epidemic control and prevention approaches. A general consensus is that all sources of data should be embraced to improve epidemic preparedness. As a disease transmission is inherently governed by individual-level responses, pathogen dynamics within infected hosts posit high potentials to inform population-level phenomena. We propose a multiscale approach showing that individual dynamics were able to reproduce population-level observations.MethodsUsing experimental data, we formulated mathematical models of pathogen infection dynamics from which we simulated mechanistically its transmission parameters. The models were then embedded in our implementation of an age-specific contact network that allows to express individual differences relevant to the transmission processes. This approach is illustrated with an example of Ebola virus (EBOV).ResultsThe results showed that a within-host infection model can reproduce EBOV’s transmission parameters obtained from population data. At the same time, population age-structure, contact distribution and patterns can be expressed using network generating algorithm. This framework opens a vast opportunity to investigate individual roles of factors involved in the epidemic processes. Estimating EBOV’s reproduction number revealed a heterogeneous pattern among age-groups, prompting cautions on estimates unadjusted for contact pattern. Assessments of mass vaccination strategies showed that vaccination conducted in a time window from five months before to one week after the start of an epidemic appeared to strongly reduce epidemic size. Noticeably, compared to a non-intervention scenario, a low critical vaccination coverage of 33% cannot ensure epidemic extinction but could reduce the number of cases by ten to hundred times as well as lessen the case-fatality rate.ConclusionsExperimental data on the within-host infection have been able to capture upfront key transmission parameters of a pathogen; the applications of this approach will give us more time to prepare for potential epidemics. The population of interest in epidemic assessments could be modelled with an age-specific contact network without exhaustive amount of data. Further assessments and adaptations for different pathogens and scenarios to explore multilevel aspects in infectious diseases epidemics are underway.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-5709-x) contains supplementary material, which is available to authorized users.

In-host 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 [52][53][54][55][56][57]. Further insights into immunology and pathogenesis of COVID-19 will help to improve the outcome of this and future pandemics.…”

“…Zou et al [20] presented 55 the viral load in nasal and throat swabs of 17 symptomatic patients. Interestingly, SARS-CoV-2 56 replication cycles may last longer than flu, about 10 days or more after the incubation period [4,20]. 57 Here, we contribute to the mathematical study of SARS-CoV-2 dynamics at within-host level based on 58 data presented by Wolfel et al [21].…”

“…In a more technical aspect using only viral load on the target cell model to estimate parameters may 191 lead to identifiability problems [48][49][50][51] multi-scale levels [52][53][54][55][56][57]. Further insights into immunology and pathogenesis of SARS-CoV-2 will help 199 to improve the outcome of this and future pandemics.…”

COVID-19 Network Model to Evaluate Vaccine Strategies towards Herd Immunity

“…Within-host dynamics such as the viral dynamics and immune responses would be important to be included in a framework with higher resolution [44].…”

Data-driven contact structures: From homogeneous mixing to multilayer networks

“…Various methods have been developed to infer the contact patterns in the absence of direct data [47][48][49], and to project them into the future [50]. And yet, most studies that use this data disregard the whole distribution of contacts and use only the average number of contacts between groups, completely neglecting the individual heterogeneity (with few exceptions [51]). As a consequence, in these studies, superspreading events cannot occur naturally, unless the model is modified, contrary to network models in which the large connectivity of some individuals can result in the appearance of such events.…”