The rate of viral transfer between upper and lower respiratory tracts determines RSV illness duration

Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela

We propose two different mathematical models to study the effect of immigration on the COVID-19 pandemic. The first model does not consider immigration, whereas the second one does. Both mathematical models consider five different subpopulations: susceptible, exposed, infected, asymptomatic carriers, and recovered. We find the basic reproduction number R₀ using the next-generation matrix method for the mathematical model without immigration. This threshold parameter is paramount because it allows us to characterize the evolution of the disease and identify what parameters substantially affect the COVID-19 pandemic outcome. We focus on the Venezuelan scenario, where immigration and emigration have been important over recent years, particularly during the pandemic. We show that the estimation of the transmission rates of the SARS-CoV-2 are affected when the immigration of infected people is considered. This has an important consequence from a public health perspective because if the basic reproduction number is less than unity, we can expect that the SARS-CoV-2 would disappear. Thus, if the basic reproduction number is slightly above one, we can predict that some mild non-pharmaceutical interventions would be enough to decrease the number of infected people. The results show that the dynamics of the spread of SARS-CoV-2 through the population must consider immigration to obtain better insight into the outcomes and create awareness in the population regarding the population flow.

Use of mathematical modelling to assess respiratory syncytial virus epidemiology and interventions: a literature review

Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infection worldwide, resulting in approximately sixty thousand annual hospitalizations of< 5-year-olds in the United States alone and three million annual hospitalizations globally. The development of over 40 vaccines and immunoprophylactic interventions targeting RSV has the potential to significantly reduce the disease burden from RSV infection in the near future. In the context of RSV, a highly contagious pathogen, dynamic transmission models (DTMs) are valuable tools in the evaluation and comparison of the effectiveness of different interventions. This review, the first of its kind for RSV DTMs, provides a valuable foundation for future modelling efforts and highlights important gaps in our understanding of RSV epidemics. Specifically, we have searched the literature using Web of Science, Scopus, Embase, and PubMed to identify all published manuscripts reporting the development of DTMs focused on the population transmission of RSV. We reviewed the resulting studies and summarized the structure, parameterization, and results of the models developed therein. We anticipate that future RSV DTMs, combined with cost-effectiveness evaluations, will play a significant role in shaping decision making in the development and implementation of intervention programs.

Mathematical modelling of SARS-CoV-2 infection of human and animal host cells reveals differences in the infection rates and delays in viral particle production by infected cells

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV -2), a causative agent of COVID-19 disease, poses a significant threat to public health. Since its outbreak in December 2019, Wuhan, China, extensive collection of diverse data from cell culture and animal infections as well as population level data from an ongoing pandemic, has been vital in assessing strategies to battle its spread. Mathematical modelling plays a key role in quantifying determinants that drive virus infection dynamics, especially those relevant for epidemiological investigations and predictions as well as for proposing efficient mitigation strategies. We utilized a simple mathematical model to describe and explain experimental results on viral replication cycle kinetics during SARS-CoV-2 infection of animal and human derived cell lines, green monkey kidney cells, Vero-E6, and human lung epithelium cells, A549-ACE2, respectively. We conducted cell infections using two distinct initial viral concentrations and quantified viral loads over time. We then fitted the model to our experimental data and quantified the viral parameters. We showed that such cellular tropism generates significant differences in the infection rates and incubation times of SARS-CoV-2, that is, the times to the first release of newly synthesised viral progeny by SARS-CoV-2-infected cells. Specifically, the rate at which A549-ACE2 cells were infected by SARS-CoV-2 was 15 times lower than that in the case of Vero-E6 cell infection and the duration of latent phase of A549-ACE2 cells was 1.6 times longer than that of Vero-E6 cells. On the other hand, we found no statistically significant differences in other viral parameters, such as viral production rate or infected cell death rate. Since in vitro infection assays represent the first stage in the development of antiviral treatments against SARS-CoV-2, discrepancies in the viral parameter values across different cell hosts have to be identified and quantified to better target vaccine and antiviral research.

Dynamical Differences in Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) is a leading viral cause of pediatric respiratory infections and early infant mortality. Despite extensive development efforts currently underway, there remain no vaccines available for the prevention of RSV. RSV is an enveloped, negative-strand RNA virus that utilizes two different proteins (G and F) to mediate attachment and entry into host cells. These G and F proteins are the primary determinants of viral strain-specific differences and elicit protective neutralizing antibodies during natural infection in humans. Earlier studies have demonstrated that these proteins play an additional role in regulating the stability of RSV particles in response to temperature and pH. However, it remains unclear how much variability exists in the stability of RSV strains and what contribution changes in temperature and pH make to the clearance of virus during an active infection. In this study, we evaluated the impacts of changes in temperature and pH on the inactivation of four different chimeric recombinant RSV strains that differ exclusively in G and F protein expression. Using these data, we developed predictive mathematical models to examine the specific contributions and variations in susceptibility that exist between viral strains. Our data provide strain-specific clearance rates and temperature–pH landscapes that shed light on the optimal contributions of temperature and pH to viral clearance. These provide new insight into how much variation exists in the clearance of a major respiratory pathogen and may offer new guidance on optimization of viral strains for development of live-attenuated vaccine preparations.

Modeling and Forecasting Cases of RSV Using Artificial Neural Networks

In this paper, we study and present a mathematical modeling approach based on artificial neural networks to forecast the number of cases of respiratory syncytial virus (RSV). The number of RSV-positive cases in most of the countries around the world present a seasonal-type behavior. We constructed and developed several multilayer perceptron (MLP) models that intend to appropriately forecast the number of cases of RSV, based on previous history. We compared our mathematical modeling approach with a classical statistical technique for the time-series, and we concluded that our results are more accurate. The dataset collected during 2005 to 2010 consisting of 312 weeks belongs to Bogotá D.C., Colombia. The adjusted MLP network that we constructed has a fairly high forecast accuracy. Finally, based on these computations, we recommend training the selected MLP model using 70% of the historical data of RSV-positive cases for training and 20% for validation in order to obtain more accurate results. These results are useful and provide scientific information for health authorities of Colombia to design suitable public health policies related to RSV.

The role of syncytia during viral infections

Respiratory syncytial virus (RSV) is a common, contagious infection of the lungs and the respiratory tract. RSV is characterized by syncytia, which are multinuclear cells created by cells that have fused together. We use a mathematical model to study how different assumptions about the viral production and lifespan of syncytia change the resulting infection time course. We find that the effect of syncytia on viral titer is only apparent when the basic reproduction number for infection via syncytia formation is similar to the reproduction number for cell free viral transmission. When syncytia fusion rate is high, we find the presence of syncytia can lead to slowly growing infections if viral production is suppressed in syncytia. Our model provides insight into how the presence of syncytia can affect the time course of a viral infection.

Initial Inoculum and the Severity of COVID-19: A Mathematical Modeling Study of the Dose-Response of SARS-CoV-2 Infections

SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) causes a variety of responses in those who contract the virus, ranging from asymptomatic infections to acute respiratory failure and death. While there are likely multiple mechanisms triggering severe disease, one potential cause of severe disease is the size of the initial inoculum. For other respiratory diseases, larger initial doses lead to more severe outcomes. We investigate whether there is a similar link for SARS-CoV-2 infections using the combination of an agent-based model (ABM) and a partial differential equation model (PDM). We use the model to examine the viral time course for different sizes of initial inocula, generating dose-response curves for peak viral load, time of viral peak, viral growth rate, infection duration, and area under the viral titer curve. We find that large initial inocula lead to short infections, but with higher viral titer peaks; and that smaller initial inocula lower the viral titer peak, but make the infection last longer.

The Emerging Role of Rhinoviruses in Lower Respiratory Tract Infections in Children - Clinical and Molecular Epidemiological Study From Croatia, 2017-2019

Rhinoviruses (RVs) are increasingly implicated not only in mild upper respiratory tract infections, but also in more severe lower respiratory tract infections; however, little is known about species diversity and viral epidemiology of RVs among the infected children. Therefore, we investigated the rhinovirus (RV) infection prevalence over a 2-year period, compared it with prevalence patterns of other common respiratory viruses, and explored clinical and molecular epidemiology of RV infections among 590 children hospitalized with acute respiratory infection in north-western and central parts of Croatia. For respiratory virus detection, nasopharyngeal and pharyngeal flocked swabs were taken from each patient and subsequently analyzed with multiplex RT-PCR. To determine the RV species in a subset of positive children, 5'UTR in RV-positive samples has been sequenced. Nucleotide sequences of referent RV strains were retrieved by searching the database with Basic Local Alignment Tool, and used to construct alignments and phylogenetic trees using MAFFT multiple sequence alignment tool and the maximum likelihood method, respectively. In our study population RV was the most frequently detected virus, diagnosed in 197 patients (33.4%), of which 60.4% was detected as a monoinfection. Median age of RV-infected children was 2.25 years, and more than half of children infected with RV (55.8%) presented with lower respiratory tract infections. Most RV cases were detected from September to December, and all three species co-circulated during the analyzed period (2017-2019). Sequence analysis based on 5'UTR region yielded 69 distinct strains; the most prevalent was RV-C (47.4%) followed by RV-A (44.7%) and RV-B (7.9%). Most of RV-A sequences formed a distinct phylogenetic group; only strains RI/HR409-18 (along with a reference strain MF978777) clustered with RV-C strains. Strains belonging to the group C were the most diverse (41.6% identity among strains), while group B was the most conserved (71.5% identity among strains). Despite such differences in strain groups (hitherto undescribed in Croatia), clinical presentation of infected children was rather similar. Our results are consistent with newer studies that investigated the etiology of acute respiratory infections, especially those focused on children with lower respiratory tract infections, where RVs should always be considered as potentially serious pathogens.

Mathematical modelling identifies the role of adaptive immunity as a key controller of respiratory syncytial virus in cotton rats

Respiratory syncytial virus (RSV) is a common virus that can have varying effects ranging from mild cold-like symptoms to mortality depending on the age and immune status of the individual. We combined mathematical modelling using ordinary differential equations (ODEs) with measurement of RSV infection kinetics in primary well-differentiated human bronchial epithelial cultures in vitro and in immunocompetent and immunosuppressed cotton rats to glean mechanistic details that underlie RSV infection kinetics in the lung. Quantitative analysis of viral titre kinetics in our mathematical model showed that the elimination of infected cells by the adaptive immune response generates unique RSV titre kinetic features including a faster timescale of viral titre clearance than viral production, and a monotonic decrease in the peak RSV titre with decreasing inoculum dose. Parameter estimation in the ODE model using a nonlinear mixed effects approach revealed a very low rate (average single-cell lifetime > 10 days) of cell lysis by RSV before the adaptive immune response is initiated. Our model predicted negligible changes in the RSV titre kinetics at early times post-infection (less than 5 dpi) but a slower decay in RSV titre in immunosuppressed cotton rats compared to that in non-suppressed cotton rats at later times (greater than 5 dpi) in silico. These predictions were in excellent agreement with the experimental results. Our combined approach quantified the importance of the adaptive immune response in suppressing RSV infection in cotton rats, which could be useful in testing RSV vaccine candidates.