Using experimental human influenza infections to validate a viral dynamic model and the implications for prediction

Mathematical modeling of postcoinfection with influenza A virus and Streptococcus pneumoniae, with implications for pneumonia and COPD-risk assessment

Background

The interaction between influenza and pneumococcus is important for understanding how coinfection may exacerbate pneumonia. Secondary pneumococcal pneumonia associated with influenza infection is more likely to increase respiratory morbidity and mortality. This study aimed to assess exacerbated inflammatory effects posed by secondary pneumococcal pneumonia, given prior influenza infection.

Materials and methods

A well-derived mathematical within-host dynamic model of coinfection with influenza A virus and Streptococcus pneumoniae (SP) integrated with dose–response relationships composed of previously published mouse experimental data and clinical studies was implemented to study potentially exacerbated inflammatory responses in pneumonia based on a probabilistic approach.

Results

We found that TNFα is likely to be the most sensitive biomarker reflecting inflammatory response during coinfection among three explored cytokines. We showed that the worst inflammatory effects would occur at day 7 SP coinfection, with risk probability of 50% (likely) to develop severe inflammatory responses. Our model also showed that the day of secondary SP infection had much more impact on the severity of inflammatory responses in pneumonia compared to the effects caused by initial virus titers and bacteria loads.

Conclusion

People and health care workers should be wary of secondary SP infection on day 7 post-influenza infection for prompt and proper control-measure implementation. Our quantitative risk-assessment framework can provide new insights into improvements in respiratory health especially, predominantly due to chronic obstructive pulmonary disease (COPD).

Modeling Influenza Virus Infection: A Roadmap for Influenza Research

Influenza A virus (IAV) infection represents a global threat causing seasonal outbreaks and pandemics. Additionally, secondary bacterial infections, caused mainly by Streptococcus pneumoniae, are one of the main complications and responsible for the enhanced morbidity and mortality associated with IAV infections. In spite of the significant advances in our knowledge of IAV infections, holistic comprehension of the interplay between IAV and the host immune response (IR) remains largely fragmented. During the last decade, mathematical modeling has been instrumental to explain and quantify IAV dynamics. In this paper, we review not only the state of the art of mathematical models of IAV infection but also the methodologies exploited for parameter estimation. We focus on the adaptive IR control of IAV infection and the possible mechanisms that could promote a secondary bacterial coinfection. To exemplify IAV dynamics and identifiability issues, a mathematical model to explain the interactions between adaptive IR and IAV infection is considered. Furthermore, in this paper we propose a roadmap for future influenza research. The development of a mathematical modeling framework with a secondary bacterial coinfection, immunosenescence, host genetic factors and responsiveness to vaccination will be pivotal to advance IAV infection understanding and treatment optimization.

Environmental role in influenza virus outbreaks

The environmental drivers of influenza outbreaks are largely unknown. Despite more than 50 years of research, there are conflicting lines of evidence on the role of the environment in influenza A virus (IAV) survival, stability, and transmissibility. With the increasing and looming threat of pandemic influenza, it is important to understand these factors for early intervention and long-term control strategies. The factors that dictate the severity and spread of influenza would include the virus, natural and acquired hosts, virus-host interactions, environmental persistence, virus stability and transmissibility, and anthropogenic interventions. Virus persistence in different environments is subject to minor variations in temperature, humidity, pH, salinity, air pollution, and solar radiations. Seasonality of influenza is largely dictated by temperature and humidity, with cool-dry conditions enhancing IAV survival and transmissibility in temperate climates in high latitudes, whereas humid-rainy conditions favor outbreaks in low latitudes, as seen in tropical and subtropical zones. In mid-latitudes, semiannual outbreaks result from alternating cool-dry and humid-rainy conditions. The mechanism of virus survival in the cool-dry or humid-rainy conditions is largely determined by the presence of salts and proteins in the respiratory droplets. Social determinants of heath, including health equity, vaccine acceptance, and age-related illness, may play a role in influenza occurrence and spread.