Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability

Epidemiological Transversal
Li Q et al
Mathematical Biosciences

Main result

Based on the hypothesis that COVID-19 may be particularly contagious during peaks of seasonal influenza epidemics and the similarity in their symptoms, the authors sought to assess the number of nosocomial infections that could occur in individuals with symptoms of influenza considered to be possibly infected by COVID-19, either quarantined or hospitalized. They also predicted that in the context of limited testing resources, an increase in the number of individuals with seasonal influenza could be detrimental. Finally, they sought to study the impact of influenza vaccine coverage to prevent the aforementioned scenarios.
A reduction in the rate of increase in daily testing capacity by 80% leads to an increase in the number of confirmed cases by 250% and in the number of cases erroneously quarantined by 370%.
The implementation of social distancing, protective measures, and quarantine appears to be most effective when testing capacity is limited.
The model predicts that an increase in influenza vaccine coverage could significantly reduce the cumulative number of cases of COVID-19 through the mechanisms outlined above. An influenza vaccine coverage rate of 90% would reduce the number of infections due to quarantine by 92.9% and the total number of cases by 23%.


Increasing testing capacity is a crucial point in controlling the epidemic.
The same applies to the implementation of social distancing, containment measures, and quarantine of suspected individuals, which are most effective where testing capacity is limited.
Increasing influenza vaccination coverage can significantly reduce the cumulative number of wrongly suspected cases from COVID-19 and thus the number of people infected when they are quarantined. It would also limit the number of people to be tested because of their influenza symptoms and thus delay the saturation of testing capacity, further reducing the toll of the epidemic.

Strenght of evidence Weak

The epidemiological model is based on a number of assumptions that have not been verified to date. These include the coincidence of periods of high infectivity of COVID-19 and seasonal influenza. Other simplifying assumptions limit these results, including the fact that, in this study, the seasonal influenza vaccine is considered to be 100% effective. Furthermore, while some model parameters are based on data reported by the NHPRC, many are only approximations made by the authors.
Finally, the authors do not offer a sensitivity analysis for the model with regard to their parameter settings, which prevents an assessment of the robustness of the results.


Model the impact of an improved seasonal influenza vaccination policy on the spread of COVID-19 in a context of limited testing capacity.


The model is based on input data provided by the NHPRC (National Health Commission of the People's Republic of China) on the cumulative number of confirmed cases, the cumulative number of deaths, the cumulative number of recovered cases and the cumulative number of suspected cases.
The model is an improved version of the IRRS (classic epidemic spread model) including social distancing and quarantine measures for individuals who exhibit clinical symptoms suggestive of COVID-19 (thus considered as suspected cases) or who have been in contact with suspected cases.

bibliovid.org and its content are bibliovid property.

Legal Notice