Models to predict H1N1 outbreaks: a literature review

Abstract

With high mortality and panic association, policy- makers face the difficult task of making appropriate and timely decisions to mitigate the serious adverse effects over a short period of time during H1N1 influenza outbreak. Mathematical models predict the number of the cases and thus provide an insight for preparedness during outbreak. The objective was to identify various mathematical models used in prediction of H1N1 outbreaks till date and to compare the usefulness of these models in providing magnitude of H1N1 outbreaks. Hence a secondary data analysis of literature review was done. The literature search was conducted using PubMed and Google scholar, restricting it to articles published until May 2015. The key parameters were set for selection of articles and hence a total of 31 articles has been reviewed. Of each article included in the review, the following data was recorded: year of the study, year of pandemic referring to, country, described mathematical model with results of study The SEIR model is most commonly used mathematical model. SIER model takes all factors in a epidemic phase of an individual i.e. Susceptible – Exposed – Infective – Recovered. While studies which utilised SIER model with the combination of other models found the other factors which influence the occurrence of H1N1 outbreaks and also helped in prediction and prevention of number of cases during outbreak. The SEIR models in combination utilised secondary models such as social networking, global network and total layered containment. The literature review suggests that probable use of mathematical model along with some secondary models will help in better prediction and prevention of number of cases during outbreak.