Modelling the epidemic spread of an H1N1 influenza outbreak in a rural university town

Abstract

Knowledge of mechanisms of infection in vulnerable populations is needed in order to prepare for future outbreaks. Here, using a unique dataset collected during a 2009 outbreak of influenza A(H1N1)pdm09 in a university town, we evaluated mechanisms of infection and identified that an epidemiological model containing partial protection of susceptibles best describes H1N1 dynamics in a rural university environment. We found that the protected group was over 14 times less susceptible to H1N1 infection than unprotected susceptibles. Our estimates show that the basic reproductive rate, R 0, was 5·96 (95% confidence interval 5·83-6·61), and, importantly, R 0 could be decreased to below 1 and similar epidemics could be avoided by increasing the proportion of the initial protected group. Moreover, several weeks into the epidemic, this protected group generated more new infections than the unprotected susceptible group, and thus, such protected groups should be taken into account while studying influenza epidemics in similar settings.

Pandemic 2009 H1N1 influenza in two settings in a small community: the workplace and the university campus

“…The reasons for the similarity observed instead could be a high transmission rate for community-acquired infection, a high transmission rate in individuals who are members of both populations, or the nature of the geographically contained community in which most individuals infrequently travel throughout the time period. This suggests that the self-isolation by infected employees who stayed home when ill may have had little effect on the epidemic spread in the workplace, as would be consistent with the results of modelling studies on the university dataset [4, 5] showing that containment interventions such as quarantine of infected individuals may not play a large role in reducing new infections for rapidly spreading outbreaks in closed communities. While similar results might not be seen in larger populations with extensive immigration, emigration, and mixing, they may be relevant for other outbreaks in smaller, geographically contained communities.…”

Estimating epidemic parameters: Application to H1N1 pandemic data

“…For more detailed introduction, see, e.g., the monograph by [1]. For another study analyzing the WSU dataset, see also [22].…”

Forecasting COVID-19-Associated Hospitalizations under Different Levels of Social Distancing in Lombardy and Emilia-Romagna, Northern Italy: Results from an Extended SEIR Compartmental Model

“…The simplest example of meta-population model is the SEIR (Supscetible-Exposed-Infected-Recovered) model, for the first time developed by Kermack and McKendrik [17]. References for metapopulation approach include [18][19][20]. Some authors have been comparing the effectiveness of agent-based and equation-based models for infectious disease epidemiology.…”