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Assessing the levels of unreporting in the 2009 A-H1N1 influenza epidemic in Lima, Peru

Mathematical models can provide insights on how future epidemics may behave. Evaluation and implementation of public health strategies can be more accurate when reliable data is used to estimate parameters. However, not all cases are reported, and the levels of uncertainty generated by the gap between the number of reported cases and the actual number of cases has not been studied in detail. We evaluate the impact of non-reported cases in the calculation of the final epidemic size and the effect of different control measures on reducing the attack rate. A system of non-linear ordinary differential equations is constructed to model the spread of influenza. The final size relation for the total number of infected individuals and the proportion of total reported cases are calculated. Both relations are used to generate an expression that helps us quantify the level of non-reporting. To illustrate our results, we consider the case of the 2009 A-H1N1 influenza outbreak in Lima, Peru. Assuming different scenarios of reporting, we estimate key parameters using the data from the initial exponential phase, from which we conclude that no more than 30% of the actual cases were reported. We also perform Monte Carlo simulations to quantify the uncertainty of the control reproductive number to model parameters. Via numerical simulations, we study the impact of different values of the per-capita isolation rates on the final epidemic size. Furthermore, we explore the effects of social distancing varying the time when the intervention policy is applied and different levels of reduction in the transmission rate

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Diego Chowell, Universiteit Utrecht
Baltazar Espinoza, Universidad de Colima
Oscar Patterson, Arizona State University
David Requena, Universidad Nacional Mayor de San Marcos
Michelle Salas, University of Portland
Karen Rios-Soto, University of Puerto Rico at Mayaguez