Lydeamore, Michael J., Campbell, Patricia T., Price, David J., Wu, Yue, Marcato, Adrian J., Cuningham, Will, Carapetis, Jonathan R., Andrews, Ross M., McDonald, Malcolm I., McVernon, Jodie, Tong, Steven Y.C., and McCaw, James M. (2020) Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data. PLoS Computational Biology, 16 (10). e1007838.

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Abstract

Author summary Impetigo (skin sores) is a condition that remains of public health interest. Late sequelae of acute rheumatic fever and rheumatic heart disease, combined with a high prevalence in remote Australian Aboriginal communities, Fiji, and other areas of socio-economic disadvantage, mean that impetigo is a substantial contributor to the burden of disease in these settings. Despite decades of study, key quantities of interest from a transmission dynamics perspective-including the force of infection, infectious period and reproductive ratio-have not yet been determined. Such measures are arguably crucial for making informed decisions on future surveillance activities and intervention strategies. Using a series of computational and statistical methods, we find that the infectious period in remote Australian Aboriginal communities is between 12 and 20 days, and that the force of infection varies by setting. Further, we show sampling every 10 days in future surveys is optimal for further refining these estimates.

Prevalence of impetigo (skin sores) remains high in remote Australian Aboriginal communities, Fiji, and other areas of socio-economic disadvantage. Skin sore infections, driven primarily in these settings by Group AStreptococcus(GAS) contribute substantially to the disease burden in these areas. Despite this, estimates for the force of infection, infectious period and basic reproductive ratio-all necessary for the construction of dynamic transmission models-have not been obtained. By utilising three datasets each containing longitudinal infection information on individuals, we estimate each of these epidemiologically important parameters. With an eye to future study design, we also quantify the optimal sampling intervals for obtaining information about these parameters. We verify the estimation method through a simulation estimation study, and test each dataset to ensure suitability to the estimation method. We find that the force of infection differs by population prevalence, and the infectious period is estimated to be between 12 and 20 days. We also find that optimal sampling interval depends on setting, with an optimal sampling interval between 9 and 11 days in a high prevalence setting, and 21 and 27 days for a lower prevalence setting. These estimates unlock future model-based investigations on the transmission dynamics of skin sores.

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