Using early detection data to estimate the date of emergence of an epidemic outbreak

While the first infection of an emerging disease is often unknown, information on early cases can be used to date it. In the context of the COVID-19 pandemic, previous studies have estimated dates of emergence (e.g., first human SARS-CoV-2 infection, emergence of the Alpha SARS-CoV-2 variant) using mainly genomic data. Another dating attempt used a stochastic population dynamics approach and the date of the first reported case. Here, we extend this approach to use a larger set of early reported cases to estimate the delay from first infection to the Nth case. We first validate our framework by running our model on simulated data. We then apply our model using data on Alpha variant infections in the UK, dating the first Alpha infection at (median) August 21, 2020 (95% interpercentile range across retained simulations (IPR): July 23-September 5, 2020). Next, we apply our model to data on COVID-19 cases with symptom onset before mid-January 2020. We date the first SARS-CoV-2 infection in Wuhan at (median) November 28, 2019 (95% IPR: November 2-December 9, 2019). Our results fall within ranges previously estimated by studies relying on genomic data. Our population dynamics-based modelling framework is generic and flexible, and thus can be applied to estimate the starting time of outbreaks in contexts other than COVID-19.

Seasonality and the persistence of vector-borne pathogens

Many vector-borne diseases are affected by the seasonality of the environment. Yet, seasonality can act on distinct steps of the life cycle of the pathogen and it is often difficult to predict the influence of the periodic fluctuations of the environment on the basic reproduction ratio R0 of vector-borne pathogens. Here, we analyse a general vector-borne disease model and we account for periodic fluctuations of different components of the pathogen's life cycle. We develop a perturbation analysis framework to obtain useful approximations to evaluate the overall consequences of seasonality on the R0 of the pathogen. This analysis reveals when seasonality is expected to increase or to decrease pathogen persistence. We show that seasonality in vector density or in the biting rate of the vector can have opposite effects on persistence and we provide a useful biological explanation for this result based on the covariance between key compartments of the epidemiological model. This framework could be readily extended to explore the influence of seasonality on other components of the life cycle of vector-borne pathogens.