Model name: Yellow fever model
Modellers: Alex Perkins, Sean Moore, Tran Minh Quan and John Huber
Institution: University of Notre Dame

The model represents zoonotic spillover with a Poisson process driven by spatially variable rates accounting for population, coverage, and environmental factors. It represents an urban transmission cycle with a branching process driven by spillover and with SIR dynamics in the event of a large outbreak. The model previously focused on South America, and has recently been extended to focus on Africa too.

Perkins, A., Huber, J. H., Tran, Q. M., Oidtman, R. J., Walters, M. K., Siraj, A. S., Moore, S. M. (2021). Burden is in the eye of the beholder: Sensitivity of yellow fever disease burden estimates to modeling assumptions. MedRxiv

Model name: Yellow fever model
Modellers: Tini Garske, Katy Gaythorpe, Kévin Jean
Institution: Imperial College London

A generalised linear model was fitted to locations of reported yellow fever outbreaks or cases with anthropogenic and environmental covariates generating a risk map for yellow fever across Africa. It was then compared to the available serological data, primarily from central and eastern Africa, to assess the country-specific scale of under-reporting and obtain absolute estimates of transmission intensity in terms of a static force of infection or a basic reproduction number in a dynamical model, taking into account the population-level vaccination coverage through time. Burden and vaccine impact estimates were then generated by combining the transmission intensity with demographic information (population size and age distribution) and observed and hypothetical vaccination coverages in any given location and year.

Geographical distribution of yellow fever occurrence and transmission.

(A) Presence/absence of yellow fever over a 25-year period, by province. White, absence; red, presence of yellow fever reports.

(B) Model predictions giving the estimated probability of at least one yellow fever report.

(C) Estimates of the annual force of infection at the province level in the 32 countries considered endemic for yellow fever.

(D) Estimates of the country-specific detection probability per infection.

Countries not considered endemic for yellow fever are shown in navy (A, B, and D) or white (C).

From Garske, T., Van Kerkhove, M. D., Yactayo, S., Ronveaux, O., Lewis, R. F., Staples, J. E., Comm, Y. F. E. (2014). Yellow Fever in Africa: Estimating the Burden of Disease and Impact of Mass Vaccination from Outbreak and Serological Data. PLoS Med, 11(5): e1001638 Licensed under a Creative Commons CC0 public domain dedication

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