A Bayesian analysis of birth pulse effects on the probability of detecting Ebola virus in fruit bats - Pleydell, David R.J.; Ndong Bass, Innocent; Mba Djondzo, Flaubert Auguste; Meta Djomsi, Dowbiss; Kouanfack, Charles; Peeters, Martine; Cappelle, Julien (Peer Community Journal)

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Since 1976 various species of Ebolavirus have caused a series of zoonotic outbreaks and public health crises in Africa. Bats have long been hypothesised to function as important hosts for ebolavirus maintenance, however the transmission ecology for these viruses remains poorly understood. Several studies have demonstrated rapid seroconversion for ebolavirus antibodies in young bats, yet paradoxically few PCR studies have confirmed the identity of the circulating viral species causing these seroconversions. The current study presents an age-structured epidemiological model that characterises the effects of seasonal birth pulses on ebolavirus transmission within a colony of African straw-coloured fruit bats (Eidolon helvum). Bayesian calibration is performed using previously published serological data collected from Cameroon, and age-structure data from Ghana. The model predicts that annual birth pulses most likely give rise to annual outbreaks, although more complex dynamic patterns – including skip years, multi-annual cycles and chaos – may be possible. Weeks 30 to 31 of each year were estimated to be the most likely period for isolating the circulating virus in Cameroon. The probability that a previous PCR campaign failed to detect Ebola virus, assuming that it was circulating, was estimated to be one in two thousand. This raises questions such as (1) what can we actually learn from ebolavirus serology tests performed without positive controls? (2) are current PCR tests sufficiently sensitive? (3) are swab samples really appropriate for ebolavirus detection? The current results provide important insights for the design of future field studies aiming to detect Ebola viruses from sylvatic hosts, and can contribute to risk assessments concerning the timing of zoonotic outbreaks.